Machine Learning-Based Intrusion Detection for Emerging Network Security Threats

Abstract

The rapid growth of cloud computing, Internet-connected services, distributed applications, and enterprise networks has significantly increased the complexity of modern cybersecurity threats. Traditional intrusion detection systems primarily depend on predefined signatures and rules, which may be less effective against previously unknown or continuously evolving attacks. Machine learning provides an alternative approach by learning patterns from network traffic and identifying behaviour that differs from normal communication. This paper proposes a machine learning-based intrusion detection framework that integrates network traffic collection, data preprocessing, feature extraction, machine learning-based threat detection, attack classification, risk assessment, and security response. The proposed framework is designed to support the identification of both known and emerging network security threats. Publicly available datasets such as CICIDS2017 and UNSW-NB15 can be used to validate the proposed approach. The study focuses on developing a structured detection architecture and evaluation methodology without assuming or presenting unverified experimental results. The proposed framework provides a foundation for developing adaptive and reliable network intrusion detection systems for modern digital environments.