The Role of Modern Algorithms in Computer Virology

Abstract

As cyber threats become extremely sophisticated, cyber experts are studying computer virology in greater depth. This review explores the critical case for the use of modern algorithms, especially Artificial Intelligence (AI), and Machine Learning (ML), in dealing with these ever-changing challenges. Today, these capabilities of malware detection, analysis, and prevention belong to AI and ML techniques that allow you to identify new threats, predict attack patterns, and automate threat mitigation processes. To detect anomalies and malicious behaviours with high accuracy, these algorithms use a very large scale of data that are derived from multiple sources including network traffic and log files. Through a review of key methodologies, including supervised and unsupervised learning, neural networks, and deep learning, the review is used to understand how malware can be generally defeated, and in particular, Natural Language Processing (NLP) to decipher malware codes and their intentions is indicated. While these technologies have exhibited tremendous promise, these technologies present challenges like being vulnerable to adversarial attacks and requiring high-quality training data. Finally, this paper outlines future research direction of enhancing algorithm robustness and maximizing real-time detection, while balancing privacy concerns at optimal threat mitigation. We explore all these dimensions to emphasize the transformative power of AI and other algorithms to indeed reinforce these artificial ecosystems, in light of growing threats of ever more complex cyber activities.