Improving the Efficiency of Misuse Detection

Guided pattern mining for API misuse detection by change-based code analysis

Automated Software Engineering ◽

10.1007/s10515-021-00294-x ◽

2021 ◽

Vol 28 (2) ◽

Author(s):

Sebastian Nielebock ◽

Robert Heumüller ◽

Kevin Michael Schott ◽

Frank Ortmeier

Keyword(s):

Pattern Mining ◽

Third Party ◽

Just In Time ◽

False Alarms ◽

Misuse Detection ◽

Code Search ◽

Usage Patterns ◽

Development Processes ◽

Code Changes ◽

Api Usage

AbstractLack of experience, inadequate documentation, and sub-optimal API design frequently cause developers to make mistakes when re-using third-party implementations. Such API misuses can result in unintended behavior, performance losses, or software crashes. Therefore, current research aims to automatically detect such misuses by comparing the way a developer used an API to previously inferred patterns of the correct API usage. While research has made significant progress, these techniques have not yet been adopted in practice. In part, this is due to the lack of a process capable of seamlessly integrating with software development processes. Particularly, existing approaches do not consider how to collect relevant source code samples from which to infer patterns. In fact, an inadequate collection can cause API usage pattern miners to infer irrelevant patterns which leads to false alarms instead of finding true API misuses. In this paper, we target this problem (a) by providing a method that increases the likelihood of finding relevant and true-positive patterns concerning a given set of code changes and agnostic to a concrete static, intra-procedural mining technique and (b) by introducing a concept for just-in-time API misuse detection which analyzes changes at the time of commit. Particularly, we introduce different, lightweight code search and filtering strategies and evaluate them on two real-world API misuse datasets to determine their usefulness in finding relevant intra-procedural API usage patterns. Our main results are (1) commit-based search with subsequent filtering effectively decreases the amount of code to be analyzed, (2) in particular method-level filtering is superior to file-level filtering, (3) project-internal and project-external code search find solutions for different types of misuses and thus are complementary, (4) incorporating prior knowledge of the misused API into the search has a negligible effect.

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Misuse Detection for Mobile Devices Using Behaviour Profiling

International Journal of Cyber Warfare and Terrorism ◽

10.4018/ijcwt.2011010105 ◽

2011 ◽

Vol 1 (1) ◽

pp. 41-53 ◽

Cited By ~ 6

Author(s):

Fudong Li ◽

Nathan Clarke ◽

Maria Papadaki ◽

Paul Dowland

Keyword(s):

Mobile Devices ◽

Error Rate ◽

Text Message ◽

Modern Society ◽

Experimental Results ◽

Equal Error Rate ◽

Misuse Detection ◽

General Application ◽

Point Of Entry ◽

Application Specific

Mobile devices have become essential to modern society; however, as their popularity has grown, so has the requirement to ensure devices remain secure. This paper proposes a behaviour-based profiling technique using a mobile user’s application usage to detect abnormal activities. Through operating transparently to the user, the approach offers significant advantages over traditional point-of-entry authentication and can provide continuous protection. The experiment employed the MIT Reality dataset and a total of 45,529 log entries. Four experiments were devised based on an application-level dataset containing the general application; two application-specific datasets combined with telephony and text message data; and a combined dataset that included both application-level and application-specific. Based on the experiments, a user’s profile was built using either static or dynamic profiles and the best experimental results for the application-level applications, telephone, text message, and multi-instance applications were an EER (Equal Error Rate) of 13.5%, 5.4%, 2.2%, and 10%, respectively.

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