Hybrid Optimization Enabled Robust CNN-LSTM Technique for Network Intrusion Detection
Nowadays, computer networks and the Internet are unprotected from many security threats. Introducing adaptive and flexible security-related techniques is challenging because of the new types of frequently occurring attacks. An intrusion detection system (IDS) is a security device similar to other me...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9796521/ |
| _version_ | 1811239031730601984 |
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| author | Bhushan Deore Surendra Bhosale |
| author_facet | Bhushan Deore Surendra Bhosale |
| author_sort | Bhushan Deore |
| collection | DOAJ |
| description | Nowadays, computer networks and the Internet are unprotected from many security threats. Introducing adaptive and flexible security-related techniques is challenging because of the new types of frequently occurring attacks. An intrusion detection system (IDS) is a security device similar to other measures, including firewalls, antivirus software, and access control models devised to strengthen communication and information security. Network intrusion detection system (NIDS) plays a vital function in defending computer networks and systems. However, several issues concerning the sustainability and feasibility of existing techniques are faced with recent networks. These concerns are directly related to the rising levels of necessary human interactions and reducing the level of detection accuracy. Several approaches are designed to detect and manage various security threats in a network. This study uses Chimp Chicken Swarm Optimization-based Deep Long Short-Term Memory (ChCSO-driven Deep LSTM) for the intrusion detection process. A CNN feature extraction process is necessary for effective intrusion detection. Here, the Deep LSTM is applied for detecting network intrusion, and the Deep LSTM is trained using a designed optimization technique to enhance the detection performance. |
| first_indexed | 2024-04-12T12:52:36Z |
| format | Article |
| id | doaj.art-80c77f33d74e4b51be2c02c2e386f48c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T12:52:36Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-80c77f33d74e4b51be2c02c2e386f48c2022-12-22T03:32:24ZengIEEEIEEE Access2169-35362022-01-0110656116562210.1109/ACCESS.2022.31832139796521Hybrid Optimization Enabled Robust CNN-LSTM Technique for Network Intrusion DetectionBhushan Deore0https://orcid.org/0000-0003-3602-711XSurendra Bhosale1Department of Electrical Engineering, Veermata Jijabai Technological Institute, Mumbai, IndiaDepartment of Electrical Engineering, Veermata Jijabai Technological Institute, Mumbai, IndiaNowadays, computer networks and the Internet are unprotected from many security threats. Introducing adaptive and flexible security-related techniques is challenging because of the new types of frequently occurring attacks. An intrusion detection system (IDS) is a security device similar to other measures, including firewalls, antivirus software, and access control models devised to strengthen communication and information security. Network intrusion detection system (NIDS) plays a vital function in defending computer networks and systems. However, several issues concerning the sustainability and feasibility of existing techniques are faced with recent networks. These concerns are directly related to the rising levels of necessary human interactions and reducing the level of detection accuracy. Several approaches are designed to detect and manage various security threats in a network. This study uses Chimp Chicken Swarm Optimization-based Deep Long Short-Term Memory (ChCSO-driven Deep LSTM) for the intrusion detection process. A CNN feature extraction process is necessary for effective intrusion detection. Here, the Deep LSTM is applied for detecting network intrusion, and the Deep LSTM is trained using a designed optimization technique to enhance the detection performance.https://ieeexplore.ieee.org/document/9796521/Intrusion detectiondeep long short-term memorychimp optimization algorithmchicken swarm optimization algorithmconvolutional neural network features |
| spellingShingle | Bhushan Deore Surendra Bhosale Hybrid Optimization Enabled Robust CNN-LSTM Technique for Network Intrusion Detection IEEE Access Intrusion detection deep long short-term memory chimp optimization algorithm chicken swarm optimization algorithm convolutional neural network features |
| title | Hybrid Optimization Enabled Robust CNN-LSTM Technique for Network Intrusion Detection |
| title_full | Hybrid Optimization Enabled Robust CNN-LSTM Technique for Network Intrusion Detection |
| title_fullStr | Hybrid Optimization Enabled Robust CNN-LSTM Technique for Network Intrusion Detection |
| title_full_unstemmed | Hybrid Optimization Enabled Robust CNN-LSTM Technique for Network Intrusion Detection |
| title_short | Hybrid Optimization Enabled Robust CNN-LSTM Technique for Network Intrusion Detection |
| title_sort | hybrid optimization enabled robust cnn lstm technique for network intrusion detection |
| topic | Intrusion detection deep long short-term memory chimp optimization algorithm chicken swarm optimization algorithm convolutional neural network features |
| url | https://ieeexplore.ieee.org/document/9796521/ |
| work_keys_str_mv | AT bhushandeore hybridoptimizationenabledrobustcnnlstmtechniquefornetworkintrusiondetection AT surendrabhosale hybridoptimizationenabledrobustcnnlstmtechniquefornetworkintrusiondetection |