OOA-modified Bi-LSTM network: An effective intrusion detection framework for IoT systems

Siva Surya Narayana Chintapalli; Satya Prakash Singh; Jaroslav Frnda; Parameshachari Bidare Divakarachari; Vijaya Lakshmi Sarraju; Przemysław Falkowski-Gilski

doi:10.1016/j.heliyon.2024.e29410

OOA-modified Bi-LSTM network: An effective intrusion detection framework for IoT systems

Heliyon. 2024 Apr 13;10(8):e29410. doi: 10.1016/j.heliyon.2024.e29410. eCollection 2024 Apr 30.

Authors

Siva Surya Narayana Chintapalli¹, Satya Prakash Singh¹, Jaroslav Frnda^{2

3}, Parameshachari Bidare Divakarachari⁴, Vijaya Lakshmi Sarraju¹, Przemysław Falkowski-Gilski⁵

Affiliations

¹ Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, India.
² Department of Quantitative Methods and Economic Informatics, Faculty of Operation and Economics of Transport and Communications, University of Zilina, 01026, Zilina, Slovakia.
³ Department of Telecommunications, Faculty of Electrical Engineering and Computer Science, VSB Technical University of Ostrava, 70800, Ostrava, Czech Republic.
⁴ Department of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology, Bengaluru 560064, Visvesvaraya Technological University, Belagavi, India.
⁵ Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Narutowicza 11/12, 80-233, Gdansk, Poland.

Abstract

Currently, the Internet of Things (IoT) generates a huge amount of traffic data in communication and information technology. The diversification and integration of IoT applications and terminals make IoT vulnerable to intrusion attacks. Therefore, it is necessary to develop an efficient Intrusion Detection System (IDS) that guarantees the reliability, integrity, and security of IoT systems. The detection of intrusion is considered a challenging task because of inappropriate features existing in the input data and the slow training process. In order to address these issues, an effective meta heuristic based feature selection and deep learning techniques are developed for enhancing the IDS. The Osprey Optimization Algorithm (OOA) based feature selection is proposed for selecting the highly informative features from the input which leads to an effective differentiation among the normal and attack traffic of network. Moreover, the traditional sigmoid and tangent activation functions are replaced with the Exponential Linear Unit (ELU) activation function to propose the modified Bi-directional Long Short Term Memory (Bi-LSTM). The modified Bi-LSTM is used for classifying the types of intrusion attacks. The ELU activation function makes gradients extremely large during back-propagation and leads to faster learning. This research is analysed in three different datasets such as N-BaIoT, Canadian Institute for Cybersecurity Intrusion Detection Dataset 2017 (CICIDS-2017), and ToN-IoT datasets. The empirical investigation states that the proposed framework obtains impressive detection accuracy of 99.98 %, 99.97 % and 99.88 % on the N-BaIoT, CICIDS-2017, and ToN-IoT datasets, respectively. Compared to peer frameworks, this framework obtains high detection accuracy with better interpretability and reduced processing time.

Keywords: Bi-directional long short-term memory network; Exponential linear unit activation function; Internet of things; Intrusion detection system; Osprey optimization algorithm.