Path sensitive static analysis of taint-style vulnerabilities in PHP code

Yan, Xiao-Bo; Wang, Qingxian; Ma, Hengtai

doi:10.1109/icct.2017.8359859

Cited by 7 publications

(4 citation statements)

References 3 publications

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“…The proposed method reduced the error rate of XSS vulnerabilities. In 2019, YAN et al [6] proposed a PHP code vulnerability detection method based on sensitive path and taint analysis. The method first converted the background code of the web application into the intermediate representation of the code, such as the abstract syntax tree, then found the slot (dangerous function), then determined the sensitive path through the slot, and finally performed taint analysis on this path to determine whether the vulnerability exists.…”

Section: Related Workmentioning

confidence: 99%

Resolving Cross-Site Scripting Attacks through Fusion Verification and Machine Learning

Wei

Qin

et al. 2022

Mathematics

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The frequent variations of XSS (cross-site scripting) payloads make static and dynamic analysis difficult to detect effectively. In this paper, we proposed a fusion verification method that combines traffic detection with XSS payload detection, using machine learning to detect XSS attacks. In addition, we also proposed seven new payload features to improve detection efficiency. In order to verify the effectiveness of our method, we simulated and tested 20 public CVE (Common Vulnerabilities and Exposures) XSS attacks. The experimental results show that our proposed method has better accuracy than the single traffic detection model. Among them, the recall rate increased by an average of 48%, the F1 score increased by an average of 27.94%, the accuracy rate increased by 9.29%, and the accuracy rate increased by 3.81%. Moreover, the seven new features proposed in this paper account for 34.12% of the total contribution rate of the classifier.

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Section: Related Workmentioning

confidence: 99%

Resolving Cross-Site Scripting Attacks through Fusion Verification and Machine Learning

Wei

Qin

et al. 2022

Mathematics

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“…Evaluations need to be done to judge if the sanitizing of user input is effective, or there could be an XSS vulnerability. However, if an entire project requires auditing, the "source-filter-sink", i.e., the XSS triples, needs to be considered as a whole [34].…”

Section: Xss Triggering In Source Codementioning

confidence: 99%

Cross-Site Scripting Guardian: A Static XSS Detector Based on Data Stream Input-Output Association Mining

Wang

Miao

et al. 2020

Applied Sciences

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The largest number of cybersecurity attacks is on web applications, in which Cross-Site Scripting (XSS) is the most popular way. The code audit is the main method to avoid the damage of XSS at the source code level. However, there are numerous limits implementing manual audits and rule-based audit tools. In the age of big data, it is a new research field to assist the manual auditing through machine learning. In this paper, we propose a new way to audit the XSS vulnerability in PHP source code snippets based on a PHP code parsing tool and the machine learning algorithm. We analyzed the operation sequence of source code and built a model to acquire the information that is most closely related to the XSS attack in the data stream. The method proposed can significantly improve the recall rate of vulnerability samples. Compared with related audit methods, our method has high reusability and excellent performance. Our classification model achieved an F1 score of 0.92, a recall rate of 0.98 (vulnerable sample), and an area under curve (AUC) of 0.97 on the test dataset.

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“…To build the mapping relation between the data and the operation, call graph and control flow graph (CFG) are common approaches along with static taint checking. Static taint checking based approaches [39,40] were proposed to detect information leakage.…”

Section: Iirelated Workmentioning

confidence: 99%

A Power-Efficient Approach to Detect Mobile Threats on the Emergent Network Environment

et al. 2020

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Mobile and IoT devices are blooming, and their applications are prevailing worldwide. In the meantime, the Industry 4.0 trend converges industrial control systems with the internet environment, which makes it vulnerable. Mobile and IoT applications provide seamless connectivity to the emergent network environment for daily work. As mobile phones contain privacy information, malicious mobile applications could compromise mobile devices and cause financial losses. Moreover, attackers could launch DDoS attacks to exhaust a resource of mobile or IoT devices in the emergent network environment. Static malware analysis consumes less computing resources and power than dynamic analysis. This study proposes a power-efficient solution to identify mobile threats, which applies taint analysis to protect the emergent network environment. The experimental results show that the proposed approach could detect mobile malware efficiently.

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Path sensitive static analysis of taint-style vulnerabilities in PHP code

Cited by 7 publications

References 3 publications

Resolving Cross-Site Scripting Attacks through Fusion Verification and Machine Learning

Resolving Cross-Site Scripting Attacks through Fusion Verification and Machine Learning

Cross-Site Scripting Guardian: A Static XSS Detector Based on Data Stream Input-Output Association Mining

A Power-Efficient Approach to Detect Mobile Threats on the Emergent Network Environment

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