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SIP Security Testing Framework

SIP Security Testing Framework. Presentation By Anil Kumar Marikukala , Syed Khaja Najmuddin Ahmed. Introduction. SIP is a text based and application layer protocol. It has several security mechanisms but it is still vulnerable to attacks.

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SIP Security Testing Framework

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  1. SIP Security Testing Framework Presentation By Anil Kumar Marikukala, Syed KhajaNajmuddin Ahmed.

  2. Introduction • SIP is a text based and application layer protocol. • It has several security mechanisms but it is still vulnerable to attacks. • SIP architecture must be robust to all vulnerabilities. • A comprehensive security testing is to be done before deploying. • This framework combines many techniques to produce many powerful test methodologies.

  3. Common Attacks on SIP • Message Flooding DoS: • attacker tries to deplete resources on a server. • Message Flow DoS: • This attack tries to disrupt the ongoing call by impersonating one of the caller. • Malformed Message Attacks: • This attack may contain Embedded Shell codes or Malicious SQL statements. • Other Attacks : • Attack on DNS server, Spam over Internet Telephony(SPIT) attacks.

  4. Testing Framework • It consists of three tiers. 1. Front Tier. 2. Middle Tier. 3. Target Tier.

  5. Front Tier : • It has uniform GUI(Graphical User Interface) which is dynamic and helps the user to fine tune the tests using Configuration files. • It acts as an interface between User and Middle tier during the setting up. • Middle Tier : • It consists of Central Control Agent and many other modules each with different test functionalities. • Target Tier : • Test agents spawned by the Control Agent constitute the Target Tier. • Performs tasks based on information from Control Agent and sends feedback. • Test agents works in parallel.

  6. Fuzz Data Generation • Fuzz testing is a Software testing technique. • It’s used to find implementation defects using malformed data. • It is considered as a valuable method in assessing the robustness and security vulnerabilities of systems. • Brute force data set, a random data set, known problematic sets these three are generally used data sets. • SIP_int, SIP_ip, SIP_string etc., are the data sets categorized by the authors from combination of above data sets.

  7. New Data Generation Algorithm: • Begin: choosing the initial population from the data sets using any combination. • Fitness: Evaluating the Fitness. • New Population: Creating New Population using different methods like: selection, crossover, mutation. • Acceptance: Placing the offspring in the new population. • Improvisation: Using the new offspring for running the algorithm • Test: stop if the end condition is satisfying.

  8. Evaluation • The following table shows the results after performing tests by calling to the different users.

  9. Continued.. • The following graph represents the response of Registered users and Unregistered users.

  10. Conclusion • SIP security Testing framework provides a uniform platform to integrate several test methodologies and generate more test scenarios. • Fuzzer is not only a protocol aware but also it has an innovative algorithm which generates fuzz data. • The results demonstrates that even though devices are resistant to individual stress and Fuzz testing, they may be vulnerable to test scenarios which combines both.

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