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@article{201679,
        author = {Soham Murkar and Ayana Xavier and Aarohi Talele and Akanksha Singh and Nilambari Narkar},
        title = {Forti Shield},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {8049-8054},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201679},
        abstract = {The rapid growth of internet usage has significantly elevated the risk of malware threats targeting individuals, organizations, and educational institutions. Traditional antivirus systems rely on signature-based detection, which renders them largely ineffective against new, unknown, and zero-day malware variants. Furthermore, most conventional tools perform post-infection scanning, allowing threats to cause serious damage before they are identified. To address these limitations, this paper presents Forti Shield, an intelligent, AI-driven malware detection system that operates in real time. Forti Shield continuously monitors the user’s Downloads folder and automatically analyzes each newly downloaded file. The system converts binary file content into image representations and employs a Convolutional Neural Network (CNN) model to classify files as safe or malicious based on learned visual patterns. Upon detecting a threat, the system immediately quarantines the malicious file, sends a real-time notification to the user, and records the incident in a secure database. Experimental results demonstrate that Forti Shield achieves accurate malware classification across diverse file types, providing proactive protection even against previously unseen threats.},
        keywords = {AI Malware Detection, Binary Image Analysis, CNN, Cybersecurity, Deep Learning, File Monitoring, Quarantine System, Real-Time Detection, Threat Prevention, Zero-Day Attack.},
        month = {May},
        }