Enhancing network intrusion detection through robust machine learning models : a comparative analysis
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The proliferation of WiFi-enabled smart devices in enterprise IoT networks introduces both enhanced consumer experiences and significant security vulnerabilities. Anticipating a rise to 46.5 percent adoption in US enterprise networks by 2023, securing IoT systems is paramount. This thesis addresses this imperative, emphasizing the need for active defense mechanisms capable of countering a spectrum of threats in the dynamic IoT landscape. Specifically, we conduct a comprehensive analysis by combining diverse network packet datasets from sources like the CTU Aposemat IoT23 dataset, Mizzou Cyber Range, and IEEE Dataport. A comprehensive and robust framework for network intrusion detection leveraging machine learning models including Random Forest, Gradient Boosting and Multilabel Perceptrons is developed employing meticulous preprocessing and Principal Component Analysis (PCA) for feature selection. Evaluation metrics, such as classification reports and ROC AUC curves, affirm the efficacy of our models in network intrusion detection. Further enhancing our approach, hyperparameter tuning through GridSearch underscores the potential of our models in accurately discerning malicious and benign network packets. The practical significance of our research extends to real-time intrusion detection, providing proactive cybersecurity measures against threats at both personal and infrastructure levels. Beyond detection, our proposed algorithms offer avenues for deterring and deceiving attackers, contributing to a deeper understanding of threat actors' motives and operations. The demonstrated accuracy and performance metrics, as well as comparative analyses with related works position our models as valuable assets in advancing the security of evolving IoT ecosystems. The findings pave the way for future developments in securing digital landscapes against a range of emerging threats.
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M.S.