Abstract
Deep learning has become a research hotspot in the field of network intrusion detection. In order to further improve the detection accuracy and performance, we proposed an intrusion detection model based on improved deep belief network (DBN). Traditional neural network training methods, like Back Propagation (BP), start to train a model with preset parameters such as the randomly initialized weights and thresholds, which may bring some issues, e.g., attracting the model to the local optimal solutions, or requiring a long training period. We use the Kernel-based Extreme Learning Machine (KELM) with the supervised learning ability to replace the BP algorithm in DBN in a bid to ameliorate the situation. Considering the problem of poor classification performance usually caused by randomly initializing kernel parameters with KELM, an enhanced grey wolf optimizer (EGWO) is designed to optimize the parameters of KELM. In order to improve the search ability and optimization ability of the traditional grey wolf optimizer algorithm, a novel optimization strategy combining the inner and outer hunting is introduced. Experiments on KDDCup99, NSL-KDD, UNSW-NB15 and CICIDS2017 datasets show that the proposed DBN-EGWO-KELM algorithm has greater advantages in terms of its accuracy, precision, true positive rate, false positive rate and other evaluation indices compared with BP, RBF, SVM, KELM, LIBSVM, CNN, DBN-KELM and other intrusion detection models, and can effectively meet the requirements of intrusion detection of complex networks.
Highlights
With the rapid development of network technologies such as 5G [1], cloud computing [2], and the Internet of Things [3], the massive amount of data generated by the network has brought huge difficulties and challenges to network security, a research topic which has attracted more and more attention
In order to solve the above problems, in view of the advantages of deep neural networks in the field of intrusion detection, this paper proposes an intrusion detection model, namely deep belief network based on enhanced grey wolf optimizer and improved kernel based extreme learning machine (DBN-EGWO-Kernel-based Extreme Learning Machine (KELM))
The Back Propagation (BP) model, DBN model, DBN-KELM model, classic machine learning model, Multi-classifier LIBSVM, and convolutional neural network (CNN) are compared with the DBN-EGWO-KELM model we proposed to compare and verify the superior performance of this algorithm
Summary
With the rapid development of network technologies such as 5G [1], cloud computing [2], and the Internet of Things [3], the massive amount of data generated by the network has brought huge difficulties and challenges to network security, a research topic which has attracted more and more attention. Nie et al [15] established a deep learning model based on convolutional neural network for Internet of Things (IoT) security issues, and designed a data-driven intrusion detection system. This method has higher detection rate and lower false alarm rate, but the learning rate is slow. Zhu et al [17] designed a multi-task LSTM neural network intrusion detection system based on the vulnerability of the Internet of Vehicles, and detected abnormal behavior from two dimensions, i.e., the time and the data This model improved the real-time performance of detection. Experimental results on different datasets show that the DBNEGWO-KELM model can effectively shorten the training and detection time, and significantly improve the classification accuracy, precision, and true positive rate
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