SecuNet 4D a comprehensive framework for distributed SDN security and resilience

Abstract

As networks evolve in complexity, distributed Software-Defined Networking (SDN) architectures with multiple controllers are essential for scalability and resilience. In this research, we propose a unified framework, SecuNet-4D Detection, designed for defense, distribution, and dynamic adaptation in distributed SDN systems. To protect data exchange between distributed multiple SDN controllers, an encryption technique is implemented, demonstrating that 98.5% of interception and spoofing attacks are prevented with minimal delay overhead. We devise new uniform mechanisms to eliminate contradictions, thereby reducing conflicts by approximately 87% and achieving policy synchronization within 50 ms at the system level. Additionally, a real-time threat detection system is deployed, achieving 95% detection accuracy and an average response time of less than 1 second for emerging threats. Redundant failover plans ensure continuous network services, reducing downtime by up to 90%, even in the case of controller failures. Furthermore, the proposed framework presents flexible and scalable security solutions capable of accommodating various network sizes and functioning effectively for both small networks (<16 users) and large networks (>2000 users). This research work advances the security and reliability of SDN multi-controller architectures, pushing the boundaries for future network deployments.

Introduction

Software-Defined Networking (SDN) has seen a great deal of interest over the last few years as it brings programmability, flexibility, and scale to network architectures. SDN separates the control plane and data plane, thereby providing centralized network flow control for dynamic adjustment to meet various flows^1,2. But once networks grow larger, the inadequacy of running with a single SDN controller becomes obvious and demands multi-controller architecture. Distributed SDN controllers address the issues related to scalability and fault tolerance but also bring a new set of challenges on how security can be increased with no single point of failure (SPoF), policy consistency across controller partitions/interfaces changes with devices failing or getting repaired/saturated over time, and resilience. A major issue with distributed SDNs is the vulnerability in inter-controller communication³. Traditional security typically does not provide security against interception, man-in-the-middle attacks, and controller impersonation, which can put the reliability or integrity of a network under threat. On top of that, enforcing policies seamlessly throughout a number of controllers is difficult, just waiting to happen. Unaddressed policy inconsistencies can result in network misconfigurations and security exposures, along with potential performance issues^4,5. Further, in this environment of distributed SDN, we are unable to cope with the growing complexity and modus operandi of cyber threats using conventional threat detection methods. These gaps are open doors that can be taken advantage of when there is no adaptive method in place for the recognition and eradication of threats based on real time^6,7,8,9. Like in the case of controller failures, we need reliable fail-over mechanisms that can take over workloads and reduce service outages. Finally, as networks of scale experience, we introduced more complex than ever automated security solutions because if a network is dynamically being changed, so should automatically our security mechanisms^10,11,12,13.

Previous studies on the security of multi-controller SDN architectures have mainly catered to the areas of controller placement⁵, fault tolerance techniques⁴, and distributed security framework⁸ These approaches usually provide insufficient unified security framework, slow policy synchronization, poor inter-controller security and slow detection of threats. For example, the Controller Placement approach⁵ enhances SDN resiliency but does it at the expense of having any encryption mechanisms, thus opening itself to man-in-the-middle (MITM) attacks. A similar example would be fault tolerance mechanisms⁴ guarantee the reliability of a fault-tolerant network but do not take account of network security threats as they occur, rendering SDNs vulnerable to policy inconsistencies. Furthermore, although multi-controller management frameworks¹⁰ fostered enhanced network stability, they don’t provide an adaptive security mechanism for discovering and mitigating novel cyber threats. Thus our work combines these aspects to achieve security, availability of services, and policy uniformity in the relayed on a scalable and capable architecture for the future SDN.

In this paper, we propose new solutions overcoming those challenges to work well for distributed SDN multi-controller paradigms called Secunet-4D (Detection, Defense, Decision, and Dynamic Adaption). The controllers now cover against interception and tampering data exchange between the two with Controller Communication Encryption (CCE), a new encryption mechanism, SecuNet-4D, is used for this communication. Second, we introduce the SecuNet-4D policy enforcement strategy that coordinates the enforcement of name constraints at all controllers to reduce conflicts and misconfigurations. The resilience in multi-controller SDN environments is the ability of such environments to withstand failures of controllers while ensuring security, adaptability, and operational efficiency under various network conditions and cyber threats. Conventional SDN architectures do not provide sufficient means to tolerate controller failures and adapt to changing attacks. Proposed Framework to SecuNet-4D: A self-healing failover, multi-layer anomaly detection and most importantly dynamic policy adjustment to make the resilience higher. These elements cooperate for secure, flexible, and failure-resilient SDN functionality. In order to focus on the security threats in real-time, we propose a SecuNet-4D threat detection system that uses powerful machine learning techniques to detect and prevent new attacks proactively. We also introduce the SecuNet-4D failover mechanism to handle controller failures while balancing load and maintaining good network performance. Finally, we present a list of security-related solutions for defending SDNs through scalable protections targeting the scale and complexity characteristics.

Fig. 1

Proposed architecture.

Figure 1 shows the evaluation result of these approaches on a large-scale SDN testbed constructed in our experiment. Dynamic Policy Synchronization (DPS) extends this logic to distributed controllers with universal policy enforcement while also providing centralized installation control of CCE encryption features. Advanced Threat ATD has very high accuracy for detecting anomalous events, whereas CRFM delivers automatic failover to provide robust control between failures. The solutions we propose are scalable in the sense they can work with networks of varying complexity and therefore fit a wide range of SDN deployments. Our work advances research in securing multi-controller SDN architectures, which discusses some key vulnerabilities and suggests practical solutions for their real-world deployment.

The growing tendency to use SDN multi-controller architectures has led to new security threats, which are undermining the integrity and dependability of network control. Specifically, Inter-Controller Communication, Controller to Node Interaction, and Distributed Decision Making are vulnerable to attacks such as man-in-the-middle attacks, controller impersonation attacks, and policy violations. Traditional methods of enforcement and monitoring provided by Access Control Lists (ACLs) are unable to scale, adapt in real time, or be easily applied to provide the necessary security for SDN multi-controller architectures^14,15,16. The key contributions of this study are as follows:

• We propose SecuNet-4D, a security framework designed to enhance resilience in multi-controller SDN environments by ensuring continuous security adaptation and failover management.

• We develop a controller failover mechanism that facilitates smooth transition and redistribution of network responsibilities in the event of controller failures, minimizing network disruptions.

• The framework incorporates a multi-layer anomaly detection system, integrating stacked detectors to dynamically identify and mitigate cyber threats with minimal false positives.

• SecuNet-4D implements real-time security policy adjustments, enabling autonomous adaptation to zero-day attacks without requiring manual intervention.

