Securing WSN-IOT with Firefly Algorithm and Machine Learning for Intrusion Detection System

Internet of Things (IoT) has seen significant problems with network intrusion detection. Existing systems often cannot consistently achieve maximum performance on many classification tasks. To improve network intrusion detection effectiveness, we provide a novel intrusion detection technique in this research. This work employs the Firefly Algorithm in conjunction with a synergistic machine learning method to enhance the security of wireless sensor networks (WSNs) and the Internet of Things. The proposed technique much exceeds expectations in terms of improving intrusion detection accuracy. This method offers a novel approach to security-related system optimization by combining machine learning with the Firefly Algorithm. In contrast to the NSL-KDD dataset, the proposed model is 99.44% accurate. In this work, we created a model to enhance intrusion detection for WSN-IoT systems by fusing machine learning with the Firefly Algorithm.

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