The rise of digital threats: A historical perspective on computer viruses and cybersecurity

Abstract

The rapid evolution of computer viruses has intensified the need for advanced detection mechanisms. This study examines the historical progression of malware and explores the role of machine learning in enhancing cybersecurity defenses. By analyzing major incidents, such as the Morris Worm, ILOVEYOU virus, and WannaCry ransomware, this research highlights patterns in malware development and the increasing sophistication of cyber threats. Findings reveal that traditional signature-based detection methods struggle to keep pace with evolving malware, necessitating a shift toward machine learning-based approaches. Techniques such as anomaly detection, behavioral analysis, and deep learning models have proven effective in identifying previously unseen threats. This study underscores how machine learning enhances real-time threat detection by recognizing subtle patterns and adapting to new attack strategies. Furthermore, the results highlight the challenges of adversarial attacks, where malware is designed to evade detection by manipulating input data. The study emphasizes the need for robust machine learning frameworks capable of resisting such threats. Additionally, integrating AI-driven models with traditional security measures has been shown to improve detection accuracy and response time. By leveraging historical insights and emerging technologies, this research advocates for a proactive approach to cybersecurity. The findings reinforce the importance of continuous advancements in machine learning-driven threat detection to counter increasingly sophisticated cyberattacks.

Keywords

• machine learning-based detection
• cybersecurity threats
• anomaly detection
• adversarial attacks
• malware evolution