An Efficient Intrusion Detection Model Based on Hybridization of Artificial Bee Colony and Dragonfly Algorithms for Training Multilayer Perceptrons
| Autor: | Waheed Ali H. M. Ghanem, Abdullah B. Nasser, Aman Jantan, Sanaa Abduljabbar Ahmed Ghaleb |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
General Computer Science
Artificial neural network Network security business.industry Computer science Network packet metaheuristic algorithm (MA) General Engineering Training (meteorology) artificial bee colony algorithm (ABC) Pattern recognition dragonfly algorithm (DA) Intrusion detection system multilayer perceptron (MLP) Perceptron Artificial bee colony algorithm Binary classification General Materials Science Artificial intelligence lcsh:Electrical engineering. Electronics. Nuclear engineering business lcsh:TK1-9971 Intrusion detection system (IDS) |
| Zdroj: | IEEE Access, Vol 8, Pp 130452-130475 (2020) |
| ISSN: | 2169-3536 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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