API Misuse Detection An Immune System inspired Approach
Maxime Gallais-Jimenez, Hoan A. Nguyen, Mohamed Aymen Saied, Tien N., Nguyen, Houari Sahraoui

TL;DR
This paper introduces APIMMUNE, a bio-inspired API misuse detection system that uses artificial immune system concepts to identify risky API usages in client code with high accuracy.
Contribution
The paper presents a novel immune system inspired approach for API misuse detection, leveraging artificial detectors generated from normal usage data.
Findings
APIMMUNE achieves high detection accuracy.
It performs well on multiple API misuse datasets.
It complements existing pattern-based detection tools.
Abstract
APIs are essential ingredients for developing complex software systems. However, they are difficult to learn and to use. Thus, developers may misuse them, which results in various types of issues. In this paper, we explore the use of a bio-inspired approach (artificial immune system) to detect API misuses in client code. We built APIMMUNE, a novel API misuse detector. We collect normal usages of a given APIs from the set of client programs using the APIs, especially after some API usages were fixed in those programs. The normal API usages are considered as normal body cells. We transform them into normal-usage signatures. Then, artificial detectors are randomly generated by generating artificial deviations from these usages with the objective of being different from the normal usage signatures. The generated detectors have the ability to detect risky uses of APIs exactly as the immune…
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Taxonomy
TopicsAdvanced Malware Detection Techniques · Network Security and Intrusion Detection · Anomaly Detection Techniques and Applications
