The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.
A Comprehensive Review on Malware Detection Approaches
Open accessAbstract
According to the recent studies, malicious software (malware) is increasing at an alarming rate, and some malware can hide in the system by using different obfuscation techniques. In order to protect computer systems and the Internet from the malware, the malware needs to be detected before it affects a large number of systems. Recently, there have been made several studies on malware detection approaches. However, the detection of malware still remains problematic. Signature-based and heuristic-based detection approaches are fast and efficient to detect known malware, but especially signature-based detection approach has failed to detect unknown malware. On the other hand, behavior-based, model checking-based, and cloud-based approaches perform well for unknown and complicated malware; and deep learning-based, mobile devices-based, and IoT-based approaches also emerge to detect some portion of known and unknown malware. However, no approach can detect all malware in the wild. This shows that to build an effective method to detect malware is a very challenging task, and there is a huge gap for new studies and methods. This paper presents a detailed review on malware detection approaches and recent detection methods which use these approaches. Paper goal is to help researchers to have a general idea of the malware detection approaches, pros and cons of each detection approach, and methods that are used in these approaches.
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Cites
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Cited by
Nankya, M., Chataut, R., & Akl, R. (2023) · Sensors · doi:10.3390/s23218840 · Cited by 114
A review of state-of-the-art malware attack trends and defense mechanisms
Ferdous, J., Islam, R., Mahboubi, A., & Islam, M. Z. (2023) · IEEE Access · doi:10.1109/access.2023.3328351 · Cited by 98
An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry
Jiang, W., Synovic, N., Hyatt, M., Schorlemmer, T. R., Sethi, R., Lu, Y.-H., Thiruvathukal, G. K., & Davis, J. C. (2023) · 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) · doi:10.1109/icse48619.2023.00206 · Cited by 76
Metadata
- Authors
- Aslan, O., & Samet, R.
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 6249-6271
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2998074434
- Cited by
- 649
- References
- 67
- FWCI
- 39.98
- Type
- journal article
- Language
- en
- Open access
- gold
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