Title
An Intelligent GWO-CNN-BiLSTM Framework for Intrusion Detection in IoT Networks Using Optimized Feature Selection
Authors
S.B Gopal, D. Nanthiya, Muthuraman Saminathan
Abstract
The fast growth of Internet of Things (IoT) technology has raised the possibility of cyber-attacks, such as Distributed Denial-of-Service (DDoS) and network intrusions. Conventional intrusion detection systems generally suffer from the difficulties in processing high-dimensional network traffic data, resulting in higher computing complexity, overfitting and worse detection accuracy. This paper presents an intelligent intrusion detection framework by integrating Grey Wolf Optimisation (GWO)-based feature selection with a hybrid Convolutional Neural Network and Bidirectional Long Short-Term Memory (CNN-BiLSTM) model to address these constraints. Therefore, the GWO method is used to choose the most important features by removing the redundant and unnecessary characteristics, thereby lowering the dimensionality and boosting the model efficiency. Then the optimised feature subsets are processed using the CNN-BiLSTM architecture to successfully understand the geographical and temporal attack patterns in IoT traffic data. The presented methodology is assessed on three benchmark datasets including NSL-KDD, UNSW-NB15 and CICIDS2017. Experimental findings show that the performance of intrusion detection is much improved following the feature optimisation. The suggested model for the NSL-KDD dataset obtained an accuracy of 97.4% with feature reduction from 41 to 15 characteristics. Likewise, the UNSW-NB15 dataset achieved 96.3% accuracy by lowering the features from 49 to 18. Also, the CICIDS2017 dataset achieved 98.1% accuracy after reducing the feature space from 78 to 24 characteristics. Moreover, the suggested technique lowered the false positive rates and reduced the training time by around 27% making it more appropriate for real time and resource limited IoT applications. The comparative study shows the efficacy, scalability and computing efficiency of the proposed GWO-CNN-BiLSTM intrusion detection architecture
Keywords
Internet of Things; Distributed Denial of Service; ANN; RNN; LSTM; GWO
Full Text
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