Evaluating Code Coverage for Kernel Fuzzers via Function Call Graph

Cho, Mingi; Jin, Hoyong; An, Dohyeon; Kwon, Taekyoung

doi:10.1109/access.2021.3129062

Cited by 3 publications

(1 citation statement)

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“…Let us note that in this approach, the so-called error space grows exponentially as the system's complexity increases. The problem with a vast error space can be solved using a testing technique called fuzzing, which is an automated or semi-automated software testing technique that involves providing incorrect, unexpected or random data as input to the system [8], [9], [10], [11]. An effective fuzzer generates such input data that is sufficiently correct not to be directly rejected by the parser but is at the same time sufficiently incorrect to cause unexpected system behavior.…”

Section: Introductionmentioning

confidence: 99%

GAN Neural Networks Architectures for Testing Process Control Industrial Network Against Cyber-Attacks

et al. 2023

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Protection of computer systems and networks against malicious attacks is particularly important in industrial networked control systems. A successful cyber-attack may cause significant economic losses or even destruction of controlled processes. Therefore, it is necessary to test the vulnerability of process control industrial networks against possible cyber-attacks. Three approaches employing Generative Adversarial Networks (GANs) to generate fake Modbus frames have been proposed in this work, tested for an industrial process control network and compared with the classical approach known from the literature. In the first approach, one GAN generates one byte of a message frame. In the next two approaches, expert knowledge about frame structure is used to generate a part of a message frame, while the remaining parts are generated using single or multiple GANs. The classical single-GAN approach is the worst one. The proposed one-GAN-per-byte approach generates significantly more correct message frames than the classical method. Moreover, all the generated fake frames have been correct in two of the proposed approaches, i.e., single GAN for selected bytes and multiple GANs for selected bytes methods. Finally, we describe the effect of cyber-attacks on the operation of the controlled process.INDEX TERMS GAN neural networks, cyber-security, cyber-attacks, industrial network.

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