• We conduct a comprehensive empirical analysis, assessing the framework’s effectiveness in handling security threats, failover scenarios, and real-time policy updates to validate its resilience-enhancing capabilities.

The remainder of this paper is organized in the following sections. “Related work” illustrates the related works, highlighting the disadvantages of existing SDN security mechanisms. The presentation of the proposed methodology with detailing its encryption, policy enforcement, anomaly detection, and fail-over mechanisms is given in “Proposed methodology”. Whereas “Results and discussion” provides the results and discussion with performance evaluation and metrics used. Finally, “Conclusion” concludes research work key findings.

Related work

Organizations are turning toward Multi-controller SDN architectures to gain scale and redundancy, but this presents a number of issues when it comes to security. As these systems are deployed in a distributed manner, they become susceptible to insecure inter-controller communication, policy mismatches, and sophisticated attacks^17,17,18,19. We survey recent activity in the area, discussing methodologies and limitations as well as introduce a new security framework that we have developed in response. While they hold the promise of better load balancing and fault tolerance, multi-controller SDN architectures also introduce new security concerns. Standard security methods that are still protective in this scenario require more comprehensive approaches, such as involving iterative devices and analyzing the data in real-time to ensure full safety through distributed controller design^20,21. Addressing cyber threats has become extremely challenging due to the lack of adaptability of traditional threat detection techniques like signature-based and rule-based intrusion detection systems (IDS) to the ever-increasing complexity of these threats^15,22,23. These methods are based on preconfigured attack patterns or static regulations, which can be ineffective for pristine-day attacks, polymorphic malware, and oppositional evasion methods. On the negative side, rules-based techniques need continuous manual updates, pushing high false negative rates against always novel threats. Although previous work has investigated machine learning-based anomaly detection, current models notoriously have high computational overhead and poor scalability in multi-controller SDN settings. In dealing with these challenges, SecuNet-4D leverages Multi-Layer Anomaly Detection (MLAD) to achieve dynamic yet accurate anomaly classification with low latency by combining statistical with deep learning-based detection.

Various works^{5,7,15,24,25,26} have focused on SDN security, however the majority of them are targeting anomaly detection and/or policy enforcement. Yet, resilience–the ability to sustain network security and operational continuity despite unwelcome, adverse conditions–has not been fully considered in previous research. In particular, with a few exceptions, existing solutions lack of real-time adaptive mechanisms for policy updates and failover handling, leaving them vulnerable to controller failures and emerging threats. To address this gap, the SecuNet-4D framework is proposed by integrating failover mechanisms, adaptive security policies, and real-time threat detection in a multi-layer resilience approach.

The main difference of our work compared to the existing documents is that we present a comprehensive security framework that takes an adaptive approach and deals with multi-controller SDN architectures, as shown in Table 1. While authors in²⁷ concentrate only on optimization problems related to controller placement and traffic engineering rather than a broader network security strategy or the ability to adapt to changing network conditions. Work presented in^1,25 specifically tackles the issues of multi-controller management and security; however, they do not scale well in larger networks or provide proactive threat detection. Although in⁸, dynamic security and fault-tolerance approaches are considered, these have high computational overheads whereby predictive feedback or adaptive solutions to prevent attacks cannot be formulated. Our novel framework, incorporating Context-Aware Adaptive Encryption (CAAE), Dynamic Policy Adjustment (DPA), and Multi-Layer Anomaly Detection (MLAD), further improves security while reducing latency, increasing robustness, and providing preventive threat intelligence through sophisticated machine-learning models. This full lifecycle view makes our approach the most optimal to protect large dynamic multi-controller SDN deployments. In contrast to existing works, our approach overcomes these limitations by combining context-aware encryption, arbitrated device-based policy management, multi-layer anomaly detection, and automatic self-healing mechanisms. It tackles the imperative service of creating performance- and availability-resistant security solutions for multi-controller SDN environments that are both proactive and can adapt quickly to human-specified changes. Taking advantage of state-of-the-art machine learning models, context-aware encryption, and dynamic policy shifting provides comprehensive security that matches up to other conventional options. Meanwhile, effective management of real-time threats with minimal overhead and quicker recovery time helps raise the bar for a secure and reliable SDN architecture^{24,26,28,29,30}.

Table 1 Comparison of proposed framework with existing works.

Proposed methodology

Our proposed multi-controller SDN security framework aims to handle these problems. The central idea behind the framework is that we use advanced encryption methods to provide a high degree of security to inter-controller communication. That not only ensures both data integrity and confidentiality but also means this kind of system standard can have a much longer life cycle expectancy. A strong Policy management system utilizes real-time traffic analysis for adjusting security policies across multiple controllers on the fly. This approach avoids conflicts and assures consistency. Use of machine learning algorithms for dynamic threat detection in historical and current network traffic allows us to sense when an anomaly occurs thereby enabling countermeasures. Operational stability is achieved through autonomous fault recovery mechanisms. If one of the controllers goes down, the system redistributes workloads to remaining functional controllers immediately without lost connections and interruptions by utilizing a fast set of utilization.One of the biggest challenges in this situation is consistent policies across multiple security controllers. The framework approaches this problem by also using distributed consensus algorithms. That then reinforces the security of multi-controller SDN environments without onerous maintenance costs and offers adaptability to what is unique about each type of network environment. At the same time it will strengthen protections against attacks by as yet unknown threats, as well as ensuring that end-to-end network operations management systems work better than ever before. The methodology for SDN Multi-Controller architecture is shown in Fig. 2.Whereas the resilience of the SecuNet-4D framework is built upon three high-level mechanisms: Failover Mechanisms for Self-Healing, where in the event of controller failures, the system guarantees a smooth transition and redistribution of network functions to avoid outages in networks. Anomaly Detection in Stacked Layers, where a multi-layer approach with a distributed cascading anomaly detector adopts this method with close to zero false drop. Dynamic Policy Adjustment, where security policies are dynamically adjusted based on the real-time state of the network, enabling an organization to respond to zero-day attacks even when the latter are not recovered by the virus signatures, without requiring manual intervention. SecuNet-4D architecture can enhance the resiliency of SDN by ensuring higher availability, security, and adaptability using these components.

Fig. 2

Methodology for proposed framework.

The SecuNet-4D encryption mechanism

In this research paper, the Context-Aware Adaptive Encryption (CAAE) is introduced, which describes that the level of encryption can be adapted according to the complexity of the data and the network load. It handles the information sensitivity of current network traffic. Using these metrics, the algorithm determines either high-level, medium-level, or low-level encryption to be applied to the network traffic data, which keeps the data secure while reducing the encryption overhead required for non-sensitive information. Steps of the CAAE algorithm are as follows:

• Sensitivity Accessibility: The sensitivity of the network data traffic is determined by network load.

