HMM-Web: a framework for the detection of attacks against Web applications

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Nowadays, the web-based architecture is the most frequently used for a wide range of internet services, as it allows to easily access and manage information and software on remote machines. The input …

Nowadays, the web-based architecture is the most frequently used for a wide range of internet services, as it allows to easily access and manage information and software on remote machines. The input of web applications is made up of queries, i.e. sequences of pairs attribute←value. A wide range of attacks exploits web application vulnerabilities, typically derived from input validation flaws. In this work we propose a new formulation of query analysis through Hidden Markov Models (HMM) and show that HMM are effective in detecting a wide range of either known or unknown attacks on web applications. In addition, despite previous works, we explicitly address the problem related to the presence of noise (i.e., attacks) in the training set. Finally, we show that performance can be increased when a sequence of symbols is modelled by an ensemble of HMM. Experimental results on real world data, show the effectiveness of the proposed system in terms of very high detection rates and low false alarm rates.

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  • 1. PRA Pattern Recognition and Applications Group! HMM-Web: a framework for the detection off attacks against Web Applications I. Corona, D. Ariu, G. Giacinto Presenter Davide Ariu Pattern Recognition and Applications Group P R A Department of Electrical and Electronic Engineering University of Cagliari, Italy June 17, 2009 ICC 2009 - HMMWeb - Davide Ariu 1
  • 2. Outline •  Motivations •  HMM-Web vs. Web Application Firewalls •  Description of the IDS Scheme •  Noise inside the training set •  Sequences codification •  Experimental Setup •  Experimental Results •  Conclusions June 17, 2009 ICC 2009 - HMMWeb - Davide Ariu 2
  • 3. Motivations Why we do address the problem of securing Web Applications? June 17, 2009 ICC 2009 - HMMWeb - Davide Ariu 3
  • 4. Motivations Source: X-Force® 2008 Trend & Risk Report – January 2009 June 17, 2009 ICC 2009 - HMMWeb - Davide Ariu 4
  • 5. Motivations Source: X-Force® 2008 Trend & Risk Report – January 2009 June 17, 2009 ICC 2009 - HMMWeb - Davide Ariu 5
  • 6. Protection of Web Applications •  Web Applications can be protected using a Web-Application Firewall (WAF) –  WAF filter applications’ input using a set of rules. •  Writing rules for a Web-Application Firewall is a procedure: –  Vulnerable to zero-days attacks •  WAF can’t stop an attack if it doesn’t have a rule against it –  Time Expensive •  Rules must be written by hand by the administrator –  Prone to errors •  Requires the administrator having an in-depth knowledge of applications which reside on the Web-Server June 17, 2009 ICC 2009 - HMMWeb - Davide Ariu 6
  • 7. HMM-Web •  HMM-Web addresses all of the weaknesses of Web-Application Firewalls because is an Intrusion Detection System: –  Anomaly Based •  This means which is also able to face with zero-days attacks –  Fully Automated for what concerns the training procedure •  Time saving •  Doesn’t require the administrator having knowledge of applications which reside on the Web-Server June 17, 2009 ICC 2009 - HMMWeb - Davide Ariu 7
  • 8. An usage scenario June 17, 2009 ICC 2009 - HMMWeb - Davide Ariu 8
  • 9. Request URI Modelling •  As attacks like XSS and SQL-Injection exploit input validation flaws, we want to model the input provided by the user. •  User-provided data are passed by the browser to the Web-Server (then to the application) using a sequence of attribute-value pairs. •  Consequently, we want to model: –  The sequence of attributes –  The value of each attribute June 17, 2009 ICC 2009 - HMMWeb - Davide Ariu 9
  • 10. Request URI Modelling •  From the example request URI GET /search.php?cat=32&key=hmm HTTP/1.1 we extract: –  The name of the application: “search.php” –  The sequence of attributes: “cat-key” –  The value of each attribute: •  “32” for the attribute cat •  “hmm” for the attribute key •  These are the elements that HMM-Web analyses June 17, 2009 ICC 2009 - HMMWeb - Davide Ariu 10
