Fuzzing vulnerability discovery techniques: Survey, challenges and future directions

Beaman, Craig; Redbourne, Michael; Mummery, J. Darren; Hakak, Saqib

doi:10.1016/j.cose.2022.102813

Cited by 26 publications

(8 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other researchers in the software engineering domain also employed the same data collection process. [54][55][56][57][58][59][60] The following steps were involved in conducting the questionnaire survey.…”

Section: F I G U R E 1 Flowchart Of Research Methodologymentioning

confidence: 99%

“…As a result, we used a non-methodical technique for data collecting, namely an online survey using snow balling technique. Other researchers in the software engineering domain also employed the same date collection process [54][55][56][57][58][59][60]. The following steps were involved in conducting the questionnaire survey:…”

Section: Step-2: Empirical Investigation Figure 1: Flowchart Of Resea...mentioning

confidence: 99%

See 1 more Smart Citation

Security risks of global software development life cycle: Industry practitioner's perspective

Khan

Akbar

et al. 2022

J Software Evolu Process

View full text Add to dashboard Cite

Software security has become increasingly important because the malicious attack and other hacker risks of a computer system have grown popularity in the last few years. As a result, several researchers have examined security solutions as early as the requirement engineering phase. With the growth of the software business and the internet, there is a need to understand the security risks against each phase of the software development life cycle (SDLC). This study aims to empirically investigate and prioritize the risks that could negatively impact the software security aspects of SDLC in the context of global software development (GSD). To achieve the study objectives, we conducted an industrial empirical study to determine the impact of software security threats against each phase of SDLC. Furthermore, the fuzzy analytical hierarchy process (FAHP) was used to prioritize the list of software security risks against the SDLC. The results and analysis of this study provide a ranked‐based decision‐making framework, which assists the practitioners in considering the most critical security risks on priority. The results show “improper plan for secure requirement identification, inception, authentication, authorization, and privacy,” “lack of threat models updating,” “lack of output validation,” “lack of certification in the final release and archive,” and “spoofing” as the top‐ranked security risks of SDLC in GSD. In addition, the application of FAHP is novel in this domain as it is helpful to address multicriteria decision‐making problems.

show abstract

Section: F I G U R E 1 Flowchart Of Research Methodologymentioning

confidence: 99%

Section: Step-2: Empirical Investigation Figure 1: Flowchart Of Resea...mentioning

confidence: 99%

Security risks of global software development life cycle: Industry practitioner's perspective

Khan

Akbar

et al. 2022

J Software Evolu Process

View full text Add to dashboard Cite

show abstract

“…Solutions: This paper suggests utilizing fuzz testing techniques to detect vulnerabilities related to handling mechanism imbalances. Fuzz testing 40 is a technique that involves supplying random, invalid, or abnormal data as input to a program in order to test its behavior. It has already been applied to the detection of vulnerabilities in blockchain smart contracts.…”

Section: Modeling and Solutionsmentioning

confidence: 99%

A Comprehensive Overview of Security Vulnerability Penetration Methods in Blockchain Cross-Chain Bridges

Zhao,

Wang,

Yang

et al. 2023

Preprint

View full text Add to dashboard Cite

Cross-chain bridges are crucial mechanisms for facilitating interoperation between different blockchains, allowing the flow of assets and information across various chains. Their pivotal role and the vast value of assets they handle make them highly attractive to attackers. Major security incidents involving cross-chain bridge projects have been occurring frequently, resulting in losses of several billion due to cyber attacks. The diversity of vulnerability exploitation methods by hackers is vast, but not entirely untraceable. There are scarce research outcomes studying cross-chain bridge cyber incidents, and we have conducted a study based on the most recent cross-chain bridge security incidents. We introduce the working principles, components, and architecture of cross-chain bridges, explain the categorization mechanisms of the trust layer in cross-chain bridges, summarize four categories of hacker vulnerability exploitation techniques from real cases, and propose preventative measures for cross-chain bridge security.

show abstract

“…The effectiveness of fuzzing in exploiting program vulnerabilities varies, as different test cases traverse distinct execution paths. Seed scheduling optimization is widely recognized as a highly effective strategy for enhancing the efficiency of fuzzing [2]. In the coverageguided fuzz testing, the fuzzer initially maintains a queue of seeds.…”

Section: Introductionmentioning

confidence: 99%

LinFuzz: Program-Sensitive Seed Scheduling Greybox Fuzzing Based on LinUCB Algorithm

Su,

Xiong,

Wan

et al. 2024

IEEE Access

View full text Add to dashboard Cite

The mutation-based grey box fuzz testing technique is one of the widely used dynamic vulnerability mining techniques. It generates test cases for testing by mutating input seeds. In the process of fuzz testing, the seed scheduling strategy and energy scheduling strategy impact the test results and efficiency. Existing seed scheduling strategies, however, only consider a few specific seed attributes and ignore contextual information during seed execution. This oversight makes it challenging to prioritize the selection of suitable seeds based on historical fuzz test results. Meanwhile, current methods for calculating coverage lack evaluation of software paths, which makes it easy to waste time on testing high-frequency and low-risk paths. This article proposes a new grey box fuzz scheme, LinFuzz, which transforms the seed scheduling problem into a contextual multi-arm bandit machine model. It utilizes the LinUCB algorithm to assess the value of seeds for scheduling by considering their historical execution information. At the same time, LinFuzz enhances the calculation method for fuzz testing path rewards and the seed energy scheduling algorithm. It allocates more energy for low-frequency paths in the testing program, thereby enhancing the exploration efficiency and path coverage ability of the testing tool. This article evaluated the proposed LinFuzz on 12 real programs in comparison with other open-source tools like AFL, AFLFast, FairFuzz, Neuzz, etc. The results show that under the same testing time budget, LinFuzz outperforms other tools in terms of vulnerability discovery quantity and code coverage ability. Compared with complex fuzz testing optimization algorithms, LinFuzz has lower memory consumption and time complexity.INDEX TERMS Coverage-guided fuzzing, LinUCB Algorithm, Seed Scheduling

show abstract

Fuzzing vulnerability discovery techniques: Survey, challenges and future directions

Cited by 26 publications

References 16 publications

Security risks of global software development life cycle: Industry practitioner's perspective

Security risks of global software development life cycle: Industry practitioner's perspective

A Comprehensive Overview of Security Vulnerability Penetration Methods in Blockchain Cross-Chain Bridges

LinFuzz: Program-Sensitive Seed Scheduling Greybox Fuzzing Based on LinUCB Algorithm

Contact Info

Product

Resources

About