The Design And Implementatiom On Efficient SIP Signaling Fuzzy Testing System For IMS

IMS (IP Multimedia Subsystem) is an all-IP core network architecture proposed by3GPP (The3rd Generation Partnership Project) standardization organizations. It is the industry-recognized best way to achieve FMC (Fixed Mobile Convergence), and is also the core of NGN (Next Generation Network). With the rapid development of IMS network, its security issues are increasingly concerned.IMS security issues mainly focus on the SIP (Session Initiation Protocol). Nowadays, fuzzy testing has become a mainstream technology for the protocol safety testing. However, current fuzzing technologies usually use the artificial realized or random methods, which mean that there is no logical association between the generated test cases. Moreover, the test efficiency is not high, and it is difficult to improve the test effect. To sovle these problems, this paper proposes an efficient SIP signaling fuzzy testing method for IMS, at the same time designs and realizes an efficient SIP signaling fuzzy testing system for IMS, which is able to provide targeted and efficient fuzzy testing service for IMS.The system improves the ordinary fuzzy testing technology in two aspects: formally describing the fuzzy testing process and dynamically maintaining the variation rules. The first one constructs FSM (Finite State Machine) according to the execution sequence. Then, it automatically selects testing sequences from the FSM model, and changes testing sequences according to the variation rules in a deformity database, so that the testing can test both the vulnerabilities of the software with network protocols and the network protocols’own logical vulnerabilities. The second one obtains the resolution mode of each IMS functional entity for SIP signaling through protocol analysis, and constructs the initial variation rules. Then, it analyzes the released information in the mainstream vulnerability databases, classifies and extends these vulnerabilities according to their causes to further enrich the variation rules. Finally, in the actual testing process, the system monitors the performance change of the network under test to update the variation rules in the deformity database. Functional tests were carried out for evaluating the system. The results show that the system is able to make an effective SIP signaling fuzzy test.