RESEARCH ON MECHANISMS TO IMPROVE THE EFFICIENCY OF COMPUTER NETWORK PROTECTION AGAINST POLYMORPHIC COMPUTER VIRUSES

Abstract

The objective of this undertaking is to execute a comprehensive assessment of the methodologies used to enhance the robustness of a computer network’s defenses against polymorphic computer viruses. This involves identifying both the strengths and weaknesses inherent in the individual components comprising this protective mechanism, and ultimately proposing adaptive strategies to counteract contemporary threats. A primary focus will be placed on developing novel methods designed to effectively combat polymorphic viruses, which possess the ability to alter their code to evade detection by conventional antivirus systems. The novelty of the research resides in the integration of adaptive methodologies, specifically behavioral analysis and machine learning techniques, to robustly identify previously unseen polymorphic virus samples. This is achieved by accounting for their inherent code mutation capabilities, preventing detection by conventional antivirus solutions. The suggested mechanism utilizes neural networks for the dynamic modeling of software behavior. This approach facilitates prompt response to emerging threats and the ability to adapt to changes within viral code. The outcome is a substantial increase in detection accuracy alongside a reduced occurrence of false positives. The study’s findings validate the proposed mechanism’s effectiveness against polymorphic viruses, leveraging a dynamic approach for anomaly detection. Implementing these methods greatly enhances the security of computer networks, fortifying them against the rapid evolution of current cyber threats. The outcomes suggest that employing machine learning and neural networks is a promising avenue for advancements in cybersecurity systems. Such systems can effectively counter unknown threats, a critical need in the present environment where cyberattacks are escalating in number and complexity. Future efforts will focus on optimizing the suggested mechanism for its integration into actual network defense systems, ultimately ensuring high security levels and minimizing losses due to cyberattacks.