Uses of Active and Passive Learning in Stateful Fuzzing

TL;DR

This paper investigates how active and passive learning techniques can enhance stateful fuzzing by inferring system models, benchmarking fuzzers, and identifying implementation differences to improve software robustness.

Contribution

It introduces the application of active and passive learning methods to stateful fuzzing, offering new ways to compare, benchmark, and improve fuzzing techniques.

Findings

Active and passive learning aid in inferring system state models.

These techniques help benchmark and compare fuzzers effectively.

They also assist in discovering implementation differences.

Abstract

This paper explores the use of active and passive learning, i.e.\ active and passive techniques to infer state machine models of systems, for fuzzing. Fuzzing has become a very popular and successful technique to improve the robustness of software over the past decade, but stateful systems are still difficult to fuzz. Passive and active techniques can help in a variety of ways: to compare and benchmark different fuzzers, to discover differences between various implementations of the same protocol, and to improve fuzzers.