Kolloquiumsvortrag 10. Dezember 2024, Avinash Rajendra Prasad (Betreuer: Al Sardy)
i7Fuzzer: An Intelligent Approach for Protocol Security Testing
This Master’s thesis focuses on protocol security testing with the goal of the development and implementation of the i7Fuzzer. i7Fuzzer represents an approach to security testing, integrating machine learning techniques with traditional fuzzing methodologies to provide comprehensive assessments of stateful protocols. By leveraging guided state exploration, message sequence construction, and neural network-based mutation probability computation, i7Fuzzer aims to uncover vulnerabilities and bolster the resilience of networked systems against cyber threats.
In a preliminary part of the thesis, an exploration of the challenges inherent in traditional fuzzing techniques when applied to stateful protocols is to be carried out. A detailed overview of i7Fuzzer’s architecture follows, explaining the roles of its key components in the security testing process. Methodologies such as guided state exploration, message sequence construction, and mutation probability computation are examined extensively to provide a comprehensive understanding of i7Fuzzer’s operational mechanisms.
Based on this information, a major part of the work will be devoted to the technical implementation of i7Fuzzer, including integration considerations with existing protocol testing environments and optimizations for scalability and performance.
Finally, the capabilities and the limitations of the implemented i7Fuzzer are to be evaluated by means of suitable examples. The evaluation encompasses considerations of performance, such as computational time, and coverage breadth, providing an accurate interpretation of the experimental findings.
Zeit: 10:15 Uhr
Ort: Raum 04.137, Martensstr. 3, Erlangen
oder
Zoom-Meeting beitreten:
https://fau.zoom-x.de/j/68350702053?pwd=UkF3aXY0QUdjeSsyR0tyRWtLQ0hYUT09
Meeting-ID: 683 5070 2053
Kenncode: 647333