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A New Method for Intrusion Detection Using Genetic Algorithm and Neural network | ||
| Journal of Advances in Computer Engineering and Technology | ||
| مقاله 5، دوره 3، شماره 4 - شماره پیاپی 12، بهمن 2017، صفحه 213-222 اصل مقاله (371.14 K) | ||
| نوع مقاله: Original Research Paper | ||
| نویسندگان | ||
mohammadreza hosseinzadehmoghadam  ؛ seyed javad mirabedini؛ toraj banirostam
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| Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran. | ||
| چکیده | ||
| Abstract— In order to provide complete security in a computer system and to prevent intrusion, intrusion detection systems (IDS) are required to detect if an attacker crosses the firewall, antivirus, and other security devices. Data and options to deal with it. In this paper, we are trying to provide a model for combining types of attacks on public data using combined methods of genetic algorithm and neural network. The goal is to make the designed model act as a measure of system attack and combine optimization algorithms to create the ultimate accuracy and reliability for the proposed model and reduce the error rate. To do this, we used a feedback neural network, and by examining the worker, it can be argued that this research with the new approach reduces errors in the classification.with the rapid development of communication and information technology and its applications, especially in computer networks, there is a new competition in information security and network security. | ||
| کلیدواژهها | ||
| Intrusion Detection System؛ Neural Network؛ Genetic algorithm؛ Clustring and firewall | ||
| مراجع | ||
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