Using a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) to Classify Network Attacks

Information

Prosenjit Chatterjee¹  Albert Esterline¹  Kaushik Roy¹  Xiaohong Yuan¹  PramitaSree Muhuri¹ 

[1] Department of Computer Science, North Carolina A & T State University, Greensboro, NC 27411, USA;

关键词: intrusion detection system;    long short-term memory;    recurrent neural network;    genetic algorithm;   

DOI  :  10.3390/info11050243

来源: DOAJ

【 摘 要 】

An intrusion detection system (IDS) identifies whether the network traffic behavior is normal or abnormal or identifies the attack types. Recently, deep learning has emerged as a successful approach in IDSs, having a high accuracy rate with its distinctive learning mechanism. In this research, we developed a new method for intrusion detection to classify the NSL-KDD dataset by combining a genetic algorithm (GA) for optimal feature selection and long short-term memory (LSTM) with a recurrent neural network (RNN). We found that using LSTM-RNN classifiers with the optimal feature set improves intrusion detection. The performance of the IDS was analyzed by calculating the accuracy, recall, precision, f-score, and confusion matrix. The NSL-KDD dataset was used to analyze the performances of the classifiers. An LSTM-RNN was used to classify the NSL-KDD datasets into binary (normal and abnormal) and multi-class (Normal, DoS, Probing, U2R, and R2L) sets. The results indicate that applying the GA increases the classification accuracy of LSTM-RNN in both binary and multi-class classification. The results of the LSTM-RNN classifier were also compared with the results using a support vector machine (SVM) and random forest (RF). For multi-class classification, the classification accuracy of LSTM-RNN with the GA model is much higher than SVM and RF. For binary classification, the classification accuracy of LSTM-RNN is similar to that of RF and higher than that of SVM.

【 授权许可】

Unknown