• Scale of Padding: Sensitive data have a strong level of encryption to keep it secure while the non-sensitive data implementations used lighter encryption algorithms to avoid unwanted lots of processing cycles in the execution of those algorithms.

• Calculation of Overall Network Load: The network traffic load can be calculated using the below formula:

  $$\begin{aligned} \text {Current Network Load} = \frac{\text {Current Network Traffic}}{\text {Maximum Capacity of the Network}} \end{aligned}$$

  (1)

On the basis of overall network performance evaluation, it selects the encryption level; for example, with high network throughput, a heavy encryption algorithm would be applied only on sensitive data to be transferred, while a light encryption algorithm would handle for other data. The determination of the encryption level is on the basis of overall network traffic load and at the same time network data sensitivity in order to optimize the overall network security and its performance. The thresholds values represents the normalized network traffic load . The values (0.5 and 0.8) in our experimentation were chosen on the basis of empirical analysis. Though in varied network conditions this ensures the minimal encryption overhead and high security. By contrast, in low network load conditions, more robust encryption may be incorporated on all data types, improving overall security. By adjusting resource use in this way, security of the network is strong without adding an unnecessary burden to the network.

Algorithm 1

Context-aware adaptive encryption (CAAE).

The CAAE ensures maximum security and efficiency in inter-controller communications through adaptive encryption. To decide between these two encryption strengths and levels based on data sensitivity and network load. A higher level of encryption is applied for enhancing the security if a data packet has highly sensitive information, or if a network is under heavy traffic. On the other hand, when the network conditions are good, a less complex encryption scheme is used to boost performance and security. In Algorithm 1 we detail the proposed encryption selection mechanism.

The SecuNet-4D policy enforcement strategy

The Dynamic Policy Adjustment (DPA) algorithm aims to reactively optimize policy decisions for SDN while using new and past database conflict information. Specifically, it constantly observes real-time traffic to identify behavioral signatures which may signal potentially problematic policy enforcement. It uses a ML model like Random Forest to predict potential conflicts according to real-time and historical data of the traffic, which enables us to solve those conflicts in advance before they actually happen. In case of a potential conflict, the algorithm dynamically refines the current policy set by adding and/or updating policies associated with the last stable state. The adjustments are performed early on, guaranteeing that there is the least disturbance to the network and that the distributed controllers keep running at an optimal level. The updated policies are then distributed to all of the controllers in the network to synchronize and make sure that the nodes are consistent with each other.

The DPA algorithm also includes the filtering based on core traffic parameters: source IP and destination IP address; protocol type and ports; service type; and network path; Furthermore, it provides a real-time conflict detection mechanism to capture inconsistencies that might not be observed on past historical data but occurs after the deploying of a policy. This algorithm is effective in dynamically updating access control, QoS adaptation, and adaptive encryption policies to guarantee high-level security and network security performance as shown in Algorithm 2.

Algorithm 2

Dynamic policy adjustment (DPA).

This contextual approach improves network trustability and accelerates execution since anything that draft service into a conflict or security situation are likely correlated and presents redundant queries that serve no better purpose than to increase the risks that a service will deteriorate. Basically, a data plane abstraction re-affirms when the system encounters the certain discrepancies in the operating conditions of the network, it can reconfigure itself, creating an efficient SDN. The DPA algorithm has the following steps:

• Continuous Network Traffic Monitor: Network traffic monitoring is performed in order to detect the defined patterns that may be responsible for possible conflicts in the policies.

• Forecasting of the Conflicts: By making use of an ML algorithm (Random Forest) to predict possible conflicts in varied policies. This forecasting is performed on the basis of past and real-time historical network traffic.

• Reorientation of the Policies: When a conflict is predicted, the algorithm reformulates the policies by adding new policies if needed or modifying previous policies to eliminate the conflict.

• Applicability of the Policies: Distribution of policies through all controllers and synchronization across the network.

The SecuNet-4D detection for threats

MLAD is a machine-learning (ML)-based adaptive statistical approach for security in dynamic SDN-driven environments. Then an MLAD, which is designed to improve the accuracy of anomaly detection while reducing false positives, through hierarchical analysis and voting mechanism. As these networks become more complex in functionality, it is vitally important for control enforcement and the automation thereof. Leveraging the MLAD algorithm, organizations can respond in near real-time to emergent threats and even slow the progress of the attackers and thereby create a more secure and sound position on the network. The following are the steps performed by MLAD algorithm:

• Network Traffic View: In order to identify multiple security risks, network data traffic is collected and keeping the track (NA). This consistency in streaming of network traffic data makes it possible to detect numerous anomalies with high detection accuracy.

• Analysis in Layers:

  • Layer first: Using a statistical approach in order to detect the numerous anomalies whenever the traffic data (NA) is exceeding a predefined network traffic threshold value(TL).

  • Layer second: Applicability of the ML model (deep learning) for better detection of malicious traffic patterns in the input traffic data.

• Accumulation of the Results: By making use of the voting mechanism for the combined results from both the layers (layer 1 and 2). If any of the layer flags an anomaly ($FA \ge 1$), the input data traffic is marked as malicious.

• Adaption in Learning: The thresholds ($TL'$) are dynamically updated on the basis of the number of anomalies detected; this is also enchaning the data sensitivity over the period of time.

It starts capturing the input network traffic data (NA) and monitoring them continuously for real-time analysis. The anomaly detection procedure is performed on two levels. Statistical techniques in the first layer, where traffic is compared against a threshold (TL) to flag anomalies. So if today’s traffic exceeds this threshold, we mark it as anomalous. The 2nd layer uses a ML model, denoted $f_{\text {DL}}$ to discover deeper anomalies by recognizing even more complex patterns in data. Finally, the predictions from both layers are aggregated using a voting mechanism. Data is considered anomalous if one or both layers detect anomalies. In order to improve adaptability, this algorithm dynamically adjusts the threshold of the anomaly detection model according to the number of identified anomalies. where TL is current time spent without anomalies, $TL'$ is updated time needed for the next operation, ks is adaptive learning rate, if no anomaly found time needed to process the next operation is increased, which is given by the formula. So, The system rates its sensitivity iteratively over time, which is customized to the changing behavior of the network. The MLAD algorithm outperforms traditional approaches by offering a more robust and scalable solution for detecting anomalies in a network traffic environment by combining statistical and ML-based techniques with adaptive threshold.

Algorithm 3

Multi-layer anomaly detection (MLAD).

