Constrained Twin Variational Auto-Encoder for Intrusion Detection in IoT Systems

TL;DR

This paper introduces CTVAE, a novel deep neural network architecture designed for IoT intrusion detection, which offers improved accuracy, lower resource consumption, and better data representation compared to existing methods.

Contribution

The paper proposes CTVAE, a constrained twin variational auto-encoder that enhances data separability and reduces resource requirements for IoT intrusion detection.

Findings

Boosts around 1% in detection accuracy and F-score.

Lower detection time than 2 microseconds.

Model size under 1 MB.

Abstract

Intrusion detection systems (IDSs) play a critical role in protecting billions of IoT devices from malicious attacks. However, the IDSs for IoT devices face inherent challenges of IoT systems, including the heterogeneity of IoT data/devices, the high dimensionality of training data, and the imbalanced data. Moreover, the deployment of IDSs on IoT systems is challenging, and sometimes impossible, due to the limited resources such as memory/storage and computing capability of typical IoT devices. To tackle these challenges, this article proposes a novel deep neural network/architecture called Constrained Twin Variational Auto-Encoder (CTVAE) that can feed classifiers of IDSs with more separable/distinguishable and lower-dimensional representation data. Additionally, in comparison to the state-of-the-art neural networks used in IDSs, CTVAE requires less memory/storage and computing power, hence making it more suitable for IoT IDS systems. Extensive experiments with the 11 most popular IoT botnet datasets show that CTVAE can boost around 1% in terms of accuracy and Fscore in detection attack compared to the state-of-the-art machine learning and representation learning methods, whilst the running time for attack detection is lower than 2E-6 seconds and the model size is lower than 1 MB. We also further investigate various characteristics of CTVAE in the latent space and in the reconstruction representation to demonstrate its efficacy compared with current well-known methods.