Scholarlink

<p>Modern wireless and distributed networks face adaptive attackers that destabilize routing and expose fundamental weaknesses. The safe routing architecture choice framework includes contextual trust evolution, entropy driven protocol mutation, semantic intent estimate, and multi objective topology optimization. These components boost robustness, minimize protocol predictability, and identify malicious routing. Routing uses updated behavioral data to adjust trust tendencies and route selection in dynamic environments. Protocol mutation reduces fingerprinting and reconnaissance by adjusting control exchange sequences to channel entropy. Semantic analysis of route sequences detects adversary deviations before interruption in the process. Topology optimization balances energy, secure zone coverage, and communication delay for strong path building in concentrated threat environments. These methods create a self adjusting routing model that maintains operational consistency in numerous hostile situations. Intent recognition, protocol robustness, anomaly reaction time, and energy aware path stability improve experimentally. The architecture balances routing logic security with large scale wireless system performance. Experimental analysis indicates packet loss reduction of 38% under attack scenarios, protocol fingerprint resistance improvement to 76%, and intent detection by more than 92% accuracy.</p>

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Design of an improved model for secure routing using CAFR-net and EPM-core in adversarial network environments

  • 期刊论文

  • 发表日期：

    2025年12月20日

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  • Rahul Mahajan,
  • Srikant V. Sonekar

摘要

Modern wireless and distributed networks face adaptive attackers that destabilize routing and expose fundamental weaknesses. The safe routing architecture choice framework includes contextual trust evolution, entropy driven protocol mutation, semantic intent estimate, and multi objective topology optimization. These components boost robustness, minimize protocol predictability, and identify malicious routing. Routing uses updated behavioral data to adjust trust tendencies and route selection in dynamic environments. Protocol mutation reduces fingerprinting and reconnaissance by adjusting control exchange sequences to channel entropy. Semantic analysis of route sequences detects adversary deviations before interruption in the process. Topology optimization balances energy, secure zone coverage, and communication delay for strong path building in concentrated threat environments. These methods create a self adjusting routing model that maintains operational consistency in numerous hostile situations. Intent recognition, protocol robustness, anomaly reaction time, and energy aware path stability improve experimentally. The architecture balances routing logic security with large scale wireless system performance. Experimental analysis indicates packet loss reduction of 38% under attack scenarios, protocol fingerprint resistance improvement to 76%, and intent detection by more than 92% accuracy.