Multiple-Input Auto-Encoder Guided Feature Selection for IoT Intrusion Detection Systems

TL;DR

This paper introduces MIAE and MIAEFS, neural network models that process heterogeneous IoT data for intrusion detection, improving accuracy and efficiency by selecting relevant features and reducing redundancy.

Contribution

The paper proposes a novel multi-input auto-encoder architecture with embedded feature selection for IoT intrusion detection, enhancing detection accuracy and reducing model complexity.

Findings

MIAE and MIAEFS outperform traditional methods on three datasets.

Achieve 96.5% accuracy in detecting sophisticated attacks.

Model detection time is approximately 1.7 microseconds.

Abstract

While intrusion detection systems (IDSs) benefit from the diversity and generalization of IoT data features, the data diversity (e.g., the heterogeneity and high dimensions of data) also makes it difficult to train effective machine learning models in IoT IDSs. This also leads to potentially redundant/noisy features that may decrease the accuracy of the detection engine in IDSs. This paper first introduces a novel neural network architecture called Multiple-Input Auto-Encoder (MIAE). MIAE consists of multiple sub-encoders that can process inputs from different sources with different characteristics. The MIAE model is trained in an unsupervised learning mode to transform the heterogeneous inputs into lower-dimensional representation, which helps classifiers distinguish between normal behaviour and different types of attacks. To distil and retain more relevant features but remove less important/redundant ones during the training process, we further design and embed a feature selection layer right after the representation layer of MIAE resulting in a new model called MIAEFS. This layer learns the importance of features in the representation vector, facilitating the selection of informative features from the representation vector. The results on three IDS datasets, i.e., NSLKDD, UNSW-NB15, and IDS2017, show the superior performance of MIAE and MIAEFS compared to other methods, e.g., conventional classifiers, dimensionality reduction models, unsupervised representation learning methods with different input dimensions, and unsupervised feature selection models. Moreover, MIAE and MIAEFS combined with the Random Forest (RF) classifier achieve accuracy of 96.5% in detecting sophisticated attacks, e.g., Slowloris. The average running time for detecting an attack sample using RF with the representation of MIAE and MIAEFS is approximate 1.7E-6 seconds, whilst the model size is lower than 1 MB.