Machine Learning for Cybersecurity Enhancement: A Comprehensive Survey

Abstract

The issue of cybersecurity has become an international one, as more and more digital infrastructures are introduced and cyber-attacks become more advanced. Conventional rule-based security mechanisms and systems can no longer be compared with advanced persistent threats, zero-day attacks and polymorphic malware. Machine learning (ML) and its feature of learning behaviour, detecting anomalies, and developing in response to new attacks have become a radically new way to enhance cybersecurity. When trained with supervised learning, unsupervised, and reinforcement learning models, ML can be applied to detect malicious activity in real time, optimize intrusion detector systems and assist in enacting automatic threat response policies. This is a machine learning survey cybersecurity article. It explains the basic ML-based models, including decision trees, support vectors machines (SVMs), neural networks, and deep learning models, and how these are used to analyse malware, intrusion prevention, phishing, and fraud. The application of ensemble learning and hybrid ML performance towards high-security is also discussed in the paper. Moreover, it discusses some of the existing issues such as data imbalance, adversarial attack on ML models, loss of interpretability and large computational needs. This survey concludes that in spite of the fact that today ML has become a far more effective tool in defending against cyber-attacks, it is weak in terms of scalability, interpretability, resistance to adversarial manipulation, and adversarial manipulation. It is to these gaps the paper is devoted, as they understand the need to combine explainable AI, federated learning to attain distributed security, and adversarial resistant ML models. One potential strategic direction of the research is to use lightweight models to improve the security of the IoT, transfer learning, and privacy-preserving ML to other cybersecurity settings. This present paper concludes that machine learning is the foundation of the future of cybersecurity and can be utilised to provide dynamic, smart, and scalable defence services through the synthesis of new research and practical application of the technology.