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@article{176506,
        author = {Vishal V and FATHIMA G},
        title = {AI POWERED MALWARE THREAT DETECTION USING RASPBERRY PI},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {7287-7291},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176506},
        abstract = {With the rapid growth of IoT devices, cybersecurity threats have become a major concern. Traditional malware detection systems are resource-intensive, making them unsuitable for low-power devices like Raspberry Pi. This research presents an AI-powered malware threat detection system that leverages machine learning algorithms for real-time threat detection on Raspberry Pi. The proposed system integrates lightweight deep learning models to classify malicious activities, ensuring efficient security without overloading system resources. This study demonstrates that Raspberry Pi, combined with AI, can serve as an affordable and effective cybersecurity solution for IoT environments. The methodology involves collecting and preprocessing network and file behavior data, followed by feature extraction to train and evaluate various classification models such as Convolutional Neural Networks (CNN), Random Forest, and Support Vector Machines (SVM). Malware is defined in this context as any code exhibiting unauthorized or malicious behavior on IoT endpoints. The system architecture incorporates components for data acquisition, preprocessing, real-time classification, and response automation. Experimental findings indicate a detection accuracy of over 90% with minimal latency, validating the efficiency of deploying AI-based detection on resource-constrained devices. The study further highlights the scalability of the system for smart home and industrial IoT environments, providing a proactive approach to endpoint security},
        keywords = {AI Security, Malware Detection, Raspberry Pi, IoT Security, Machine Learning, Deep Learning.},
        month = {April},
        }