A Study on Heuristic Algorithms Combined With LR on a DNN-Based IDS Model to Detect IoT Attacks

Tran Thi Thanh Thuy; Luong Duc Thuan; Nguyen Hong Duc; Hoang Trong Minh

doi:10.13164/mendel.2023.1.062

Tran Thi Thanh Thuy Faculty of Electronic Engineering 1, Posts and Telecommunication Institute of Technology, Hanoi, Vietnam
Luong Duc Thuan Faculty of Telecommunication 1, Posts and Telecommunication Institute of Technology, Hanoi, Vietnam
Nguyen Hong Duc Faculty of Telecommunication 1, Posts and Telecommunication Institute of Technology, Hanoi, Vietnam
Hoang Trong Minh Faculty of Electronic Engineering 1, Posts and Telecommunication Institute of Technology, Hanoi, Vietnam

DOI: https://doi.org/10.13164/mendel.2023.1.062

Keywords: Intrusion Detection System, Deep Neuron Network, Heuristic Algorithms, IoT-23 dataset

Abstract

Current security challenges are made more difficult by the complexity and difficulty of spotting cyberattacks due to the Internet of Things explosive growth in connected devices and apps. Therefore, various sophisticated attack detection techniques have been created to address these issues in recent years. Due to their effectiveness and scalability, machine learning-based intrusion detection systems (IDSs) have increased. However, several factors, such as the characteristics of the training dataset and the training model, affect how well these AI-based systems identify attacks. In particular, the heuristic algorithms (GA, PSO, CSO, FA) optimized by the logistic regression (LR) approach employ it to pick critical features of a dataset and deal with data imbalance problems. This study offers an intrusion detection system (IDS) based on a deep neural network and heuristic algorithms combined with LR to boost the accuracy of attack detections. Our proposed model has a high attack detection rate of up to 99% when testing on the IoT-23 dataset.

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