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@article{176810,
        author = {Ashfan Ulla Shaik and Abhiram R Nair and Aniruddha J Nair and Abhijith B and Dr. Gyanappa A Walikar},
        title = {AI Driven Antivirus Software},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {6668-6671},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176810},
        abstract = {The rapid advancement of artificial intelligence (AI) has significantly enhanced the capabilities of antivirus software, transitioning it from traditional signature-based detection to more dynamic and proactive defense mechanisms. AI-driven antivirus software leverages machine learning, deep learning, and behavioral analysis to identify, mitigate, and predict emerging cyber threats with greater precision and speed. Unlike conventional methods that rely on predefined virus signatures or heuristic analysis, AI-powered solutions continuously evolve by learning from new data, adapting to novel attack vectors, and autonomously detecting patterns in malicious activities.    
This shift enables AI-driven systems to detect zero-day vulnerabilities, polymorphic malware, and sophisticated advanced persistent threats (APTs) that may bypass traditional security measures. Additionally, AI-powered antivirus software often integrates real-time decision-making capabilities, reducing the reliance on human intervention and significantly enhancing system resilience against cyberattacks. While challenges such as false positives, data privacy concerns, and adversarial attacks on AI models remain, the integration of AI in antivirus technology holds promise for a more adaptive and intelligent cybersecurity framework, offering a robust defense against the increasingly complex landscape of cyber threats.},
        keywords = {Artificial Intelligence (AI), Machine Learning (ML) Antivirus Software, Cybersecurity, Threat Detection, SVM.},
        month = {April},
        }