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@article{198097,
        author = {Pallavi A. Wadekar and Anuja P. Gosavi and Nikita S. Kardile and Omkar R. Ghadge},
        title = {Intrusion Detection System Using PCA With Random Forest Approach},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {9402-9404},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198097},
        abstract = {Wireless Sensor Networks are prone to security threats due to their distributed nature. This work presents a machine learning-based intrusion detection system designed for efficient and real-time detection. The system analyzes network traffic features to classify activities as normal or malicious. It achieves approximately 99% accuracy with low computational complexity, making it suitable for practical deployment.},
        keywords = {Machine Learning, Intrusion Detection, Network Security, Traffic Analysis, Real-Time Detection, Random Forest},
        month = {April},
        }