Volume : 12, Issue : 2, FEB 2026

ADVANCING CYBERSECURITY DEFENCE MECHANISMS: A MACHINE LEARNING APPROACH TO THREAT DETECTION, PREVENTION, AND MITIGATION

SUMANDEEP KAUR, DR. GEETANJALI

Abstract

The growing complexity and frequency of cyberattacks challenge the effectiveness of traditional cybersecurity methods, which typically rely on static, signature-based detection. These conventional approaches are often inadequate against rapidly evolving threats, including zero-day exploits, advanced persistent threats (APTs), and AI-driven attacks. This thesis explores the application of machine learning (ML) as a dynamic and adaptive strategy to strengthen cybersecurity through improved threat detection, prevention, and response.

The research investigates how different ML techniques—supervised, unsupervised, and reinforcement learning—can be utilized to build intelligent systems that detect anomalies, analyse network behaviour, and predict potential breaches in real time. The study evaluates the performance of various algorithms, including decision trees, support vector machines, neural networks, and clustering methods, in identifying malicious activity within diverse and complex digital environments.

In addition to algorithmic performance, the dissertation addresses several key challenges in deploying ML for cybersecurity. These include handling imbalanced datasets, selecting relevant features, ensuring model robustness against adversarial attacks, and improving the interpretability of ML models for security analysts. Through extensive experimentation and comparative analysis, the research highlights the strengths and limitations of each ML approach in the context of cybersecurity.

The findings demonstrate that machine learning can significantly enhance the flexibility, accuracy, and efficiency of cyber defence systems. By enabling proactive threat detection and adaptive responses, ML-based models offer a pathway toward more resilient and scalable security infrastructures. This work contributes practical insights and methodological guidance for researchers and practitioners aiming to develop next-generation cybersecurity frameworks powered by machine learning.

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IESRJ

International Educational Scientific Research Journal

E-ISSN: 2455-295X

International Indexed Journal | Multi-Disciplinary Refereed Research Journal

ISSN: 2455-295X

Peer-Reviewed Journal - Equivalent to UGC Approved Journal

Peer-Reviewed Journal

Article No : 7

Number of Downloads : 79

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