Article detail · 2020
A Comprehensive Review on Malware Detection Approaches
- Year
- 2020
- Type
- article
Data source split
- YÖKSİS YÖKSİS article record
- YÖKSİS venue IEEE Access
- Catalog match (ISSN) IEEE Access
- OpenAlex OpenAlex enrichment (abstract, citations, topics)
Abstract
OpenAlex · English
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.
Topics
Citations
OpenAlex cited_by_count. Not a WoS or Scopus citation count; those sources have no separate column here.
649 citations
OpenAlex cited_by_count (cache / database)
30 publications in the local catalog that cite this work (OpenAlex reference match; not the full global list).
- A Comprehensive Review of Cyber Security Vulnerabilities, Threats, Attacks, and Solutions 2023
- A Comprehensive Systematic Literature Review on Intrusion Detection Systems 2021
- A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions 2024
- A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions 2024
- A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions 2024
- A Comprehensive Systematic Literature Review on Intrusion Detection Systems 2021
- Intelligent Behavior-Based Malware Detection System on Cloud Computing Environment 2021
- An ensemble of pre-trained transformer models for imbalanced multiclass malware classification 2022
- A Review of Cloud-Based Malware Detection System: Opportunities, Advances and Challenges 2021
- Development of a Scale to Measure Cybercrime-Awareness on Social Media 2023