Enhancing Cyber security through Machine Learning-Based Intrusion Detection and Malware Classification

The rapid growth of digital infrastructure has been accompanied by an alarming increase in the frequency, scale, and sophistication of cyberattacks. Traditional signature-based and rule-based security mechanisms are often inadequate for detecting novel, zero-day, and polymorphic attacks. In this context, machine learning (ML) has emerged as a promising approach to strengthen cybersecurity defenses through intelligent, adaptive, and data-driven threat detection. This paper presents a comprehensive study on the design and development of machine learning-based intrusion detection systems (IDS) and malware classification frameworks. Supervised, unsupervised, and deep learning models are explored to improve detection accuracy, reduce false positives, and enhance adaptability to evolving attack patterns. Benchmark cybersecurity datasets such as NSL-KDD, CICIDS2017, and VirusShare are utilized for experimental evaluation. The proposed framework integrates data preprocessing, feature extraction, model training, adaptive learning, and real-time deployment. Experimental results demonstrate that ML-based approaches significantly outperform traditional methods in detecting intrusions and classifying malware, highlighting their suitability for modern cybersecurity environments.