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Article

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

Department of Computer Science, North Carolina A & T State University, Greensboro, NC 27411, USA
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Author to whom correspondence should be addressed.
Information 2020, 11(5), 243; https://doi.org/10.3390/info11050243
Received: 26 March 2020 / Revised: 23 April 2020 / Accepted: 28 April 2020 / Published: 1 May 2020
(This article belongs to the Special Issue Machine Learning for Cyber-Security)
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. View Full-Text
Keywords: intrusion detection system; long short-term memory; recurrent neural network; genetic algorithm intrusion detection system; long short-term memory; recurrent neural network; genetic algorithm
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MDPI and ACS Style

Muhuri, P.S.; Chatterjee, P.; Yuan, X.; Roy, K.; Esterline, A. Using a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) to Classify Network Attacks. Information 2020, 11, 243. https://doi.org/10.3390/info11050243

AMA Style

Muhuri PS, Chatterjee P, Yuan X, Roy K, Esterline A. Using a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) to Classify Network Attacks. Information. 2020; 11(5):243. https://doi.org/10.3390/info11050243

Chicago/Turabian Style

Muhuri, Pramita S., Prosenjit Chatterjee, Xiaohong Yuan, Kaushik Roy, and Albert Esterline. 2020. "Using a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) to Classify Network Attacks" Information 11, no. 5: 243. https://doi.org/10.3390/info11050243

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