  • 11. Classifier Ensemble •  HMM-Web is based on Hidden Markov Models •  For each application running on the Web Server HMM-Web creates a module consisting of –  An HMM-Ensemble to model the sequence of attributes •  This feature allows to detect request URI modified by hand –  An HMM-Ensemble for each one of attributes received by the Web Application •  This feature allows to detect if one attribute is receiving an anomalous value. June 17, 2009 ICC 2009 - HMMWeb - Davide Ariu 11
  • 12. IDS-Scheme June 17, 2009 ICC 2009 - HMMWeb - Davide Ariu 12
  • 13. Noise in the training set •  HMM-Web is trained on a training set made of requests toward the Web-Server we want to protect. •  This means that this training set might contain both legitimate and attack requests. •  From a Pattern Recognition point of view,this is a problem of training on noisy data.. How does this noise affect HMM-Web performances? June 17, 2009 ICC 2009 - HMMWeb - Davide Ariu 13
  • 14. Noise in the training set •  The assumption that the most part of queries inside the training set is legitimate is not reasonable for applications which are rarely interrogated. June 17, 2009 ICC 2009 - HMMWeb - Davide Ariu 14
  • 15. Noise in the training set Countermeasure •  We propose to model the fraction of attacks inside the training set as: M 1 α = ∑α i ⋅ | q(w i ) | N i=1 •  Where: –  M is the number of applications on the Web Server –  N is the number of queries in the training set –  | q(w i ) | is the number of queries on the i-th application –  α i is the fraction of attacks on the i-th application € How can we estimate effectively αi € € for each application? June 17, 2009 ICC 2009 - HMMWeb - Davide Ariu 15
  • 16. Noise in the training set Countermeasure •  Experimental results show that even a rough estimate of the amount of attacks inside the training set, allows to improve the performances of the IDS. •  A good estimate of α i is that provided by the following formula: α αi = , ∀i ∈ [1, M ] M ⋅ freq(w i ) •  freq(w i ) is € simply the ratio between the number of queries toward the i-th application and the overall number of queries. € € June 17, 2009 ICC 2009 - HMMWeb - Davide Ariu 16
  • 17. Attribute value codification •  The values passed to the attributes might contain digits, alphabetic letters or meta- characters. •  As it is not important distinguishing between elements belonging to each one of these categories, HMM-Web –  Replaces all the digits with the symbol “N” –  Replaces all the alphabetic letters with the symbol “A” –  Leaves immutate meta-characters •  E.g. The attribute value “/dir/sub/1,2” becomes “/AAA/AAA/N,N” June 17, 2009 ICC 2009 - HMMWeb - Davide Ariu 17
  • 18. Experimental Setup •  We tested HMM-Web on a production Web- Server of our Academic Institution. •  The Web-Server hosts 52 Applications: –  24 provide services for registered users –  28 provide public services •  Dataset D: 150.000 queries toward the Web – Server •  Dataset A: 38 attacks against 18 applications –  19 Cross Site Scripting Attacks –  19 SQL Injection Attacks June 17, 2009 ICC 2009 - HMMWeb - Davide Ariu 18
  • 19. Experimental Results Effectiveness of attributes’ codification The curve on the right has been obtained using the codification proposed by Kruegel et al. In “A multimodel approach to the detection of web-based attacks”, Computer Networks, 2005. June 17, 2009 ICC 2009 - HMMWeb - Davide Ariu 19
  • 20. Experimental Result Effectiveness of the MCS Approach June 17, 2009 ICC 2009 - HMMWeb - Davide Ariu 20
  • 21. Conclusions •  In this work we propose an anomaly-based IDS for the protection of Web-Applications •  Respect to traditional WAF HMM-Web is able to face with zero-days attacks and doesn’t require the administrator having an in-dept knowledge of applications to be protected. •  We suggest also a solution for the codification of queries toward the web server and a strategy to take into account the noise into the training set. •  HMM-Web achieves excellent results in terms of detection/false positive rate, even against attacks that are similar to those inside the training set. June 17, 2009 ICC 2009 - HMMWeb - Davide Ariu 21
  • 22. Questions? June 17, 2009 ICC 2009 - HMMWeb - Davide Ariu 22