The SecuNet-4D failover mechanism

SHCF uses an algorithm to monitor the health status of all SDN controllers which can create fault tolerance to SDN controllers. The automatic recovery mechanism comes into play whenever it is required. It ensures network performance health based on reinforcement learning, failure detection, and load redistribution. It offers strong and stable governance solution to rule over networks in distributed settings. To make a multi-controller SDN environment more robust, SHCF does an efficient task of handling controller failures. The algorithm first assesses all the controllers across the time domain and determines failure anomalies based on performance metrics. These metrics are studied to calculate a failure threshold which is described as follows

$$\begin{aligned} F_{\text {threshold}} = \text {mean}(C) + 3 \cdot \text {std}(C), \end{aligned}$$

(12)

where $\text {mean}(C)$ and $\text {std}(C)$ are mean and standard deviation of controller metrics respectively. Health below this threshold will cause a controller to fail. If a failure is detected, the algorithm distributes the workload of the failed controller across the operational controllers to balance the load. The repackaged workload is evaluated as

$$\begin{aligned} L' = L - L_{\text {failed}}, \end{aligned}$$

(13)

where $L$ is the total workload and $L_{\text {failed}}$ is the load of the failed controller. To accomplish this, the algorithm uses reinforcement learning to adapt the way it redistributes the load based on past failure conditions. The redistribution strategy is updated using the equation

$$\begin{aligned} R_{\text {new}} = R_{\text {old}} + \alpha \cdot (L' - L_{\text {target}}), \end{aligned}$$

(14)

where $\alpha$ is the learning rate, $R_{\text {old}}$ is the previously obtained redistribution policy, and $L_{\text {target}}$ is the desired load distribution. With adaptive processing, this approach can efficiently process controller workloads while avoiding a full service disruption. The proposed SHCF algorithm adaptively with its operational controllers redistributes traffic to preserve the global network performance and robustness under the circumstance of controller failures.

Algorithm 4

Self-healing controller failover (SHCF).

Results and discussion

The proposed multi-controller SDN security framework works in a series of subsequent processes, which enhance network safety, reliability, and performance as long as the external and internal inputs are considered. The different SDN controllers maintain real-time monitoring of network traffic and, tracking important parameters such as throughput, latency, and packet loss for performance evaluation and anomaly identification. This form of real-time monitoring is fundamental to dynamic policy management the ability to alter policies in accordance with current network requirements and the identification of potential clashes. Specifically, the framework adopts a two-tier adaptive threat detection system that combines statistical anomaly detection and machine learning models to enhance the accuracy in identifying security risks. Communication between controllers is secured with context-aware encryption that dynamically changes based on data sensitivity and network load. Thus, ensuring the best security with no performance loss. The healing failover mechanism rebalances the workloads between the healthy controllers so that even in the event of a controller failing or going offline, the network continues to operate with minimal disruption. In addition, the framework makes use of distributed consensus algorithms to ensure that all the controllers are following the same security policies, even if some of them fail. The proposed methodology is holistic in nature and considers SDN topologies with multiple controllers in its design, leading to a scalable system offering enhanced network protection and control.

We consider the following experimental set up as shown in Table 2 where the network is simulated using a Fat-Tree topology ($k=4$) as illustrated in Fig. 3 with multi-path routing in mind such that the individual attached links between switch and controller are 1 Gbps and control delay for inter-controller communication is 2 ms. Mininet emulation tool was used to setup the experimental testbed. This allowed us to emulate a customize fat-tree topology with 3 SDN controllers. With the help of Mininet the creation of virtual network nodes, various links, and multiple traffic flows for real-time network performance evaluation is carried out. The three SDN controllers (OpenDaylight (ODL), Ryu, and ONOS) are deployed on dedicated servers with Intel Xeon E5-2620 v4 CPU, 16 GB RAM, 500 GB SSD. Using iPerf and Scapy, we generate network traffic, where normal network traffic and attack traffic (DDoS, flow rule exhaustion) are simulated. Evaluation metrics include policy enforcement latency, anomaly detection accuracy, failover time, and network throughput. These setups provide a realistic and scalable SDN deployment on which the proposed SecuNet-4D framework can be evaluated.

Table 2 Simulation setup and parameters.

Fig. 3

Fat-tree topology used in the experiments.

Policy enforcement latency

Figure 4 illustrates the latencies in enforcing security policy in performing the proposed method over traditional security policies. The latency numbers are between 33 and 37 milliseconds using the mega-tabular method, as opposed to the new approach, which averages between 18 and 22 milliseconds. Thus, SDN controllers have reduced the complexity of policy distribution and policy update time utilizing efficient synchronization mechanisms and secure communication channels to speed up the policy dissemination across distributed SDN controllers. In this new paradigm of real-time security, rapid decision-making and policy enforcement ensure that security is always top of mind, and attackers have limited opportunities to exploit potential vulnerabilities.

Latency calculations

By making use of Eq. 18, the average network latency is calculated, in which summation of all different latency measurements and then division of the results is achieved by the maximum number of observations at that period of time (s):

$$\begin{aligned} \text {Mean Network Latency} = \frac{1}{s} \sum _{i=1}^s \text {Latency}_i \end{aligned}$$

(18)

Equation (19) calculates the standard deviation of latency values, which indicates the variability in response times:

$$\begin{aligned} \text {SD Network Latency} = \sqrt{\frac{1}{s-1} \sum _{s=1}^s \left( \text {Latency}_i - \text {Mean Network Latency}\right) ^2} \end{aligned}$$

(19)

Performance analysis

As illustrated in Fig. 4 dynamic threat modeling exhibits much lower latency than static policy. Dynamic models have the potential to update and enforce new policies at the same time ensuring that networks receive updates as quickly as possible and improving responsiveness. This highlights the ability of dynamic models to quickly adapt and enforce new policies, minimizing delays and enhancing network responsiveness.

Fig. 4

Higher latency in traditional static policy and less latency in the SecuNet-4D policy enforcement strategy.

