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首页> 外文会议>International Workshop on Engineering Safety and Security Systems;IEEE international conference on software testing, verification, and validation workshops;Workshop on Testing: Academic and Industrial Conference-Practice and Research Techniques;Workshop on Advances in Model Based Testing;International Workshop on Combinatorial Testing;Workshop on Mutation Analysis;International Workshop on Search-Based Software Testing;International Workshop on TESTing Techniques and Experimentation Benchmarks for Event-Driven Software;Workshop on the Constraints in Software Testing, Verification and Analysis;International Workshop on Regression Testing;International Workshop on Security Testing >Generic Approach for Security Error Detection Based on Learned System Behavior Models for Automated Security Tests
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Generic Approach for Security Error Detection Based on Learned System Behavior Models for Automated Security Tests

机译：基于学习的系统行为模型的自动安全测试的安全错误检测的通用方法

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摘要

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.
机译：软件和IT系统的复杂性不断提高，因此有必要进行研究以解决当前关键安全挑战的技术。为了满足IT基础结构中的安全要求，必须建立安全工程流程。促成更高级别安全性的一个关键因素是在安全测试过程中可靠地检测到安全漏洞。在提出的方法中，我们观察被测系统的行为，并基于派生的行为指标介绍机器学习方法。这是针对不同测试目标的通用方法，可提高自动安全性测试方法的安全性测试结果的准确性。可靠地自动确定安全测试结果中的安全故障，可以提高被测试软件的安全质量，并避免进行昂贵的手动验证。

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