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All Journal International Journal of Electrical and Computer Engineering
Ahmed Gafar
Imam Abdulrahman Bin Faisal University
Computer Science & IT Electrical & Electronics Engineering

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Journal : International Journal of Electrical and Computer Engineering

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An ensemble model to detect packet length covert channels Muawia A. Elsadig; Ahmed Gafar
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5296-5304

Abstract

Covert channel techniques have enriched the way to commit dangerous and unwatched attacks. They exploit ways that are not intended to convey information; therefore, traditional security measures cannot detect them. One class of covert channels that difficult to detect, mitigate, or eliminate is packet length covert channels. This class of covert channels takes advantage of packet length variations to convey covert information. Numerous research articles reflect the useful use of machine learning (ML) classification approaches to discover covert channels. Therefore, this study presented an efficient ensemble classification model to detect such types of attacks. The ensemble model consists of five machine learning algorithms representing the base classifiers. The base classifiers include naive Bayes (NB), decision tree (DT), support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF). Whereas, the logistic regression (LR) classifier was employed to aggregate the outputs of the base classifiers and thus to generate the ensemble classifier output. The results showed a good performance of our proposed ensemble classifier. It beats all single classification algorithms, with a 99.3% accuracy rate and negligible classification errors.
Co-Authors Muawia A. Elsadig