Lightweight and Robust Malware Detection Using Dictionaries of API Calls Article Swipe
Ammar Yahya Daeef
,
Ali Al‐Naji
,
Javaan Chahl
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/telecom4040034
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/telecom4040034
Malware in today’s business world has become a powerful tool used by cyber attackers. It has become more advanced, spreading quickly and causing significant harm. Modern malware is particularly dangerous because it can go undetected, making it difficult to investigate and stop in real time. For businesses, it is vital to ensure that the computer systems are free from malware. To effectively address this problem, the most responsive solution is to operate in real time at the system’s edge. Although machine learning and deep learning have given promising performance for malware detection, the significant challenge is the required processing power and resources for implementation at the system’s edge. Therefore, it is important to prioritize a lightweight approach at the system’s edge. Equally important, the robustness of the model against the concept drift at the system’s edge is crucial to detecting the evolved zero-day malware attacks. Application programming interface (API) calls emerge as the most promising candidate to provide such a solution. However, it is quite challenging to create API call features to achieve a lightweight implementation, high malware detection rate, robustness, and fast execution. This study seeks to investigate and analyze the reuse rate of API calls in both malware and goodware, shedding light on the limitations of API call dictionaries for each class using different datasets. By leveraging these dictionaries, a statistical classifier (STC) is introduced to detect malware samples. Furthermore, the study delves into the investigation of model drift in the STC model, employing entirely distinct datasets for training and testing purposes. The results show the outstanding performance of the STC model in accurately detecting malware, achieving a recall value of one, and exhibiting robustness against model drift. Furthermore, the proposed STC model shows comparable performance to deep learning algorithms, which makes it a strong competitor for performing real-time inference on edge devices.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/telecom4040034
- https://www.mdpi.com/2673-4001/4/4/34/pdf?version=1698906583
- OA Status
- gold
- Cited By
- 3
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388221193
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388221193Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/telecom4040034Digital Object Identifier
- Title
-
Lightweight and Robust Malware Detection Using Dictionaries of API CallsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-02Full publication date if available
- Authors
-
Ammar Yahya Daeef, Ali Al‐Naji, Javaan ChahlList of authors in order
- Landing page
-
https://doi.org/10.3390/telecom4040034Publisher landing page
- PDF URL
-
https://www.mdpi.com/2673-4001/4/4/34/pdf?version=1698906583Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2673-4001/4/4/34/pdf?version=1698906583Direct OA link when available
- Concepts
-
Malware, Computer science, Robustness (evolution), System call, Cryptovirology, Application programming interface, Reuse, Artificial intelligence, Machine learning, Classifier (UML), Computer security, Operating system, Chemistry, Biology, Ecology, Gene, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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37Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| publication_date | 2023-11-02 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W6752925936, https://openalex.org/W2965780602, https://openalex.org/W4200486360, https://openalex.org/W2800740927, https://openalex.org/W2061796027, https://openalex.org/W3000731494, https://openalex.org/W3205886849, https://openalex.org/W4361755177, https://openalex.org/W4367311412, https://openalex.org/W4205607115, https://openalex.org/W4309089711, https://openalex.org/W3158694465, https://openalex.org/W2099425933, https://openalex.org/W4293106276, https://openalex.org/W2900633536, https://openalex.org/W2997139308, https://openalex.org/W6744444321, https://openalex.org/W3000953536, https://openalex.org/W2990166555, https://openalex.org/W4315853584, https://openalex.org/W2969763191, https://openalex.org/W1966917005, https://openalex.org/W2307930854, https://openalex.org/W4308973957, https://openalex.org/W4319083647, https://openalex.org/W4210802791, https://openalex.org/W2990527448, https://openalex.org/W3127265720, https://openalex.org/W3095563611, https://openalex.org/W3171309826, https://openalex.org/W1995416968, https://openalex.org/W2926701059, https://openalex.org/W2898457271, https://openalex.org/W3006140559, https://openalex.org/W2981560863, https://openalex.org/W6888444293, https://openalex.org/W2844415866 |
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