“AI-Driven Intrusion Detection: Machine Learning for Harmful Packet Detection”

Abstract: In an era of increasing digital connectivity, the sophistication and frequency of cyberattacks have grown exponentially, rendering traditional rule-based intrusion detection systems (IDS) insufficient. This literature survey explores the recent advancements in AI-powered IDS solutions, with a particular focus on machine learning (ML)-driven approaches for harmful packet detection. The review analyzes 25 recent research papers published between 2020 and 2025, highlighting trends in model development, dataset utilization, real-time deployment, edge computing, and automation in threat response. While many existing systems achieve high detection accuracy using algorithms such as Random Forest, SVM, CNN, and ensemble techniques, they often fall short in critical areas—such as real-time performance, attack simulation, automated remediation, and handling minority class attacks. This survey identifies those gaps and establishes the motivation for a lightweight, modular IDS that not only detects but also responds to intrusions through intelligent patch recommendations. By comparing existing approaches and their limitations, the paper lays the foundation for building adaptive, scalable, and semi-autonomous security solutions suitable for modern network environments.

Keywords: Intrusion Detection System, Machine Learning, NSL-KDD, Network Security, Automated Patching, Real-Time Threat Detection, Cyberattack Classification, Lightweight IDS