Kaur, Jaskaran2023-07-042023-07-042023-07-03vt_gsexam:37221http://hdl.handle.net/10919/115633Automated software testing techniques have become increasingly popular in recent years, with fuzzing being one of the most prevalent approaches. However, fuzzing Finite State Machines (FSMs) poses a significant challenge due to state and input dependency, resulting in exponential exploration time required to unlock the Finite State Machine. To address this issue, we present a novel approach in this research paper by introducing FINITEFUZZ, a Grey Box Fuzzer explicitly designed to fuzz Finite State Machines. Unlike the Blackbox fuzzers, FINITEFUZZ employs a mutational technique that utilizes feedback to steer the fuzzing process. FINITEFUZZ takes a random set of states and compares them with the desired FSM and records the states that increase the coverage of the Finite State Machine. The next seed incorporates the feedback received from all the previous seed inputs. This avoids exploring the same path multiple times and results in linear performance for all the types of Finite State machines possible. Our findings reveal that the use of FINITEFUZZ significantly reduces the exploration time required to uncover each state of the machine, making it a promising solution for generating Finite State Machines. We tested our FINITEFUZZ on 4 different types of Finite State Machines with each scenario resulting in at least 5X performance improvement in FSM generation. The potential applications of FSMs are vast, and our research suggests that the proposed approach can be used to generate any type of Finite State Machine.ETDenIn CopyrightFuzzingFinite State MachineIndustrial Control Systems}Thesis