Enhancing IoT Security: A Study on Hybrid Intrusion Detection Methods
Rabea M. Ali, Mansi Ram Baheti · 2024
This electronic document is a “live” template and already defines the components of your paper [title, text, heads, etc.] in its style sheet. *CRITICAL: Do Not Use Symbols, Special Characters, Footnotes, or Math in Paper Title or Abstract. (Abstract) The rapid proliferation of Internet of Things (IoT) devices has led to increased vulnerability to cyber intrusion threats like malware and denial-of-service attacks, which can severely impact system availability, reliability, and trustworthiness. Existing intrusion detection systems often rely on single environments for training models, limiting their real-world versatility against evolving attacks spanning diverse systems. To address these challenges, this study proposes an integrated deep learning approach that combines heterogeneous datasets using representation learning based on stacked convolutional autoencoders and distribution alignment. This approach aims to improve model generalization and enable the detection of threats across various IoT environments. The proposed method employs hybrid convolutional neural networks (CNN) and long short-term memory (LSTM) networks to perform robust binary and multi-class classification. By analyzing spatial context and sequential traffic dynamics, the model can distinguish normal functionality from different attack types. Extensive evaluation on the NSL-KDD and SDN-5-IoT intrusion detection benchmarks demonstrates over 99% accuracy in detecting both known and zero-day attacks. Comparisons with standalone deep architectures indicate consistent performance gains from the synergistic fusion of spatial and temporal modeling. The proposed approach advances adaptive threat intelligence for securing real-world IoT ecosystems against evolving cyber threats. The integration of heterogeneous datasets and the combination of CNN and LSTM architectures enable the model to learn more generalized representations of network traffic patterns, enhancing its ability to detect intrusions across diverse IoT systems. This research contributes to the development of robust and versatile intrusion detection systems that can adapt to the ever-changing landscape of cyber threats in the IoT domain, ultimately improving the security and reliability of connected devices and networks.