Punitive-IDS: A Reputation-Based Punishment Framework for Enhancing Network Performance in Malicious Node Management for MANETs

Abstract

Malicious node activity in Mobile Ad-hoc Networks (MANETs) degrades not only security integrity but also
fundamental network performance indicators, including throughput, packet delivery ratio (PDR), and end-to-end
delay. Existing intrusion detection systems predominantly optimise classification accuracy while neglecting the
network-level consequences of detection decisions, particularly the disruptive effects of false positives on routing
availability and connectivity. This paper presents Punitive-Intrusion Detection System (IDS), a reputation-based
punishment framework for an Intrusion Detection System (IDS) that integrates a Q-Learning-enhanced Artificial
Neural Network Intrusion Detection System (ANN-IDS) with a Random Forest classifier and a severity-aware
punitive decision engine. Detected malicious nodes receive graded reputation score reductions and are excluded
from routing decisions, with reinstatement permitted upon subsequent behavioural improvement. Evaluation
across four attack scenarios and eight node density configurations (25 to 200 nodes) using NS-3 simulation
demonstrates that Punitive-IDS achieves a throughput of 779.4 kbps, packet delivery ratio of 96.7%, and end-toend delay of 118.2 ms under active attack conditions, representing improvements of 27.3%, 40.3%, and 44.9%
respectively over an unprotected MANET baseline. Comparative analysis confirms that Punitive-IDS outperforms
Static ANN-IDS with Random Forest, SVM-based, and KNN-based intrusion detection frameworks across all
network-level metrics, while simultaneously achieving a superior classification accuracy of 99.21%.