Cyber Threat Detection Using Machine Learning

As our world becomes more and more dependent on cyberspace in all fields, the number of cyber threats, their frequency and complexity have risen with an alarming rate. There are many forms of illegal activities committed over the internet, and together they form cyber-threats; from malware to phishing attacks, APT (advanced persistent threats), ransomware etc. Traditional security sits interaction of these threats is still limited compared to evolving nature, and hardly mitigates zero day attacks. As a result, Machine learning (ML) has become an essential indeed much-needed technology to empower Cyber threat detection and response. This paper investigates the increase in cyber threats as well as how cybersecurity techniques are perpetually enforced, while analysing methodology used by hackers. Here, we investigate a few of the bleeding-edge ML techniques being applied to detect and fight cyber threats from deep learning models like Convolutional Neural Networks (CNNs), Recurrent Neural Network, ensemble learning methods such as Random Forest and Support Vector Machine (SVM). This comprehensive overview highlights the effectiveness of these ML techniques in identifying and mitigating cyber threats, emphasizing the need for continuous innovation to stay ahead of increasingly sophisticated cybercriminal activities. KEYWORDS: Cyber Threat; Cybercrime; Machine Learning Application; Malware; Phishing; Ransomware; Spam;