A Comprehensive Review on Malware Detection Approaches
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 affec...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8949524/ |
| _version_ | 1819171339356864512 |
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| author | Omer Aslan Refik Samet |
| author_facet | Omer Aslan Refik Samet |
| author_sort | Omer Aslan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T19:49:43Z |
| format | Article |
| id | doaj.art-eadd6ea07c0d4d2cb16e7120832d9060 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T19:49:43Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-eadd6ea07c0d4d2cb16e7120832d90602022-12-21T18:14:36ZengIEEEIEEE Access2169-35362020-01-0186249627110.1109/ACCESS.2019.29637248949524A Comprehensive Review on Malware Detection ApproachesOmer Aslan0https://orcid.org/0000-0003-0737-1966Refik Samet1https://orcid.org/0000-0001-8720-6834Computer Engineering Department, Ankara University, Ankara, TurkeyComputer Engineering Department, Ankara University, Ankara, TurkeyAccording 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.https://ieeexplore.ieee.org/document/8949524/Cyber securitymalware classificationmalware detection approachesmalware features |
| spellingShingle | Omer Aslan Refik Samet A Comprehensive Review on Malware Detection Approaches IEEE Access Cyber security malware classification malware detection approaches malware features |
| title | A Comprehensive Review on Malware Detection Approaches |
| title_full | A Comprehensive Review on Malware Detection Approaches |
| title_fullStr | A Comprehensive Review on Malware Detection Approaches |
| title_full_unstemmed | A Comprehensive Review on Malware Detection Approaches |
| title_short | A Comprehensive Review on Malware Detection Approaches |
| title_sort | comprehensive review on malware detection approaches |
| topic | Cyber security malware classification malware detection approaches malware features |
| url | https://ieeexplore.ieee.org/document/8949524/ |
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