The field of computational mechanics applies ideas from statistical mechanics, information theory, automata theory, and machine learning to create minimally-sized, optimal predictors of stochastic processes. These predictors, called ε-machines, are a subset of a well known statistical model class called the Hidden Markov Model (HMM). Despite being a subset, ε-machines have several important adv...

Fuzzing is a popular software testing method that discovers bugs by massively feeding target applications with automatically generated inputs. Many state-of-art fuzzers use branch coverage as feedback metric to guide the fuzzing process. The fuzzer retains inputs for further mutation only if increased. However, provides shallow sampling of program behaviours and hence may discard interesting mu...

Directed greybox fuzzing (DGF) can quickly discover or reproduce bugs in programs by seeking to reach a program location explore some locations order. However, due their static stage division and coarse-grained energy scheduling, prior DGF tools perform poorly when facing multiple target (targets for short). In this paper, we present targets directed which aims campaign. Specifically, propose n...

Smart contract transactions are increasingly interleaved by cross-contract calls. While many tools have been developed to identify a common set of vulnerabilities, the vulnerability is overlooked existing tools. Cross-contract vulnerabilities exploitable bugs that manifest in presence more than two interacting contracts. Existing methods however limited analyze maximum contracts at same time. D...

Byzantine fault-tolerant algorithms promise agreement on a correct value, even if subset of processes can deviate from the algorithm arbitrarily. While these provide strong guarantees in theory, practice, protocol bugs and implementation mistakes may still cause them to go wrong. This paper introduces ByzzFuzz, simple yet effective method for automatically finding errors implementations through...

Fuzz testing is an automated technique providing random data as input to a software system in the hope to expose a vulnerability. In order to be effective, the fuzzed input must be common enough to pass elementary consistency checks; a JavaScript interpreter, for instance, would only accept a semantically valid program. On the other hand, the fuzzed input must be uncommon enough to trigger exce...