Deep Learning-Based Intrusion Detection System for Internet of Things Networks for Enhancing Security Against Cyber Attacks

Deep learning algorithms are used in this research to propose a novel approach to intrusion detection in Internet of Things (IoT) networks. The suggested intrusion detection system employs a six-layered deep neural network architecture, which is augmented with a feature extraction module to examine network packet data and identify dangerous activities. The system was evaluated against a large dataset that comprised the following five primary attack types encountered in IoT environments: Blackhole Attacks, Opportunistic Attacks, Distributed Denial of Service (DDoS) Attacks, Wormhole Attacks, and Sinkhole Attacks. Performance evaluation metrics such as precision, recall, accuracy, specificity, and F1 Score were used to evaluate the system's effectiveness in recognizing and categorizing attacks. The data demonstrate that across various forms of assault, accuracy and recall rates vary from 93 to 96.4%. The F1 Score demonstrates balanced performance, highlighting the system's ability to eliminate false positives and false negatives. The feature extraction module considerably improved the dataset by adding essential network packet attributes such as source and destination IP addresses, session duration, and transmission rates, increasing the overall accuracy of the intrusion detection system. The utility of the proposed deep learning model in dealing with diverse attack scenarios is shown by its ability to detect and neutralize various intrusion attempts. This research extends intrusion detection methodologies by presenting a robust and intelligent solution to safeguard critical data and resources in IoT networks. Continuous research and development, on the other hand, are essential in real-world IoT deployments to handle new attack vectors and provide system adaptability to dynamic network situations.