Table 1 Comparison of existing security approaches in MANETs.
From: Secure bio-inspired optimization with intrusion aware on-demand routing in MANETs
Study | Technique used | Strengths | Weaknesses and limitations |
|---|---|---|---|
Guided Whale Optimization Algorithm (GWOA)23 | Multi-objective optimization + Trust-based routing | Enhances packet delivery and minimizes overhead | Lacks encryption, making it vulnerable to data interception |
EAACK (Enhanced Adaptive Acknowledgment)24 | RSA encryption + P2P-ACK protocol | Prevents packet dropping and improves routing security | High computational overhead due to RSA encryption |
ACHIO (Adaptive Coronavirus Herd Immunity Optimizer)25 | Chaotic map-based encryption for MANET-IoT | Achieves 86.02% encryption accuracy | Increased processing time for larger datasets |
Distributed Clustering Algorithm Dependent IDS (DCAIDS)26 | Intrusion detection via clustering | Reduces delay and prevents unauthorized access | Energy consumption is high due to continuous monitoring |
Optimized Link State Routing (OLSR) Protocol27 | Proactive routing with real-time updates | Suitable for military applications with dynamic topology | High routing overhead; security vulnerabilities persist |
Refined Adaptive Harris Hawks Optimization (RAHHO)28 | Adaptive security parameter updates | Strong attack detection with dynamic adjustments | Computationally expensive; unsuitable for real-time use |
Authentication-Based Associate Neighbor Node Selection (AANNS)29 | Trust metric using signal strength & energy | Prioritizes reliable nodes to prevent Sybil & blackhole attacks | Not scalable for large, dynamic networks |
Active Routing Authentication System (AAS)30 | BAN logic for authentication | Increases packet delivery by 18.4%, resists route spoofing | Authentication delays; requires specific protocol integration |
Cooperative Self-Scheduling Secure Routing Protocol (CoS3RP)31 | Elite Sparrow Search Algorithm (ESSA) + Multipath Optimal Distance Selection (MODS) | Reduces latency, improves routing and security | Requires high computational power for clustering |
ATAODV (Multi-Agent System)32 | Aggregated Trust (AT) + Routing Agent (RA) | Provides two-layer security with authentication & route trust | Additional processing delays in establishing trust values |
Kangaroo-based IDS with Bi-LSTM33 | Deep learning + TriChain Blockchain | Secure routing with improved intrusion detection | Complexity in route discovery; higher processing overhead |
Graph Theory-Based Optimization for MANET34 | Machine learning-based network security | Efficient in handling large datasets and detecting anomalies | Requires extensive data collection and model training |
Swarm Intelligence-based Secure AODV (SIS-AODV)35 | ECC encryption + Ant Colony Optimization | Strong authentication and non-repudiation with lightweight hashing | Moderate overhead due to cryptographic operations |
