A soft computing approach for benign and malicious web robot detection
•We propose a method called SMART (Soft computing for MAlicious RoboT detection).•The method detects benign and malicious robots, and human visitors to a web server.•SMART selects its features on a particular web server by fuzzy rough set theory.•A graph-based clustering algorithm classifies session...
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| Published in: | Expert systems with applications Vol. 87; pp. 129 - 140 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
30.11.2017
Elsevier BV |
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | •We propose a method called SMART (Soft computing for MAlicious RoboT detection).•The method detects benign and malicious robots, and human visitors to a web server.•SMART selects its features on a particular web server by fuzzy rough set theory.•A graph-based clustering algorithm classifies sessions into the three agent types.•Analyses on web logs suggest state-of-the-art results to detect both robot types.
The accurate detection of web robot sessions from a web server log is essential to take accurate traffic-level measurements and to protect the performance and privacy of information on a Web server. Moreover, the irrecoverable risks of visits from malicious robots that intentionally try to evade web server intrusion detection systems, covering-up their visits with fabricated fields in their http request packets, cannot be ignored. To separate both types of robots from humans in practice, analysts turn to heuristic methods or state-of-the-art soft computing approaches that have only been tuned to the specification of a kind of web server. Noting that the landscape of web robot agents is ever changing, and that behavioral patterns and characteristics vary across different web servers, both options are lacking. To overcome this challenge, this paper presents SMART, a soft computing system that simultaneously detects benign and malicious types of robot agents from web server logs and can automatically adapt to the session characteristics of a web server. The results of experiments over some access log file servers, each servicing different domains of the web, demonstrate outperformance of the proposed method on state-of-the-art ones for benign and malicious robot detection. |
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| AbstractList | The accurate detection of web robot sessions from a web server log is essential to take accurate traffic-level measurements and to protect the performance and privacy of information on a Web server. Moreover, the irrecoverable risks of visits from malicious robots that intentionally try to evade web server intrusion detection systems, covering-up their visits with fabricated fields in their http request packets, cannot be ignored. To separate both types of robots from humans in practice, analysts turn to heuristic methods or state-of-the-art soft computing approaches that have only been tuned to the specification of a kind of web server. Noting that the landscape of web robot agents is ever changing, and that behavioral patterns and characteristics vary across different web servers, both options are lacking. To overcome this challenge, this paper presents SMART, a soft computing system that simultaneously detects benign and malicious types of robot agents from web server logs and can automatically adapt to the session characteristics of a web server. The results of experiments over some access log file servers, each servicing different domains of the web, demonstrate outperformance of the proposed method on state-of-the-art ones for benign and malicious robot detection. •We propose a method called SMART (Soft computing for MAlicious RoboT detection).•The method detects benign and malicious robots, and human visitors to a web server.•SMART selects its features on a particular web server by fuzzy rough set theory.•A graph-based clustering algorithm classifies sessions into the three agent types.•Analyses on web logs suggest state-of-the-art results to detect both robot types. The accurate detection of web robot sessions from a web server log is essential to take accurate traffic-level measurements and to protect the performance and privacy of information on a Web server. Moreover, the irrecoverable risks of visits from malicious robots that intentionally try to evade web server intrusion detection systems, covering-up their visits with fabricated fields in their http request packets, cannot be ignored. To separate both types of robots from humans in practice, analysts turn to heuristic methods or state-of-the-art soft computing approaches that have only been tuned to the specification of a kind of web server. Noting that the landscape of web robot agents is ever changing, and that behavioral patterns and characteristics vary across different web servers, both options are lacking. To overcome this challenge, this paper presents SMART, a soft computing system that simultaneously detects benign and malicious types of robot agents from web server logs and can automatically adapt to the session characteristics of a web server. The results of experiments over some access log file servers, each servicing different domains of the web, demonstrate outperformance of the proposed method on state-of-the-art ones for benign and malicious robot detection. |
| Author | Zabihimayvan, Mahdieh Rude, H. Nathan Sadeghi, Reza Doran, Derek |
| Author_xml | – sequence: 1 givenname: Mahdieh surname: Zabihimayvan fullname: Zabihimayvan, Mahdieh email: zabihimayvan.2@wright.edu, mahdieh.zabihi@gmail.com – sequence: 2 givenname: Reza surname: Sadeghi fullname: Sadeghi, Reza email: sadeghi.2@wright.edu – sequence: 3 givenname: H. Nathan surname: Rude fullname: Rude, H. Nathan email: howard.rude@wright.edu – sequence: 4 givenname: Derek surname: Doran fullname: Doran, Derek email: derek.doran@wright.edu |
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| Keywords | Web Robot Detection Web crawler Fuzzy Rough Set Theory Malicious web agents Markov clustering algorithm |
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| Snippet | •We propose a method called SMART (Soft computing for MAlicious RoboT detection).•The method detects benign and malicious robots, and human visitors to a web... The accurate detection of web robot sessions from a web server log is essential to take accurate traffic-level measurements and to protect the performance and... |
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| SubjectTerms | Automation Clustering Cybersecurity File servers Fuzzy Rough Set Theory Heuristic methods Intrusion detection systems Malicious web agents Markov clustering algorithm Privacy Robots Set theory Soft computing State of the art Traffic information Web crawler Web Robot Detection |
| Title | A soft computing approach for benign and malicious web robot detection |
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