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   a new method for intrusion detection using genetic algorithm and neural network  
   
نویسنده hosseinzadehmoghadam mohammadreza ,mirabedini seyed javad ,banirostam toraj
منبع journal of advances in computer engineering and technology - 2017 - دوره : 3 - شماره : 4 - صفحه:213 -222
چکیده    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.
کلیدواژه intrusion detection system ,neural network ,genetic algorithm ,clustring and firewall
آدرس islamic azad university, central tehran branch, department of computer engineering, iran, islamic azad university, central tehran branch, department of computer engineering, iran, islamic azad university, central tehran branch, department of computer engineering, iran
پست الکترونیکی banirostam@iauctb.ac.ir
 
 

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