An Intrusion Detection System Based on a Simplified Residual Network

An Intrusion Detection Method for Industrial Control System Based on Machine Learning

The integration of communication networks and the internet of industrial control in Industrial Control System (ICS) increases their vulnerability to cyber attacks, causing devastating outcomes. Traditional Intrusion Detection Systems (IDS) largely rely on predefined models and are trained mostly on specific cyber attacks, which means the traditional IDS cannot cope with unknown attacks. Additionally, most IDS do not consider the imbalanced nature of ICS datasets, thus suffering from low accuracy and high False Positive Rates when being put to use. In this paper, we propose the NCO–double-layer DIFF_RF–OPFYTHON intrusion detection method for ICS, which consists of NCO modules, double-layer DIFF_RF modules, and OPFYTHON modules. Detected traffic will be divided into three categories by the double-layer DIFF_RF module: known attacks, unknown attacks, and normal traffic. Then, the known attacks will be classified into specific attacks by the OPFYTHON module according to the feature of attack traffic. Finally, we use the NCO module to improve the model input and enhance the accuracy of the model. The results show that the proposed method outperforms traditional intrusion detection methods, such as XGboost and SVM. The detection of unknown attacks is also considerable. The accuracy of the dataset used in this paper reaches 98.13%. The detection rates for unknown attacks and known attacks reach 98.21% and 95.1%, respectively. Moreover, the method we proposed has achieved suitable results on other public datasets.

Network Intrusion Detection Technology Based on Convolutional Neural Network and BiGRU

To solve the problem of low accuracy and high false-alarm rate of existing intrusion detection models for multiple classifications of intrusion behaviors, a network intrusion detection model incorporating convolutional neural network and bidirectional gated recurrent unit is proposed. To solve the problems of many dimensions of features and imbalance of positive and negative samples in the original traffic data, sampling processing is performed with the help of a hybrid sampling algorithm combining ADASYN and RENN, and feature selection is performed by combining random forest algorithm and Pearson correlation analysis; after that, spatial features are extracted by the convolutional neural network, and further features are extracted by incorporating average pooling and max pooling, and then BiGRU is used to extracts long-distance dependent information features to achieve comprehensive and effective feature learning. Finally, the Softmax function is used for classification. In this paper, the proposed model is evaluated on the UNSW_NB15, NSL-KDD, and CIC-IDS2017 data sets with an accuracy of 85.55%, 99.81%, and 99.70%, which is 1.25%, 0.59%, and 0.27% better than the same type model of CNN-GRU.

Network Intrusion Detection Model Based on CNN and GRU

A network intrusion detection model that fuses a convolutional neural network and a gated recurrent unit is proposed to address the problems associated with the low accuracy of existing intrusion detection models for the multiple classification of intrusions and low accuracy of class imbalance data detection. In this model, a hybrid sampling algorithm combining Adaptive Synthetic Sampling (ADASYN) and Repeated Edited nearest neighbors (RENN) is used for sample processing to solve the problem of positive and negative sample imbalance in the original dataset. The feature selection is carried out by combining Random Forest algorithm and Pearson correlation analysis to solve the problem of feature redundancy. Then, the spatial features are extracted by using a convolutional neural network, and further extracted by fusing Averagepooling and Maxpooling, using attention mechanism to assign different weights to the features, thus reducing the overhead and improving the model performance. At the same time, a Gated Recurrent Unit (GRU) is used to extract the long-distance dependent information features to achieve comprehensive and effective feature learning. Finally, a softmax function is used for classification. The proposed intrusion detection model is evaluated based on the UNSW_NB15, NSL-KDD, and CIC-IDS2017 datasets, and the experimental results show that the classification accuracy reaches 86.25%, 99.69%, 99.65%, which are 1.95%, 0.47% and 0.12% higher than that of the same type of CNN-GRU, and can solve the problems of low classification accuracy and class imbalance well.