Using a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) to Classify Network Attacks
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<b>. </b>In t...
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MDPI AG
2020-05-01
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| Online Access: | https://www.mdpi.com/2078-2489/11/5/243 |
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| author | Pramita Sree Muhuri Prosenjit Chatterjee Xiaohong Yuan Kaushik Roy Albert Esterline |
| author_facet | Pramita Sree Muhuri Prosenjit Chatterjee Xiaohong Yuan Kaushik Roy Albert Esterline |
| author_sort | Pramita Sree Muhuri |
| collection | DOAJ |
| description | 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<b>. </b>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. |
| first_indexed | 2024-03-10T20:07:43Z |
| format | Article |
| id | doaj.art-a72509cfe4004ba4be23307870891b0b |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-10T20:07:43Z |
| publishDate | 2020-05-01 |
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| record_format | Article |
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| spelling | doaj.art-a72509cfe4004ba4be23307870891b0b2023-11-19T23:12:16ZengMDPI AGInformation2078-24892020-05-0111524310.3390/info11050243Using a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) to Classify Network AttacksPramita Sree Muhuri0Prosenjit Chatterjee1Xiaohong Yuan2Kaushik Roy3Albert Esterline4Department of Computer Science, North Carolina A & T State University, Greensboro, NC 27411, USADepartment of Computer Science, North Carolina A & T State University, Greensboro, NC 27411, USADepartment of Computer Science, North Carolina A & T State University, Greensboro, NC 27411, USADepartment of Computer Science, North Carolina A & T State University, Greensboro, NC 27411, USADepartment of Computer Science, North Carolina A & T State University, Greensboro, NC 27411, USAAn 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<b>. </b>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.https://www.mdpi.com/2078-2489/11/5/243intrusion detection systemlong short-term memoryrecurrent neural networkgenetic algorithm |
| spellingShingle | Pramita Sree Muhuri Prosenjit Chatterjee Xiaohong Yuan Kaushik Roy Albert Esterline Using a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) to Classify Network Attacks Information intrusion detection system long short-term memory recurrent neural network genetic algorithm |
| title | Using a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) to Classify Network Attacks |
| title_full | Using a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) to Classify Network Attacks |
| title_fullStr | Using a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) to Classify Network Attacks |
| title_full_unstemmed | Using a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) to Classify Network Attacks |
| title_short | Using a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) to Classify Network Attacks |
| title_sort | using a long short term memory recurrent neural network lstm rnn to classify network attacks |
| topic | intrusion detection system long short-term memory recurrent neural network genetic algorithm |
| url | https://www.mdpi.com/2078-2489/11/5/243 |
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