Performance Optimized Machine Unlearning in Intrusion Detection Systems for High Model Accuracy: novel approach.

Abstract

Cyber attacks are growing in number and complexity. Modern networks faces various real cyber threats such as API, DDPS, ICMP,UDP, TCP, botnet, Bit LINK kind of attacks. Intrusion detection system depends on machine learning for detect these attacks, but they faces various challenges in present scenario like un wanted data , poisoned data , stale data and privacy risk. Machine unlearning MU provides reliable and trust full solution by allowing various kind of latest model to remove harmful, unwanted, outdated data. This paper presents comprehensive survey of recent studies on machine UN learning applied to intrusion detection system IDS. We analyzed various approaches for unlearning time optimize, model accuracy, attacks types, and computational efficiency. the study highlight bets practices , performance trends, research gaps, time optimization , model performance accuracy , providing a roadmap for future development of high-accuracy, adaptive IDS frameworks. This paper provides researchers and practitioners with: (1) a structured, critical appraisal of the MU-IDS landscape; (2) quantitative benchmarks for cross-method comparison; (3) identification of unresolved challenges and adversarial threat models; and (4) concrete future research directions toward practical, privacy-compliant, and adversarially robust intrusion detection systems.