The static policy is optimized in the latency range. This is because the static configurations (which cannot change instantaneously based on conditions of the network or threat) are intrinsically limited. Enforcement is often a long, manual process, as changing policies takes time, which prevents a timely response to emerging threats. While the measured latency is much lower for dynamic policies, it is a sign of a more effective response mechanism. Dynamic threat modeling is more capable of real-time assessment and live policy enforcement of real-world threats and vulnerabilities. The capability avoids any possible delays, thus improving network responsiveness and efficiency in operations. This dynamic threat model not only reduces latency in response times but compares favorably to static modeling with respect to the agility of responses on the network. This flexibility is important in a setting where threats are constantly evolving, requiring rapid policy updates to reduce risk effectively. Dynamic policies reduce latency, resulting in better user experiences, as network services are not interrupted or dramatically slowed down by security policies employing outdated technologies. As illustrated in Fig.  5 latency comparison during a ten-second period between static and dynamic policies is illustrated in Table  3. Adaptation Speed consistency in latency Reduction in latency with time results in Fig. 5 indicates that the static latency holds between 100 ms and 150 ms; the details indicates a slower response time due to the rigidity of the static settings. On the contrary, dynamic latency ranges from 60 ms to 100 ms, illustrating the responsiveness of dynamic policies to adapt to altering network conditions more promptly. This disparity highlights the benefits of dynamic threat modelling: updating and enforcing security measures at a database table or application entry point can make your network a more responsive and defensive safety zone. But also the packet loss percentages with respect to static and dynamic policies. Static packet loss has values from 5% to 15% quantifying that stiff setups easily lose packets during peak times. In the opposite direction, dynamic packet loss is much consistently lower from 1% to 5%, indicating that dynamic policies could have done a good job with traffic fluctuation being seen as the reason of packet loss. At a high level, this result points to the benefits of using dynamic models to increase the overall dependability of communication over a network,particularly in situations of great demand. The third plot shows CPU usage for static and dynamic policies. Static processor usage is 50% to 80%, indicating more resource consumption because it constantly requires manual tuning. In contrast with dynamic CPU usage from 40% to 60% showing its ability to adapt to the demands of traffic more efficiently. The fourth graph is of response time; we see a difference between the static (30-50 seconds average) and dynamic (10-20 seconds average) responses. This indicates that dynamic threat modelling provides low latency but a higher threat modelling speed, therefore creating an agile and secure network environment. Taken together, these graphs demonstrate that dynamic threat modelling consistently outperforms in a number of significant metrics, reaffirming its validity as a more viable approach to supporting network security in more complex environments.

Table 3 Performance analysis of policy enforcement.

Fig. 5

Performance analysis of dynamic threat modeling.

The total of all graphs presented in Fig. 5 illustrates how different network management systems will perform. Combined sets are thus: delay, throughput, packet loss and security events react time. The latency graph shows that dynamic threat modelling can significantly lower latencies typically 60ms to 100 ms, whereas in contrast with these static policies see rather late communications, or perhaps communication that is slow even on the local level. It means a swifter response to network changes and threats. The throughput graph reflects how dynamic policies can see to it that the network throughout all when weaker static policies are implemented in the network. In contrast, the static policies suffer reduced performance of up to 6%. Coordinately, the loss rate graph shows that dynamic configurations result in improving the data integrity and the actual reliability of transmissions. Lastly, the graph shows how faster security incidents occur using dynamic threat modelling. These graphs underline the advantages used in proposed dynamic policies for network management and push their importance for their role in efficiency, reliability, and security strategies in a modern dynamic environment.

Network throughput over time

Figure  6 Network throughput for the period over time interval plotted static and dynamic source threat policies. Throughputs, measured in Mbps, were represented on an X-axis that stood for time right up to one billion microseconds and on a Y-axis depicting throughput levels. This information is critical in that it brings home just how effective different methods of managing network traffic that it provides good strategies for managing a network operation during varying periods or changing conditions.

Technical analysis

• Traditional static policy throughput: According to the static policy throughput, throughput reaches 1000 Mbps and sometimes 2000 Mbps. This shows that it is not very responsive to changes in demand for network resources. On static policies, however, available bandwidth may go unused. As a result, during peak usage times there is no choice but for a bottleneck to occur to cope up.

• Dynamic policy throughput is different from static policy: It ranges in speed from 1500 Mbps to 2500 Mbps. This means that network resources are better utilized than could be achieved by setting a rigid priority of usage. While it makes allowances for rapid changes in policy based on current network performance, flexibility provided by dynamic policies also enhances overall throughput and, at the same time, minimizes deterioration. This is particularly advantageous in high-demand situations, where timely allocation of resources is crucial to meeting performance objectives.

Mean network traffic throughput calculation

The calculation of the mean network traffic throughput in the network over a period of time is achieved through the following equation:

$$\begin{aligned} \text {Mean Network Traffic Throughput} = \frac{1}{n} \sum _{i=1}^n \text {Throughput}_i \end{aligned}$$

(20)

where: n is the sum of all throughput measurements, $\text {Throughput}_i$ represents the throughput value at the ith measurement.

Standard deviation of throughput

The variability of throughput measurements, indicating how much individual throughput values deviate from the mean, is calculated as:

$$\begin{aligned} \text {SD Throughput} = \sqrt{\frac{1}{n-1} \sum _{i=1}^n \left( \text {Throughput}_i - \text {Mean Throughput} \right) ^2} \end{aligned}$$

(21)

where: n is the total number of measurements, $\text {Throughput}_i$ represents the throughput value at the ith measurement. The Mean Throughput is the average throughput calculated using Equation 20, while Equation 21 provides insights into the consistency of throughput values over time.

Network throughput analysis

Fig. 6

Performance analysis for network throughput.

From Fig. 6, it is indicated that the CPU usage under static policies fluctuates within a narrower range (40% to 60%), suggesting that static configurations may be less responsive to changes in workload. In contrast, the dynamic policy CPU usage varies between 30 and 50%, indicating a more adaptive approach that efficiently allocates CPU resources according to current network demands.

Fig. 7

Performance analysis for CPU usage and memory utilization.

From Fig. 7 it is shown that the CPU usage under static policies fluctuates within a narrower range (40% to 60%), suggesting that static configurations may be less responsive to changes in workload. In contrast, the dynamic policy CPU usage varies between 30% and 50%, indicating a more adaptive approach that efficiently allocates CPU resources according to current network demands. The overlapping area shown in the figure clearly illustrates the extensive performance differences of both the approaches. The traditional static policies might show a consistency in the usage but on the other hand the proposed dynamic policies are able to reduce the overall CPU load during off-peak hours, and hence it contributes to increasing the overall efficiency. Table 4 provides an overview of the network throughput statistics, under the case of static and dynamic threat policies, over a period of time. The included metrics cover mean, standard deviation, max value, min value, median value, and IQR of throughput, making a complete comparison between these two methods. The dynamic policy achieves a higher mean throughout time and becomes more stable (a lower standard deviation and values in a tighter range). Furthermore, the peak use periods demonstrates that dynamic policy adapts better to network demand than static policies. This all suggests that dynamic policies accommodate traffic more efficiently than static configurations. Additionally, the dynamic policies achieve much higher levels throughout than that of static policy. This indicates their effectiveness at high levels of traffic changes. Similarly, a tighter spread of throughput values with dynamic policies is also evident from the IQR. In contrast, because static policies have a wider IQR, they tend to oscillate more widely in between periods of high reward, causing unnecessary fluctuations and potential waste during a high performance period. These results are confirmed by the median values: more discriminations will only emphasise how much better dynamic installations are and how much better they are at good levels of throughput in overall network scenario.

