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@article{196072,
        author = {Manju Dalai and Nitisha Rajgure},
        title = {A Deep Learning and machine learning Driven Framework for Intelligent Cyber Attack and Malware Detection system},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {1532-1540},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196072},
        abstract = {With the increasing dependence on desktop-based systems for sensitive data processing cyber-attacks and malware infections have become more frequent and sophisticated. Conventional security mechanisms often fail to detect advanced and unknown threats due to their reliance on predefined signatures. To address this challenge, this project proposes a Deep Learning and Machine Learning driven intelligent cyber-attack and malware detection framework implemented as a desktop application. The proposed system utilizes machine learning techniques to analyze system behavior, network activity, and executable file characteristics to identify malicious patterns. Deep learning models are incorporated to learn complex attack behaviors and detect previously unseen malware variants. The desktop application continuously monitors system operations, extracts relevant features, and classifies activities as benign or malicious in real time. A graphical user interface provides clear visualization reports, and detection status, enabling users to respond quickly to potential security breaches. By integrating machine learning and deep learning approaches, the system improves detection accuracy, minimizes false alarms, and adapts to evolving cyber threats. The proposed desktop-based solution offers an efficient, intelligent, and user-friendly approach to enhancing system security and protecting against modern cyber-attacks and malware threats.},
        keywords = {},
        month = {April},
        }