A Hybrid PCA-MAO Based LSTM Model for Intrusion Detection in IoT Environments

Abstract

With the rapid advances in Internet of Things (IoT) technologies, the number of smart objects connected to IoT networks is increasing day by day. Parallel to this exponential growth, attacks against IoT networks are also increasing rapidly. Various Intrusion Detection Systems (IDS) have been proposed by researchers to improve accuracy in detecting attacks with different behaviors and reduce intrusion detection time. This work presents a novel IDS based on the combination of the Principal Component Analysis and Mayfly Optimization methods (PCA-MAO) for dimensionality reduction, the Borderline Synthetic Minority Oversampling Technique (BSMOTE) for data balancing, and the Long Short-Term Memory (LSTM) method for classification. A new dataset was created by combining IoTID20, CIC-ToN-IoT and USB-IDS-1 datasets to be used in the performance test of the proposed model. Thus, the performance evaluation of the proposed model was performed for more attack types with different behaviors. As a result of classification using the proposed hybrid PCA-MAO based LSTM model, an accuracy of 99.51% was obtained. It has been observed that the proposed IDS provides superior intrusion detection performance for high-dimensional, complicated, and imbalanced data compared to classical machine learning (ML) methods.

Cyber Intrusion detection in Industry 4.0 Data using Machine Learning

“…This dataset stands out with its heterogeneous data sources, addressing a gap found in other datasets [30]. It further enhances its relevance with a vast collection of over 5 million samples, amalgamating both malicious and benign data [31]. 2) Rich Feature Set: A limitation evident in prior datasets is the paucity of features, especially in IoT-based IDS datasets [32].…”

“…Its data, derived from a testbed architecture, encompasses detailed audit traces from Linux operating systems, including hard disk, memory, and process logs [33]. Such granularity in data strengthens the dataset's utility for appraising intrusion detection mechanisms in IoT settings [31]. In summation, the ToN IoT dataset surpasses its predecessors by addressing their limitations and offering a plethora of data sources, an enriched feature set, unmatched scalability, and genuine real-world representations.…”

Cyber Intrusion detection in Industry 4.0 Data using Machine Learning

“…This dataset stands out with its heterogeneous data sources, addressing a gap found in other datasets [30]. It further enhances its relevance with a vast collection of over 5 million samples, amalgamating both malicious and benign data [31]. 2) Rich Feature Set: A limitation evident in prior datasets is the paucity of features, especially in IoT-based IDS datasets [32].…”

“…Its data, derived from a testbed architecture, encompasses detailed audit traces from Linux operating systems, including hard disk, memory, and process logs [33]. Such granularity in data strengthens the dataset's utility for appraising intrusion detection mechanisms in IoT settings [31]. In summation, the ToN IoT dataset surpasses its predecessors by addressing their limitations and offering a plethora of data sources, an enriched feature set, unmatched scalability, and genuine real-world representations.…”