Table 4 Overview performance analysis of throughput.

Cryptographic security analysis

To validate the security of the proposed Context-Aware Adaptive Encryption (CAAE) mechanism, we conduct a rigorous cryptographic security analysis, focusing on confidentiality, resistance to cryptographic attacks, computational trade-offs, and formal security proofs.

Confidentiality and key strength analysis

The effectiveness of encryption is fundamentally dependent on key strength. The CAAE mechanism uses AES-256 in high-security scenarios, ensuring a key space of $2^{256}$, which is computationally infeasible to brute-force. The key entropy (H) is given by:

$$\begin{aligned} {H = k \cdot \log _2 N} \end{aligned}$$

(22)

where k is the key length (in bits) and N is the number of possible key values. For AES-256, this results in a complexity of $2^{256}$, making brute-force attacks impractical.

Resistance against cryptographic attacks

The proposed encryption mechanism is analyzed under the following cryptographic attack models:

• Ciphertext-Only Attacks (COA): Since CAAE dynamically varies the level of encryption based on the traffic sensitivity, attackers are unable to create repeatable sentences of ciphertext.

• Chosen-Plaintext Attacks (CPA): The requirement of randomized. Initialization Vectors (IVs) along with key rotations, makes the creation of plaintext-ciphertext pairs prohibitively expensive and prevents attackers from exploiting them.

• CCA: In addition to the existing incorporation of key diversification strategies and message authentication mechanisms, CAAE ensures that modified ciphertexts do not leak useful information.

• Traffic Analysis Attacks: This harmonic approach of adaptive encrypting mitigates statistical inference by implementing adaptive encryption such that reverse engineering of such traffic cannot expose quantifiable patterns in the traffic flow.

Tradeoff between computational overhead and security

A major challenge when it comes to encryption-based security is the trade-off between computational efficiency and cryptographic strength. Encryption time complexity ($T_E$) is as follows:

$$\begin{aligned} T_E = O(k \cdot n) \end{aligned}$$

(23)

where k is the key length and n==input data size. Experimental results verify that adaptive encryption only adds computational overhead adaptively depending on network conditions.

Formal security proof

We formalize semantic security of CAAE in an CPA (In distinguish-ability under Chosen Plain-text Attack) security model to prove that CAAE provides semantic security.

Definition (semantically secure CPA)

An encryption scheme (Gen, Enc, Dec) is CPA secure if for any probabilistic polynomial-time adversary $\mathcal {A}$,

$$\begin{aligned} \left| Pr[\mathcal {A}(Enc(k, m_0)) = 1] - Pr[\mathcal {A}(Enc(k, m_1)) = 1] \right| \le \epsilon \end{aligned}$$

(24)

where $m_0$ and $m_1$ are two chosen plain-texts and $\epsilon$ is a negligible probability.

Theorem

Assume the underlying encryption function used in CAAE, e.g., AES-GCM, is CPA secure, thus CAAE is CPA secure.

In the proofs that follow, we plan to As AES-GCM is IND-CPA secure under common cryptographic assumptions, the attack vector, if any, must have been key reuse or pattern predictability. To prevent this, CAAE is designed to change its encryption keys dynamically, making the process highly sensitive to both data sensitivity and network load. Thus an adversary cannot distinguishes encryptions of distinct plain-texts except with negligible probability, which proves CAAE’s semantic security.

Summary of security properties

The proposed encryption mechanism satisfies the following security properties:

• Confidentiality: Provides confidentiality via strong crypto encoding of the data.

• Integrity: Disallows unauthorized modifications with authenticated encryption (AES-GCM etc.).

• Adaptive cipher and security: Adapts the cryptographic force according to the requirement and obtain maximum performance with strong security.

• Forward and backward secrecy: Regularly updating the keys prevents an attacker from deriving past or future keys (even in the case where a key is compromised).

Threat detection accuracy

As shown in Fig. 8 with a higher level of detection accuracy for network threats, our novel approach achieved a stance ranging from 95 to 99% throughout the period being observed. This represented a higher threat detection accuracy on the traditional methods. Over the same period, it was ranging from 82 to 87% throughput value. This is because the Adaptive threat detection mechanism that is novel uses real-time monitoring. While traditional static security techniques cannot easily adapt to new attacks methods, this results in lowering their accuracy and makes them more susceptible to sophisticated attacks over the entire network. Foremost, these results collectively demonstrate that the introduction of such new technology into an SDN network not only brings benefits in terms of performance with latency, throughput and detection accuracy but, at the same time, also dynamically improves the adaptive security threats and provides effective real-time threat robustness. Higher accuracy in the new approach reduces false positives and negatives; thus, less legitimate traffic will be identified as false alarms, reducing unnecessary interruption in the traffic. To ensure the security of the SDN network without affecting throughput or latency by implementing real-time code enforcement means that if a threat is found, the system can respond immediately. With the capability of adhering to the policy of considering the SDN network status and needs to perform nothing to hamper the ongoing security measures for the future. The combination of an adaptive view towards security with the improvement of performance makes this innovative method a robust solution to problems experienced in highly dynamic and demanding SDN environments over a period of time.

Fig. 8

Performance analysis for threat detection accuracy.

To further quantify the performance of static and dynamic threat modelling, the static policy accuracy fluctuates between 70% and 80%, reflecting its consistent but constrained threat detection capability as shown in Fig. 8. Due to its fixed nature, the static policy lacks adaptability to evolving threats, making it effective only for known vulnerabilities but less responsive to emerging attack vectors. This limitation highlights its inability to react to dynamic network conditions and sophisticated threats that require real-time analysis and adaptation. In contrast, the Dynamic Threat Modelling Accuracy ranges from 85% to 95%, demonstrating a significantly higher detection accuracy. The dynamic approach adapts to changing network environments by leveraging machine learning algorithms and real-time data analysis, improving its ability to detect both known and novel threats. This adaptability allows for more proactive threat detection, reducing vulnerabilities, and enhancing overall network security. Mean Accuracy Calculation is a measure that aggregates the accuracy over a specified number of evaluations. The formula for mean accuracy is calculated using the following formula:

$$\begin{aligned} \text {Mean Threat Accuracy} = \frac{1}{n} \sum _{i=1}^n \text {Accuracy}_i \end{aligned}$$

(25)

Here n is the total number of accuracy measurements taken, and Accuracy i represents the accuracy at the i-th measure. The standard deviation for threat detection accuracy ensures the indication with the help of every single individual accuracy values deviation with respect to the mean value. The formula for the same is given as follows:

$$\begin{aligned} \text {SD Threat Accuracy} = \sqrt{\frac{1}{n-1} \sum _{i=1}^n \left( \text { Threat Accuracy}_i - \text {Mean Threat Accuracy}\right) ^2} \end{aligned}$$

(26)

This provides an understanding of the stability of detection accuracy over time. The lower the SD, the more stable performance over a period. Conversely, the higher standard deviation indicates maximum changes in detection accuracy in this interval of time.

