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@article{187373,
        author = {Apoorva Patil and Dr.Ankit Pandit and Dr.Sanjeev Kumar Gupta and Dr.Laxmi Singh},
        title = {Network Security by Identifying Malicious Activity by Optimizing Features and Machine Learning Model},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {4275-4282},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187373},
        abstract = {Wireless Sensor Networks (WSNs) are widely used in military and civilian applications for remote monitoring through interconnected sensors. Their open deployment and wireless communication make them highly prone to security vulnerabilities. As a result, WSNs are especially susceptible to various routing attacks. This paper presents a Genetic Algorithm and Feature-Optimized Nonnegative Matrix Factorization System (GAFNS) for efficient network intrusion detection. The proposed model performs dataset preprocessing, feature selection using a Genetic Algorithm, and feature transformation through Nonnegative Matrix Factorization (NMF). An ensemble of Long Short-Term Memory is then used for classification.},
        keywords = {Genetic Algorithm, Intrusion Detection, Feature Optimization, Machine Learning.},
        month = {November},
        }