A Hybrid Intrusion Detection System Based on Scalable K-Means+ Random Forest and Deep Learning
Digital assets have come under various network security threats in the digital age. As a kind of security equipment to protect digital assets, intrusion detection system (IDS) is less efficient if the alert is not timely and IDS is useless if the accuracy cannot meet the requirements. Therefore, an...
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| Veröffentlicht in: | IEEE access Jg. 9; S. 75729 - 75740 |
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| Sprache: | Englisch |
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2021
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| Abstract | Digital assets have come under various network security threats in the digital age. As a kind of security equipment to protect digital assets, intrusion detection system (IDS) is less efficient if the alert is not timely and IDS is useless if the accuracy cannot meet the requirements. Therefore, an intrusion detection model that combines machine learning with deep learning is proposed in this paper. The model uses the k-means and the random forest (RF) algorithms for the binary classification, and distributed computing of these algorithms is implemented on the Spark platform to quickly classify normal events and attack events. Then, by using the convolutional neural network (CNN), long short-term memory (LSTM), and other deep learning algorithms, the events judged as abnormal are further classified into different attack types finally. At this stage, adaptive synthetic sampling (ADASYN) is adopted to solve the unbalanced dataset. The NSL-KDD and CIS-IDS2017 datasets are used to evaluate the performance of the proposed model. The experimental results show that the proposed model has better TPR for most of attack events, faster data preprocessing speed, and potentially less training time. In particular, the accuracy of multi-target classification can reach as high as 85.24% in the NSL-KDD dataset and 99.91% in the CIC-IDS2017 dataset. |
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| AbstractList | Digital assets have come under various network security threats in the digital age. As a kind of security equipment to protect digital assets, intrusion detection system (IDS) is less efficient if the alert is not timely and IDS is useless if the accuracy cannot meet the requirements. Therefore, an intrusion detection model that combines machine learning with deep learning is proposed in this paper. The model uses the k-means and the random forest (RF) algorithms for the binary classification, and distributed computing of these algorithms is implemented on the Spark platform to quickly classify normal events and attack events. Then, by using the convolutional neural network (CNN), long short-term memory (LSTM), and other deep learning algorithms, the events judged as abnormal are further classified into different attack types finally. At this stage, adaptive synthetic sampling (ADASYN) is adopted to solve the unbalanced dataset. The NSL-KDD and CIS-IDS2017 datasets are used to evaluate the performance of the proposed model. The experimental results show that the proposed model has better TPR for most of attack events, faster data preprocessing speed, and potentially less training time. In particular, the accuracy of multi-target classification can reach as high as 85.24% in the NSL-KDD dataset and 99.91% in the CIC-IDS2017 dataset. |
| Author | Liu, Chao Wang, Jialiang Gu, Zhaojun |
| Author_xml | – sequence: 1 givenname: Chao orcidid: 0000-0002-3490-0512 surname: Liu fullname: Liu, Chao email: liuc@cauc.edu.cn organization: College of Safety Science and Engineering, Civil Aviation University of China, Tianjin, China – sequence: 2 givenname: Zhaojun surname: Gu fullname: Gu, Zhaojun organization: Information Security Evaluation Center, Civil Aviation University of China, Tianjin, China – sequence: 3 givenname: Jialiang surname: Wang fullname: Wang, Jialiang organization: College of Computer Science and Technology, Civil Aviation University of China, Tianjin, China |
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| SubjectTerms | Accuracy Adaptive sampling Algorithms Artificial neural networks Classification Classification algorithms Computer networks Datasets Deep learning deep learning algorithm Distributed processing Feature extraction Hybrid systems Intrusion detection Intrusion detection system Intrusion detection systems k-means Machine learning machine learning algorithm Machine learning algorithms Radio frequency random forest Security Sparks |
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| Title | A Hybrid Intrusion Detection System Based on Scalable K-Means+ Random Forest and Deep Learning |
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