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...

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Veröffentlicht in:IEEE access Jg. 10; S. 65611 - 65622
Hauptverfasser: Deore, Bhushan, Bhosale, Surendra
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Access control
Anti-virus software
chicken swarm optimization algorithm
chimp optimization algorithm
Computational modeling
Computer networks
convolutional neural network features
Cybersecurity
Deep learning
deep long short-term memory
Feature extraction
Intrusion detection
Intrusion detection systems
Network intrusion detection
Optimization
Optimization techniques
Security
Training
ISSN:2169-3536, 2169-3536
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Zusammenfassung: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.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3183213