An efficient intrusion detection model based on hybridization of artificial bee colony and dragonfly algorithms for training multilayer perceptrons
One of the most persistent challenges concerning network security is to build a model capable of detecting intrusions in network systems. The issue has been extensively addressed in uncountable researches and using various techniques, of which a commonly used technique is that based on detecting int...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020
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| Online Access: | http://umpir.ump.edu.my/id/eprint/30046/ http://umpir.ump.edu.my/id/eprint/30046/1/An%20efficient%20intrusion%20detection%20model%20based%20on%20hybridization.pdf |
| _version_ | 1848823421865033728 |
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| author | Ghanem, Waheed Ali H. M. Aman, Jantan Ahmed Ghaleb, Sanaa Abduljabbar Naseer, Abdullah B. |
| author_facet | Ghanem, Waheed Ali H. M. Aman, Jantan Ahmed Ghaleb, Sanaa Abduljabbar Naseer, Abdullah B. |
| author_sort | Ghanem, Waheed Ali H. M. |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | One of the most persistent challenges concerning network security is to build a model capable of detecting intrusions in network systems. The issue has been extensively addressed in uncountable researches and using various techniques, of which a commonly used technique is that based on detecting intrusions in contrast to normal network traffic and the classification of network packets as either normal or abnormal. However, the problem of improving the accuracy and efficiency of classification models remains open and yet to be resolved. This study proposes a new binary classification model for intrusion detection, based on hybridization of Artificial Bee Colony algorithm (ABC) and Dragonfly algorithm (DA) for training an artificial neural network (ANN) in order to increase the classification accuracy rate for malicious and non-malicious traffic in networks. At first the model selects the suitable biases and weights utilizing a hybrid (ABC) and (DA). Next, the neural network is retrained using these ideal values in order for the intrusion detection model to be able to recognize new attacks. Ten other metaheuristic algorithms were adapted to train the neural network and their performances were compared with that of the proposed model. In addition, four types of intrusion detection evaluation datasets were applied to evaluate the proposed model in comparison to the others. The results of our experiments have demonstrated a significant improvement in inefficient network intrusion detection over other classification methods. |
| first_indexed | 2025-11-15T02:56:52Z |
| format | Article |
| id | ump-30046 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:56:52Z |
| publishDate | 2020 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-300462022-11-10T03:13:28Z http://umpir.ump.edu.my/id/eprint/30046/ An efficient intrusion detection model based on hybridization of artificial bee colony and dragonfly algorithms for training multilayer perceptrons Ghanem, Waheed Ali H. M. Aman, Jantan Ahmed Ghaleb, Sanaa Abduljabbar Naseer, Abdullah B. QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering One of the most persistent challenges concerning network security is to build a model capable of detecting intrusions in network systems. The issue has been extensively addressed in uncountable researches and using various techniques, of which a commonly used technique is that based on detecting intrusions in contrast to normal network traffic and the classification of network packets as either normal or abnormal. However, the problem of improving the accuracy and efficiency of classification models remains open and yet to be resolved. This study proposes a new binary classification model for intrusion detection, based on hybridization of Artificial Bee Colony algorithm (ABC) and Dragonfly algorithm (DA) for training an artificial neural network (ANN) in order to increase the classification accuracy rate for malicious and non-malicious traffic in networks. At first the model selects the suitable biases and weights utilizing a hybrid (ABC) and (DA). Next, the neural network is retrained using these ideal values in order for the intrusion detection model to be able to recognize new attacks. Ten other metaheuristic algorithms were adapted to train the neural network and their performances were compared with that of the proposed model. In addition, four types of intrusion detection evaluation datasets were applied to evaluate the proposed model in comparison to the others. The results of our experiments have demonstrated a significant improvement in inefficient network intrusion detection over other classification methods. IEEE 2020-07-15 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/30046/1/An%20efficient%20intrusion%20detection%20model%20based%20on%20hybridization.pdf Ghanem, Waheed Ali H. M. and Aman, Jantan and Ahmed Ghaleb, Sanaa Abduljabbar and Naseer, Abdullah B. (2020) An efficient intrusion detection model based on hybridization of artificial bee colony and dragonfly algorithms for training multilayer perceptrons. IEEE Access, 8 (9141282). pp. 130452-130475. ISSN 2169-3536. (Published) https://doi.org/10.1109/ACCESS.2020.3009533 https://doi.org/10.1109/ACCESS.2020.3009533 |
| spellingShingle | QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering Ghanem, Waheed Ali H. M. Aman, Jantan Ahmed Ghaleb, Sanaa Abduljabbar Naseer, Abdullah B. An efficient intrusion detection model based on hybridization of artificial bee colony and dragonfly algorithms for training multilayer perceptrons |
| title | An efficient intrusion detection model based on hybridization of artificial bee colony and dragonfly algorithms for training multilayer perceptrons |
| title_full | An efficient intrusion detection model based on hybridization of artificial bee colony and dragonfly algorithms for training multilayer perceptrons |
| title_fullStr | An efficient intrusion detection model based on hybridization of artificial bee colony and dragonfly algorithms for training multilayer perceptrons |
| title_full_unstemmed | An efficient intrusion detection model based on hybridization of artificial bee colony and dragonfly algorithms for training multilayer perceptrons |
| title_short | An efficient intrusion detection model based on hybridization of artificial bee colony and dragonfly algorithms for training multilayer perceptrons |
| title_sort | efficient intrusion detection model based on hybridization of artificial bee colony and dragonfly algorithms for training multilayer perceptrons |
| topic | QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/30046/ http://umpir.ump.edu.my/id/eprint/30046/ http://umpir.ump.edu.my/id/eprint/30046/ http://umpir.ump.edu.my/id/eprint/30046/1/An%20efficient%20intrusion%20detection%20model%20based%20on%20hybridization.pdf |