An Intelligent Intrusion Detection System for Internet of Things Using Convolutional Neural Networks and Bidirectional LSTM

International Journal of Computer Science and Technology | Vol. 1, No. 9, September 2020 | pp. 65–72

Research Article

Title: An Intelligent Intrusion Detection System for Internet of Things Using Convolutional Neural Networks and Bidirectional LSTM

Names of Authors: Mehmet Yilmaz¹, Ayse Kaya², and Emre Demir³

Authors’ Affiliations:
¹Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
²Department of Artificial Intelligence and Data Engineering, Istanbul Technical University, Istanbul, Turkey
³Department of Cyber Security, Bilkent University, Ankara, Turkey

Abstract: The exponential expansion of Internet of Things (IoT) ecosystems has introduced profound security vulnerabilities due to heterogeneous device hardware, weak default credentials, and lack of standardized firmware security protocols. IoT networks are exceptionally susceptible to botnet infections, distributed scanning, and man-in-the-middle exploits. Conventional signature-based intrusion detection systems fail to identify novel, polymorphic zero-day attacks targeting smart environments. This paper proposes a robust, hybrid deep learning intrusion detection system (IDS) combining 1D Convolutional Neural Networks (CNN) for spatial feature extraction and Bidirectional Long Short-Term Memory (BiLSTM) networks for capturing temporal sequential dependencies in network traffic. The network model processes raw packet streams categorized from benchmark IoT security datasets, including BoT-IoT and TON_IoT. The CNN layers automatically extract salient local correlations from traffic features, while the stacked BiLSTM layers analyze directional context across packet time sequences. Experimental evaluations demonstrate that the proposed CNN-BiLSTM hybrid architecture achieves an exceptional classification accuracy of 99.2%, precision of 98.9%, and recall of 99.1% across multi-class attack categories such as DDoS, data theft, and reconnaissance. Furthermore, the false alarm rate is suppressed to 0.008, confirming high operational reliability. The lightweight deployment analysis indicates that the model executes inference efficiently, making it well-suited for integration into resource-constrained edge gateways protecting smart home and industrial IoT installations.

Keywords: Intrusion detection system, Internet of things, Convolutional neural network, Bidirectional LSTM, Deep learning, Network security

Manuscript Timeline: Received: June 10, 2020; Revised: July 15, 2020; Accepted: August 12, 2020; Published: September 1, 2020

Citation: Yilmaz, M., Kaya, A., & Demir, E. (2020). An intelligent intrusion detection system for internet of things using convolutional neural networks and bidirectional LSTM. International Journal of Computer Science and Technology, 1(9), 65–72. DOI: 10.46882/2020/IJCST/000009

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