ADVANCED MACHINE LEARNING TECHNIQUES FOR CYBERSECURITY: OPPORTUNITIES AND EMERGING CHALLENGES

Abstract

This study investigates applying advanced machine learning techniques in enhancing cybersecurity systems, particularly in phishing detection, network intrusion detection, and malware and ransomware classification. Supervised learning algorithms such as random forests and support vector machines (SVM), deep learning models like convolutional neural networks (CNN) and recurrent neural networks (RNN), and ensemble methods were employed to improve detection accuracy and reduce false positives. The study also addresses key challenges, including adversarial attacks, data imbalance, and the need for continuous learning to adapt to evolving threats. Results indicated that machine learning models, especially deep learning techniques, demonstrated high accuracy in detecting complex threats, with phishing detection models achieving over 96% accuracy and network intrusion detection models reaching 98.2%. The study also explored the use of transfer learning and continuous learning systems, which showed promise in adapting to new threats while minimising the need for extensive retraining. However, adversarial vulnerabilities and the challenge of catastrophic forgetting in continuous learning models remain significant obstacles. Recommendations include integrating adversarial training, improving data augmentation techniques, and optimising continuous learning systems for real-time threat adaptation. This research contributes to the growing body of knowledge on machine learning applications in cybersecurity, highlighting both its potential and the need for ongoing refinement to address emerging cyber threats.