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Astract: Voice over IP systems are more and more replacing Public Switched Telephone Network infrastructures. The number of voice telephony installations and the number of Session Initiation Protocol users is constantly increasing. Attacks against Voice over IP systems are becoming more imaginative and many attacks can cause financial damage, e.g., attackers gain money or create costs for the victim. Therefore, the dependency on available and secure Voice over IP systems to conduct secure business is given. We provide an environment to uncover real-world toll fraud attacks by collecting data using a Voice over IP honeynet solution.

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Astract: The number of Voice over IP systems and the number of Session Initiation Protocol users is constantly increasing. The new Voice over IP infrastructures are connected with the traditional Public Switched Telephone Network and attacks against the phone infrastructure are becoming more imaginative. The attacks can cause financial losses, e.g., attackers steal money or incur costs for the victim. We analyze the current status of toll fraud attacks by analyzing real-world attacks collected in a Voice over IP honeynet solution. Based on the detailed data about real attacks, the creation or adaption of existing prevention mechanisms is possible in order to avoid toll fraud attacks in live environments.

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Astract: The increasing complexity of software and IT systems creates the necessity for research on technologies addressing current key security challenges. To meet security requirements in IT infrastructures, a security engineering process has to be established. One crucial factor contributing to a higher level of security is the reliable detection of security vulnerabilities during security tests. In the presented approach, we observe the behavior of the system under test and introduce machine learning methods based on derived behavior metrics. This is a generic method for different test targets which improves the accuracy of the security test result of an automated security testing approach. Reliable automated determination of security failures in security test results increases the security quality of the tested software and avoids costly manual validation.

Keywords:
Astract: The increasing complexity of software and IT systems creates the necessity for research on technologies addressing current key security challenges. To meet security problems in IT infrastructures, a security engineering process has to be established. One crucial factor contributing to a higher level of security is the reliable detection of security vulnerabilities during security tests. We observe the behavior of the system under test and introduce machine learning methods based on derived behavior metrics. This improves the accuracy of the security test result of an automated security testing approach. Reliable automated determination of security failures in security test results increases the security quality of the tested software and avoids costly manual validation.

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Astract: Voice over Internet Protocol based systems replace phone lines in many scenarios and are in wide use today. Automated security tests of such systems are required to detect implementation and configuration mistakes early and in an efficient way. In this paper we present a plugin for our fuzzer framework fuzzolution to automatically detect security vulnerabilities in Session Initiation Protocol based Voice over Internet Protocol softphones, which are examples for endpoints in such telephone systems. The presented approach automates the interaction with the Graphical User Interface of the softphones during test execution and also observes the behavior of the softphones using multiple metrics. Results of testing two open source softphones by using our fuzzer showed that various unknown vulnerabilities could be identified with the implemented plugin for our fuzzing framework.