A Robust Model for Enabling Insider Threat Detection and Prevention
A. Sheik Abdullah, Shivansh Dhiman, Arif Ansari · 2025
In today's era, cybersecurity is essential as data breaches and cyber-attacks become more prevalent. Insider threats, which are security risks from within an organization, involve employees or contractors who possess access to information and exploit it to harm the organization. Simultaneously, new methods and technologies are emerging that leverage tools like machine learning, artificial intelligence, and behavioral analytics to identify and mitigate insider threats accurately. As technology advances rapidly, malware also evolves in sophistication. This challenges cybersecurity professionals who strive to keep up with the changing landscape. Modern malware utilizes techniques, such as polymorphism and metamorphism, enabling it to modify its code to evade detection by traditional antivirus programs. Consequently, detecting and removing malware from compromised systems has become increasingly difficult for security experts. We can evolve these measures into more robust threat detection and prevention tools. This will enable us to better protect against potential security breaches and ensure the safety and security of our systems and data.