طراحی یک سیستم تشخیص نفوذ به کمک روش های مبتنی بر سیستم های هوشمند

ﺑﺸﺮ در ﺣﺎل ﮔﺬران ﺑﺮهه ای از ﺗﺎرﯾﺦ ﺧﻮد اﺳﺖ ﮐﻪ ﺳﯿﺴﺘﻢهای ﮐﺎﻣﭙﯿﻮﺗﺮی ﺑﻪ ﺑﺨش ﺟﺪاﯾﯽ ﻧﺎﭘﺬﯾﺮ ازﺣﯿﺎت وی ﺑﺪل ﺷﺪه اﺳﺖ. ﺳﯿﺴﺘﻢهایﮐﺎﻣﭙﯿﻮﺗﺮی و ﺑﻪﻃﻮر ﺧﺎصﺷﺒکه های ﮐﺎﻣﭙﯿﻮﺗﺮی در ﺑﺴﯿﺎری از ﺟﻨﺒﻪ های زﻧﺪگی اﻣﺮوزی ﻧﻘﺶﺗﻌﯿﯿﻦﮐﻨﻨﺪه ای داﺷﺘﻪ و ﺑﻪ همین دﻟﯿﻞ اﻣﻨﯿﺖ آن ها ﺑﺴﯿﺎر ﻣﻮرد ﺗﻮﺟﻪ اﺳﺖ. ﺑﺮای ﭘﻮﺷﺶ اﻣﻨﯿﺖ آن ها و در اﺻﻄﻼح ﺗﺸﺨﯿﺺ ﻧﻔﻮذ در شبکه های ﮐﺎﻣﭙﯿﻮﺗﺮی روش های زﯾﺎدی از ﺟﻤﻠﻪ ﺷبکه های ﻋﺼﺒﯽ ﻣﺼﻨﻮعی ﭘﯿﺸﻨهاد ﺷﺪه اﺳﺖ، اﻣﺎ یکی از ﭼﺎﻟﺶ های ﺷﺒکه های ﻋﺼﺒﯽ اﻓﺘﺎدن در دام بهینه های ﻣﺤلی اﺳﺖ. از دﻻﯾﻞ ﺑﺮوز اﯾﻦ ﻣﺸکل اﺳﺘﻔﺎده ازروش های مبتنی ﺑﺮﮐﺎهش ﺷﯿﺐ در ﻓﺮآﯾﻨﺪ آﻣﻮزش اﺳﺖ ﮐﻪ در ﻧﺘﯿﺠﻪ ﺑﺮای ﺣﻞ اﯾﻦ ﭼﺎﻟﺶ از روش های بهینهﺳﺎزی ﺑﺴﯿﺎری اﺳﺘﻔﺎده میﺷﻮد. در اﯾﻦ ﻣﻘﺎﻟﻪ سعی ﺷﺪه اﺳﺖ ﺑﺎ اﺳﺘﻔﺎده از اﻟگورﯾﺘﻢ رﻗﺎﺑﺖ اﺳﺘﻌﻤﺎری ﯾک ﺷﺒکه ﻋﺼﺒﯽ ﭘﺮﺳﭙﺘﺮون ﭼﻨﺪ ﻻﯾﻪ را آﻣﻮزش داده و ﺑﻪ ﻣﻨﻈﻮر ﺑﺮرسی ﮐﺎراﯾﯽ آن، روش ﭘﯿﺸﻨهادی ﺑﺎ دو روش دیگر ﻣﻘﺎﯾﺴﻪ ﺷﺪه اﺳﺖ. ﻧﺘﺎﯾﺞ روش ﭘﯿﺸﻨهادی در ﻣﻮاردی ﮐﻪ ﺗﻌﺪاد ﻧﻤﻮﻧﻪها ﺑﯿﺸﺘﺮ ﺑﻮده اﺳﺖ بهبود 10 تا 15 درﺻﺪی را ﺑﻪ دﻧﺒﺎل داﺷﺘﻪ اﺳﺖ.

design of an intrusion detection system using methods based on intelligent systems

humans are going through a period of their history that computer systems have become an integral part of his life. computer systems, particularly computer networks, have played a crucial role in many aspects of today’s life, so their security is highly regarded. in order to cover their security and the so - called penetration detection in computer networks, many methods such as artificial neural networks are proposed, but one of the challenges of neural networks is to fall into local. the main reason for this problem is the use of slope - based methods in the training process, which is therefore used to solve this challenge by many optimization methods. in this paper, we have tried to train a multilayer perceptron neural network using the colonial competition algorithm and in order to evaluate its efficiency, the proposed method is compared with two other methods. the results of the proposed method have improved 10 - 15 % improvement in cases where the number of samples was higher.