Fig. 9

Performance analysis for enhanced security metrics.

Figure  9 summarizes the mean threat accuracy of both approaches (traditional static and SecuNet-4D), indicating the good performance of our proposed approach. Additionally, the histograms depict the distribution of accuracy levels for both policy types. Static policy accuracy shows a narrower distribution than SecuNet-4D, resulting in greater spread and lower overall accuracy. Overall, these visualizations point out that dynamic threat detection mechanisms are in fact more able not only to deliver higher performance but also to maintain accuracy levels in real scenarios where static methods fail drastically. These results as a whole show that the novel approach not only improves performance metrics of latency, throughput, and accuracy That dynamic enforcement of network policy, alongside the use of both rule-based and anomaly detection technology, is more resistant to dynamic threats than traditional static-method. The detection rates for static and dynamic policies are shown in Fig. 10. The static policy achieves a detection rate of 70%-80%, indicating no adaptability to threats emerging in real time. In contrast, the detection rate for dynamic policy is higher at 85–95%. It suggests that it has autonomous threat recognition, and its monitoring of potential threats in real-time through ML makes possible detection in real time over the network with no guaranteed process to get caught by interference between network traffic or firewalls. Figure 10 presents a box plot of false positive rates compared between static and dynamic systems. It is clear that the static policy has a higher median rate of 10–15%, evidencing its tendency to wrongly classify as benign traffic what would actually cause damage. Compared to the dynamic policy, whose rate stays between 5% and 8%. demonstrations its finer sensitivity over the time period. By comparing the bar chart of the packet loss rate, we can see that the proposed dynamic policy never exceeds 0.02%, being between 0.01% to 0.05% while the static policy highest packet losses are above 0.1%. This shows that the dynamic policy can adapt to changing network conditions and avoid data loss in transmission. On the network latency box plot comparison, the dynamic policy stands out in its responsiveness. The median latency is about 100 ms, and its variance is quite small compared with traditional static policy with a higher median latency around 130 ms, where there often even more oscillation Looking at the static policy packet loss rate distribution, we see that most values between 0.07% to 0.09% suggests large and steady packets being lost as a result of its failure to adjust to changing circumstances. In contrast, for dynamic policy data capture rate distribution is about 0.03% to 0.05% large packets are less frequent and further proof against higher burst rates emphasizes its capability to manage changes. Contrasting the usage of the computer-based resources, we find static policy makes more demands on them, with states using between 40% to 60% compared to a value around 50% for dynamic policy as illustrated in Fig.  10. This indicates better handling for dynamic resource function. Lastly, bandwidth use comparison illustrates that the dynamic policy has the best utilization of all, its range being from 70 to 100 Mbps to the traditional static policy range 50 to 80 Mbps. This underscores how dynamic policies can allocate bandwidth more effectively, thereby increasing overall network efficiency. Overall, the proposed dynamic policy outperforms the traditional static policy in all aspects, offering lower packet loss, less latency, superior resource economy, and greater bandwidth use, making it an ideal solution for modern network challenges. These improvements that, especially in dynamic, high-traffic network environments, make the proposed dynamic policy so effective at overcoming the constraints on traditional static policy can be depicted in Fig.  11. The dynamic policy can adapt its function as the situation changes in real time. This ensures consistent performance, which nowadays applications that trade off high latency for low reliability cannot do without. The dynamic policy efficiency in using bandwidth and reducing resource requirements also makes it economical and promotes environmental protection. What is more, the reduced packet loss and latency value obtained by employing dynamic policies help overall quality of service to no small extent when network loads rise and fall ceaselessly. Even when these resources are consumed, the loss is so small as to not be significant.

Fig. 10

Performance metrics comparison.

Figure 11 compares the average packet loss rates of static and dynamic policy across the evaluation period. The static policies average packet loss rate of around 8%, highlights its limitations in addressing varying network demands. In contrast, our dynamic policy achieves an average packet loss rate of about 4% indicating its dynamic capability of real-time optimization of routing and resource allocation. These design considerations allow groups of users both to collectively speed up transactions and to increase the reliability of their own experience while helping to stabilize the network under load. The dynamic policy also yields a much more stable packet loss ratio for different traffic patterns. Dynamic parameters can be applied to avoid the excess use of the network and ensure QoS for timing-sensitive applications and timely support of applications such as video conferencing and online gaming. Overall, these research results reveal how real-time traffic data can be utilized to improve several network management paradigms to better serve high-traffic scenarios in modern networks.

Fig. 11

(a) Improvements in performance metrics. (b) Enhanced security posture with novel approach.

Table 5 Performance comparison of static and SecuNet-4D.

The comparative analysis of network performance and security metrics illustrated in Table  5 shows the significant differences between static and dynamic policies. For throughput, the dynamic policy achieves a mean of 2000 Mbps with a range of 1500 to 2500 Mbps, outperforming the static policy mean of 1500 Mbps, which ranges from 1000 to 2000 Mbps. This enhanced throughput is accompanied lower mean CPU and memory usage in the dynamic policy (45% and 55%, respectively) compared to the static policy (60% and 65%). In terms of reliability, the dynamic policy exhibits a markedly lower mean packet loss rate of 0.03% (range: 0.02–0.06%) versus 0.08% for the static policy (range: 0.05–0.10%). Latency is also reduced significantly, with the dynamic policy showing a mean of 90 ms compared to 130 ms for the static approach. Finally, the detection accuracy of threats is notably higher for dynamic policies at 90% (range: 85–95%) compared to 75% for static policies (range: 70–80%), underscoring the superior adaptability and effectiveness of dynamic threat detection mechanisms.

Table 6 Comparative analysis of network performance and security metrics.

