Machine Learning in Identifying Cyber Threats
Jaspreet Kaur, Kamini Sharma, Aman Preet · 2025
In an era where cyberattacks are escalating at an unprecedented rate, with global damages projected to exceed $10 trillion annually by 2025, the urgency to fortify digital defences has never been greater. Traditional cybersecurity measures, though crucial, often prove inadequate against the growing complexity and adaptability of modern threats. This chapter discovers the transformative impact of machine learning (ML) on contemporary cybersecurity, highlighting its capacity to detect, predict, and counter threats in real time. Key ML techniques, such as supervised and unsupervised learning, anomaly detection, and deep learning, are analysed alongside their applications in malware detection, intrusion prevention, and threat intelligence. The study also examines key challenges, such as adversarial attacks, data privacy concerns, and the necessity for robust training datasets. By leveraging the power of ML, organizations can not only enhance their defensive capabilities but also proactively adapt to the constantly evolving cyber threat landscape, marking a paradigm shift in the way digital security is approached.