Citation:

Vikram Nattamai Sankaran, Dr. A. Punitha, Rakesh Thoppaen Suresh Babu, 2022. "Wireless Network Intrusion Detection: A Comprehensive Evaluation of Modified Catboost Classification Models" ESP Journal of Engineering & Technology Advancements  2(2): 35-43.

Abstract:

The increasing prevalence of wireless networks has made them a prime target for cyber threats and unauthorized access. Effective intrusion detection in such environments is crucial to maintaining network security and integrity. This study presents a novel approach to wireless network intrusion detection by leveraging a modified CatBoost algorithm, enhanced through Whale Optimization Algorithm (WOA)-based hyperparameter tuning. CatBoost, a gradient boosting framework, is adapted with modifications to better handle the unique challenges of intrusion detection, including class imbalance and high-dimensional data. The Whale Optimization Algorithm, inspired by the hunting behavior of whales, is employed to optimize CatBoost's hyperparameters, improving its performance and accuracy in detecting intrusions. The proposed method is evaluated on a real-world wireless network dataset, demonstrating superior detection capabilities compared to traditional approaches. The results indicate that the combination of modified CatBoost and WOA leads to a more robust and effective intrusion detection system, offering enhanced security for wireless networks.

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Keywords:

Wireless Network Intrusion Detection, CatBoost Classification Models, Gradient Boosting Algorithms, Modified CatBoost, Network Security, Intrusion Detection Systems (IDS), Machine Learning for Intrusion Detection, Feature Engineering, Categorical Feature Handling.