Intelligent Cyber Attack Detection and Monitoring: A Comprehensive Review for Next-Generation Cybersecurity

Cybersecurity professionals primarily focus on assessing risk profiles and developing strategies to mitigate them effectively. A key goal in this domain is to design robust methods that strengthen security measures. The integration of machine learning has significantly enhanced modern cyber defense systems. Breakthroughs in storage capacity, computing power, and networking have accelerated the adoption of cloud services, advanced networks, and evolutionary programming. As digital transformation accelerates worldwide, the demand for addressing complex privacy and security challenges grows rapidly, requiring stronger safeguards against emerging threats. Increasing vulnerabilities in computer systems have contributed to a rise in global cyber terrorism. By leveraging machine learning techniques, various global cybersecurity challenges—such as detecting malware, identifying ransomware, recognizing fraudulent activities, and verifying spoofing attempts—are being tackled more effectively. This review explores how online behavior modeling can be used for both attack and defense purposes, offering insights into cyber risks through machine learning tools and methodologies. It examines the most common cybersecurity threats and highlights how machine learning supports the detection and prevention of attacks, vulnerability analysis, and open-source risk evaluation in the digital landscape.