Network Intrusion Detection System Using Machine Learning

Abstract: With the rapid growth of Internet services and digital communication technologies, cyberattacks have become increasingly frequent, complex, and sophisticated. Modern computer networks are continuously exposed to various types of security threats that can compromise the confidentiality, integrity, and availability of data. Traditional security mechanisms such as firewalls and signature-based detection systems are widely used to protect networks; however, these methods are often ineffective in detecting unknown or evolving attack patterns. Therefore, more intelligent and adaptive security solutions are required to ensure effective protection of network infrastructures.

Intrusion Detection Systems (IDS) play an important role in identifying malicious activities by continuously monitoring network traffic and detecting abnormal behaviour. In recent years, machine learning techniques have gained significant attention in the field of cybersecurity due to their ability to analyse large volumes of data and identify hidden patterns associated with cyberattacks. This paper proposes a Machine Learning-based Network Intrusion Detection System designed to improve the detection of cyberattacks in network environments. The proposed system performs several processes including data preprocessing, feature selection, and classification to distinguish between normal and malicious network traffic. Various machine learning algorithms are applied to analyse network behaviour and detect attacks such as Denial of Service (DoS), Probe attacks, and unauthorized access attempts.

The proposed model is trained and evaluated using a standard intrusion detection dataset and implemented using Python. Experimental analysis demonstrates that the proposed approach improves detection accuracy while reducing false alarm rates. The results indicate that the system provides an efficient and reliable solution for enhancing network security and detecting cyber threats in modern computing environments.

Keywords: Intrusion Detection System, Machine Learning, Cyber Security, Network Security, Data Classification