Network Shield: Machine Learning Based Threat Detection

Abstract

The rapid escalation of cyber-crime has created an urgent demand for advanced and intelligent solutions to safeguard modern computing environments. Traditional Intrusion Detection Systems (IDS), which primarily rely on rule-based or signature-driven methods, have proven insufficient in detecting and mitigating the dynamic and sophisticated nature of contemporary cyber-attacks. These conventional systems often fail to recognize emerging threats and adapt to the evolving tactics used by attackers. Machine learning has become a pivotal tool in the realm of cybersecurity, offering powerful capabilities for detecting intrusions, classifying malware, filtering spam, and identifying phishing attempts. Unlike static systems, machine learning models can analyse vast amounts of data, learn patterns of malicious behavior, and generalize to uncover unknown or zero-day threats. Although machine learning introduces its own set of challenges such as handling imbalanced datasets, feature selection, and interpretability it consistently demonstrates superior performance in identifying security threats. It significantly reduces the manual workload on security analysts and enhances the accuracy and responsiveness of threat detection systems. Adaptive learning techniques can yield high detection rates, minimize false alarms, and operate with efficient computational resource usage. This research focuses on the development of machine learning-based cybersecurity solutions aimed at overcoming the limitations of traditional IDS. The goal is to design intelligent, adaptive, and scalable systems that bolster cybersecurity defenses and protect network infrastructures against the ever-evolving landscape of cyber threats.