We summarize operational metrics between dynamic and static policies in Table  6 for network management. Overall performance rating of dynamic policy is 9/10, while static policies receive only 7 out of 10. They also claim an average detection time reduced to 150 ms relative to the static policies which were about 250ms with a user experience impact rated at high (4.5/5). Dynamic policies are better with resource allocation because their mean utilization is about 60% percentage with per-static policy as statistical backup and have an approximate amount of 70%. But the dynamic has low (10%) and static has (20%) system overhead. The SecuNet-4D policies lead to a 5% alert false positive rate, which is not only a lot better than static, but smartly flexible, allowing for low periods of downtime. The accumulate user satisfaction scores correspondingly grow up to 90% for SecuNet-4D with respect the baseline, in 70% which also means the dynamic approach improves their stability and responsiveness.

Comparison with state-of-the-art solutions

In order to further validate the effectiveness of the proposed SecuNet-4D framework, we provide comparison in terms of salient characteristics and performance of SecuNet-4D with the recent state-of-the-art approaches in the area of SDN security. The comparison is focused on critical parameters including anomaly detection capability, failover mechanisms, encryption adaptability, and dynamic policy adjustment.

A number of studies have investigated security enhancements in SDN, focusing on anomaly detection, fail-over mechanisms and adaptive security policies. A systematic review on securing SDN using AI/ML and blockchain-based approaches was presented by authors³², where it was emphasized the need for adaptive resilience. Similarly, Khanal et al.³³ especially in dynamic networks, threat mitigation is highlighted as the central concern to emphasize that a real-time anomaly detection framework is proposed from the author of³³. Additionally, Janabi et al.³⁴ presented a detailed survey study on intrusion detection systems regarding SDN with a wide variety of detection techniques.

Shirsath et al.³⁵ provided a literature survey of SDN security showing the multidimensional challenges in terms of policy enforcement and traffic monitoring. Furthermore, Dhadhania et al.³⁶ proposed SDN based GNN for anomaly detection, which served as an example of potential AI integration in IoT security. This helps us understand how SecuNET-4D align with current and best-of-breed approaches.

Table 7 presents a comparative analysis of SecuNet-4D against existing frameworks. The comparison highlights improvements in adaptability, self-healing capabilities, and overall network resilience.

Table 7 Comparison of SecuNet-4D with state-of-the-art approaches.

As evident from the Table  7 SecuNet-4D introduces a multi-layer anomaly detection mechanism that enhances threat identification accuracy while minimizing false positives. Unlike traditional SDN security models, SecuNet-4D incorporates a self-healing failover mechanism that ensures seamless controller failover without manual intervention. Furthermore, our framework uniquely integrates dynamic policy adjustments to enhance adaptability against evolving threats, a feature not present in most conventional solutions. Overall, the comparative analysis demonstrates that SecuNet-4D offers a significant advancement in SDN security and resilience, outperforming existing approaches by integrating self-healing capabilities, real-time anomaly detection, and adaptive security policies.

Overall observations

The micro-benchmark comparison of static and the SecuNet-4D threat detection systems offers insight into the costs, efficiencies, and effectiveness from a variety of perspectives. In the SecuNet-4D policies, threat detection Accuracy is substantially better on average (90% vs. 75%) with a tighter range of only five percent on either side around the mean (85% to 95%), versus 13% for static policies’ accuracy scores combined across all evaluation runs from each dataset/discipline rating group, in which Privacy Preservation Error is $\le$ 5%. The SecuNet-4D policies outperformed static ones in this area because these dynamic systems have adaptive mechanisms able to identify and respond faster than others directly with real-time data via ML techniques. This versatility is indispensable considering our constantly changing cyber threat terrain, where new attack vectors appear regularly. For Policy Enforcement Latency, we see that the SecuNet-4D policies also yield a performance gain; an average latency of 150 ms for the SecuNet-4D policy compared to 250 ms for static leads not only to quicker response times but ensures more swift threat mitigation, which in turn enhances user experience. Also, network throughput is significantly higher with the SecuNet-4D policies than with static ones, as shown above (2000 Mbps vs. 1500 Mbps). When the same amount of hardware resources is used but greater traffic can be processed by these dynamic systems, it serves as an indicator of their applicability to today maximum-capacity networks. The results reinforce the idea that we must transform traditional threat detection policies from static to SecuNet-4D in nature. All these changes in accuracy, latency, and throughput contribute to an improved security posture that will reduce network attacks as well as optimize resource utilization. With the increasing complexity of cyber threats, organizations would likely benefit from implementing dynamic policies to maintain high user satisfaction without compromising performance. For such an outcome, the results suggest a proposed SecuNet-4D framework of security measures that is needed and can be responsive to dynamic changes or threats.

Limitations

While the proposed SecuNet-4D framework increases security and resilience, there are still some limitations that can be taken into account from a research perspective. In high throughput environments, the encryption mechanism and the multi-layer anomaly detection may produce processing overhead, to some extent affecting the real-time network operations. Initially, the evaluation we made focused on the scale up to 2000 nodes, however, the massive SDN environment of thousand of controllers yet to be validated. The framework was tested in a Mininet virtual environment, and deploying to real devices such as physical switches in a complex heterogeneous network likely makes this more difficult. Although the key idea of dynamic policy adjustments in SecuNet-4D provide more adaptability of SecuNet-4D to defend against the novel attacks, future military work can be inspired from AI based learning to identify and adapt some specific parameters of SecuNet-4D to cope up with the newly evolving attacks.

Conclusion

This paper presents a general and scalable security architecture based on SDN, which is capable of meeting the dynamic security requirements of contemporary networks. SecuNet-4D adopts a four-pronged approach encompassing Detection, Defense, Decision making, and Dynamic Adaptation to address security to ensure real-time threat detection and mitigation, efficient decision-making and seamless dynamic adaptation to changing network conditions With dynamic policies in our proposed framework, SecuNet-4D detection rates are more than three orders of magnitude higher than those in static settings. In terms of threat detection accuracy, policy enforcement latency, and network throughput, SecuNet-4D policies succeeded in an all-out success rate against modern security attacks. With a detection accuracy of at least 90 percent, delay lower than 150 ms and throughput capable of tens or even hundreds Mb/s, dynamic policies are effective in security strategies for defending against the type of cyber threats prevalent today. Not only does this benefit security, but it also strengthens resource allocation and means that the user experience is better. The results demonstrate that the propsoed SecuNet-4D is better than static in terms of the performance metrices: threat detection accuracy, different policy enforcement latency, and overall network throughput. Compared with a static rule set, the SecuNet-4D was able to handle the network threat complexities with 90% accuracy in its detection rate , where as 150 ms latency and throughput up to around 2000 Mbps. Furthermore, the research suggests that as cyber threats evolve, they quickly repsond to them. Substantial performance improvements were demonstrated by the SecuNet-4D policies, making it necessary to review the current defense strategy. The higher threat detection accuracy and lower latency volumes indicating to an overall and more efficient operation with faster response to events, thus reducing network damage.