CNN-BiLSTM Hybrid Model for Network Anomaly Detection in Internet of Things

Omarov B. Auelbekov O. Suliman A. Zhaxanova A.
2023 Science and Information Organization

International Journal of Advanced Computer Science and Applications
2023 #14 Issue 3 436 - 444 pp.

Anomaly detection in internet of things network traffic is a critical aspect of intrusion and attack detection, in which a deviation from typical behavior signals the existence of malicious or inadvertent assaults, faults, flaws, and other issues. The necessity to examine a large number of security events to identify anomalous behavior of smart devices adds to the urgency of addressing the challenge of picking machine-learning and deep learning models for identifying anomalies in network traffic. For the challenge of binary data categorization, a software implementation of an intrusion detection system based on supervised-learning algorithms has been completed. The UNSWNB15 open dataset, which contains 2,540,044 records - vectors of TCP/IP network connection signals and their associated class labels are used to train and test the system. This research compares different machine-learning models and proposes CNNBiLSTM hybrid model for IoT network intrusion detection. The metrics for measuring the quality of classification and the running duration of algorithms for different ratios of train and test samples are the result of the built framework testing

anomaly attack , internet of things , IoT , machine learning , network anomalies , network security , supervised learning , UNSW-NB15

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Al-Farabi Kazakh National University, Almaty, Kazakhstan
Institute Information and Computational Technologies CS MES RK, Almaty, Kazakhstan
INTI International University, Kuala Lumpur, Malaysia
M. Auezov South Kazakhstan University, Shymkent, Kazakhstan

Al-Farabi Kazakh National University
Institute Information and Computational Technologies CS MES RK
INTI International University
M. Auezov South Kazakhstan University

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