Fuzz testing system on basis of multi-swarm collaboration evolution genetic algorithm

Abstract

The invention discloses a fuzz testing system on the basis of a multi-swarm collaboration evolution genetic algorithm, and belongs to the field of software safety tests. The fuzz testing system comprises a fuzz testing end and a test sample generating end. The fuzz testing end is responsible for inputting test samples into systems to be tested and repeatedly testing and observing the systems to be tested; the test sample generating end is used for generating the test samples and transmitting the test samples to the fuzz testing ends via networks, computes individual adaptability values by the aid of collected information according to coverage frequencies of basic blocks and trigger conditions of unsafe functions, combines the individual adaptability values with the types of the unsafe functions and enables the test samples to evolve by the aid of a multi-swarm collaboration evolution genetic model, so that new samples can be generated. The fuzz testing system has the advantages that a code coverage rate can be increased when application programs of the networks are tested, and the vulnerability mining efficiency can be improved.

Description

technical field [0001] The invention relates to the field of software safety testing, in particular to a fuzzing testing system based on multi-population cooperative evolution genetic algorithm. Background technique [0002] In recent years, various viruses, worms, Trojan horses and malicious programs have spread in large numbers, which makes the protection of information security face an increasingly severe situation. Among all types of vulnerabilities, the main source of most network attacks is software security vulnerabilities, which are the main reason why these attacks can be formed. Then the mining and analysis of this type of vulnerability is one of the main tasks to deal with security threats. [0003] Simply put, fuzz testing technology is a kind of mining software faults by providing unexpected (deformed) input to the target (software or system) to be tested, and then observing whether the system's processing of these special inputs will cause abnormal results. t...

AI Technical Summary

Problems solved by technology

In the process of testing network applications, due to insufficient understanding of the protocol, the test samples generated by the system have insufficient code test coverage of the system to be tested, which makes fuzz testing inefficient.

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