پديد آورندگان :
ﻋﺒﺪي، زﻫﺮا داﻧﺸﮕﺎه آزاد اﺳﻼﻣﯽ واﺣﺪ ﻋﻠﻮم و ﺗﺤﻘﯿﻘﺎت - داﻧﺸﮑﺪه ﺑﺮق و ﮐﺎﻣﭙﯿﻮﺗﺮ، ﺗﻬﺮان، اﯾﺮان , ﻣﺎزوﭼﯽ، ﻣﺠﺘﺒﯽ ﭘﮋوﻫﺸﮕﺎه ارﺗﺒﺎﻃﺎت و ﻓﻨﺎوري اﻃﻼﻋﺎت، ﺗﻬﺮان، اﯾﺮان , ﭘﻮرﻣﯿﻨﺎ، ﻣﺤﻤﺪﻋﻠﯽ داﻧﺸﮕﺎه آزاد اﺳﻼﻣﯽ واﺣﺪ ﻋﻠﻮم و ﺗﺤﻘﯿﻘﺎت - داﻧﺸﮑﺪه ﺑﺮق و ﮐﺎﻣﭙﯿﻮﺗﺮ، ﺗﻬﺮان، اﯾﺮان
كليدواژه :
رﺗﺒﻪﺑﻨﺪي ﺗﺮاﻓﯿﮑﯽ , ﺷﻨﺎﺳﺎﯾﯽ رﺑﺎت , ﺑﺮﭼﺴﺐﮔﺬاري ﻧﺸﺴﺖ , ﻻگ دﺳﺘﺮﺳﯽ وب ﺳﺮور , دادهﮐﺎوي
چكيده فارسي :
ﺑﺎ ﮔﺴﺘﺮش اﯾﻨﺘﺮﻧﺖ و ﻓﻀﺎي وب، ﺑﺮﻗﺮاري ارﺗﺒﺎط و ﮐﺴﺐ اﻃﻼﻋﺎت در ﺑﯿﻦ اﻓﺮاد از ﺷﮑﻞ ﺳﻨﺘﯽ و اوﻟﯿﮥ ﺧﻮد ﻓﺎﺻﻠﻪ ﮔﺮﻓﺘﻪ و ﺑﻪ درون ﺗﺎرﻧﻤﺎﻫﺎ ﮐﺸﯿﺪه ﺷﺪه اﺳﺖ. ﻫﻤﭽﻨﯿ ﻦ ﻓﻀﺎي ﺟﻬﺎﻧﯽ وب، ﻓﺮﺻﺖ ﺑﺰرﮔ ﯽ را ﺑﺮاي ﮐﺴﺐ و ﮐﺎرﻫﺎ ﻓﺮاﻫﻢ ﻣﯽﮐﻨﺪ ﺗﺎ ارﺗﺒﺎط ﺧﻮد را ﺑﺎ ﻣﺸﺘﺮي ﺑﻬﺒﻮد ﺑﺒﺨﺸﻨﺪ و ﺑﺎزار ﺧﻮد را در دﻧﯿ ﺎي ﺑﺮﺧﻂ ﮔﺴﺘﺮش دﻫﻨ ﺪ. ﮐ ﺴﺐ و ﮐﺎرﻫﺎ ﺑﺮاي ﺑﺮرﺳﯽ ﻣﯿﺰان ﺑﺎزدﯾﺪ و ﻣﺤﺒﻮﺑﯿﺖ ﺳﺎﯾﺖﻫﺎﯾﺸﺎن از ﻣﻌﯿﺎري ﺑﻪ ﻧﺎم رﺗﺒﻪ ﺑﻨﺪي ﺗﺮاﻓﯿﮑﯽ اﺳﺘﻔﺎده ﻣﯽﮐﻨﻨﺪ. رﺗﺒﻪﺑﻨﺪي ﺗﺮاﻓﯿﮑ ﯽ ﻣﯿﺰان ﺑﺎزدﯾﺪﮐﻨﻨﺪﮔﺎن ﯾﮏ ﺳﺎﯾﺖ را اﻧﺪازه ﮔﺮﻓﺘﻪ و ﺑﺮاﺳﺎس ﻫﻤﯿﻦ آﻣﺎر، رﺗﺒﻪاي را ﺑﻪ ﺳﺎﯾﺖ اﺧﺘﺼﺎص ﻣﯽدﻫﺪ. ﯾﮑﯽ از ﻣﻬﻢﺗﺮﯾﻦ ﭼﺎﻟﺶﻫﺎي ﻣﻮﺟﻮد در رﺗﺒﻪﺑﻨﺪي، اﯾﺠﺎد ﺗﺮاﻓﯿﮏ ﺟﻌﻠﯽ ﺗﻮﻟﯿﺪﺷﺪه ﺑﻪوﺳﯿﻠﮥ ﺑﺮﻧﺎﻣﻪ ﻫﺎي ﮐﺎرﺑﺮدي ﺑﻪ ﻧﺎم رﺑﺎت اﺳﺖ. رﺑﺎتﻫﺎ اﺟﺰاي ﻧﺮماﻓﺰاري ﻣﺨﺮب ﻣﻮرد اﺳﺘﻔﺎده ﺑﺮاي ﺗﻮﻟﯿ ﺪ ﻫﺮزﻧﺎﻣﻪ ﻫﺎ، راهاﻧﺪازي ﺣﻤﻼت ﻣﺨﺘﻞﮐﻨﻨﺪة ﺳﺎﻣﺎﻧﻪ، ﻓﯿﺸﯿﻨﮓ ، ﺳﺮﻗﺖ ﻫﻮﯾﺖ و ﺧﺮوج اﻃﻼﻋﺎت و دﯾﮕﺮ ﻓﻌﺎﻟﯿﺖ ﻫﺎي ﻏﯿﺮ ﻗﺎﻧﻮﻧﯽ ﻫﺴﺘﻨﺪ ﺗﺎﮐﻨﻮن روشﻫﺎي ﻣﺨﺘﻠﻔﯽ ﺑﺮاي ﺷﻨﺎﺳﺎﯾﯽ و ﮐﺸﻒ رﺑﺎت ﺻﻮرت ﮔﺮﻓﺘﻪ اﺳﺖ. در اﯾﻦ ﭘﮋوﻫﺶ، ﺷﻨﺎﺳﺎﯾﯽ رﺑﺎتﻫﺎ از ﻃﺮﯾﻖ ﺗﺤﻠﯿﻞ و ﭘﺮدازش ﻻگ دﺳﺘﺮﺳﯽ وب ﺳﺮور و اﺳﺘﻔﺎده از روشﻫﺎي دادهﮐﺎوي، اﻧﺠﺎم ﻣﯽﺷﻮد. ﻧﺘﺎﯾﺞ ﺗﺠﺮﺑﯽ ﻧﺸﺎن ﻣﯽ دﻫﺪ ﮐﻪ روش ﭘﯿﺸﻨﻬﺎد ي در اﯾﻦ ﭘﮋوﻫﺶ ﺑﺎ ﮐﺸﻒ وﯾﮋﮔ ﯽﻫﺎي ﺟﺪﯾﺪ و ﻣﻌﺮﻓﯽ ﺷﺮط ﺟﺪﯾﺪ در ﺑﺮﭼﺴﺐﮔﺬاري ﻧﺸﺴﺖﻫﺎ ، ﺑﺎﻋﺚ ﺑﻬﺒﻮد دﻗﺖ در ﺷﻨﺎﺳﺎﯾﯽ رﺑﺎت ﻫﺎ و در ﻧﺘﯿﺠﻪ اﯾﺠﺎد ﺑﻬﺒﻮد در رﺗﺒﻪﺑﻨﺪي ﺗﺮاﻓﯿﮑﯽ ﺗﺎرﻧﻤﺎﻫﺎ ﻧﺴﺒﺖ ﺑﻪ ﮐﺎرﻫﺎي ﭘﯿﺸﯿﻦ ﺷﺪه اﺳﺖ.
چكيده لاتين :
With the expansion of the Internet and the Web, communication and information gathering between individual has distracted from its traditional form and into web sites. The World Wide Web also offers a great opportunity for businesses to improve their relationship with the client and expand their marketplace in online world. Businesses use a criterion called traffic ranking to determine their site's popularity and visibility. Traffic ranking measures the amount of visitors to a site and based on these statistics, allocates a ranking to the site. One of the most important challenges in the ranking is the creation of fake traffic that generated by applications called robots. Robots are malicious software components that used to generate spam, set up distributed denial of services attacks, fishing, identity theft, removal of information and other illegal activities .there are already several ways to identify and discover the robot. According to Doran et al., The identification methods are divided into two categories: offline and real-time. The offline detection method is divided into three categories: Syntactical Log Analysis, Traffic Pattern Analysis, and Analytical Learning Techniques. The real-time method is performed by the Turing test system. In this research, the identification of robots is done through the offline method by analysis and processing of access logs to the web server and the use of data mining techniques. In this method, first, the features of each session are extracted, then generally these sessions are labeled with three conditions into two categories of human and robot. Finally, by using data mining tool, web robots are detected. In all previous studies, the features are extracted from each sessions, for example in first studies, Tan&Kumar extracted 25 features of sessions. After that Bomhardt et al. used 34 features to identify the robots. In 2009 Stassopoulou et al. used 6 features that was extracted from sessions and so on. But in this research, features are extracted from sessions of a unique user. Experimental results show that the proposed method in this research, by discovering new features and introducing a new condition in session labeling, improves the accuracy of identifying robots and moreover, improves the ranking of web traffic from previous work.
