From Static to AI-Driven Detection: A Comprehensive Review of Obfuscated Malware Techniques
The frequency of cyber attacks targeting individuals, businesses, and organizations globally has escalated in recent years. The evolution of obfuscated malware, designed to evade detection, has been unprecedented, employing new and sophisticated mechanisms to breach systems, steal sensitive data, an...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10924165/ |
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| author | Saranya Chandran Sreelakshmi R. Syam Sriram Sankaran Tulika Pandey Krishnashree Achuthan |
| author_facet | Saranya Chandran Sreelakshmi R. Syam Sriram Sankaran Tulika Pandey Krishnashree Achuthan |
| author_sort | Saranya Chandran |
| collection | DOAJ |
| description | The frequency of cyber attacks targeting individuals, businesses, and organizations globally has escalated in recent years. The evolution of obfuscated malware, designed to evade detection, has been unprecedented, employing new and sophisticated mechanisms to breach systems, steal sensitive data, and disrupt operations. This work advances research on obfuscated malware detection by offering a comprehensive review of studies conducted over the past decade on multiple platforms. In addition, the diversity of obfuscation techniques and the effectiveness of detection methods, such as static, dynamic, hybrid, and AI, are presented in a comparative manner. Furthermore, memory forensics, an often underexplored area, is of paramount importance for real-time analysis and the detection of advanced obfuscated malware. Hybrid analysis, which amalgamates the strengths of various approaches, emerges as a robust solution against obfuscated malware detection. The role of AI in detecting advanced ransomware, spyware, and fileless malware by enabling real-time detection and adaptive defenses against these increasingly prevalent threats is presented. In addition, a novel framework is proposed, combining Generative AI and digital twins to simulate and predict malware behavior, offering enhanced detection capabilities. This study synthesizes the findings of 76 approaches for the detection of obfuscated malware, incorporates cutting-edge technologies, and identifies open research challenges, such as ensuring scalability, enhancing generalization across platforms, and reducing resource requirements for constrained environments to guide future advancements in obfuscated malware detection. |
| format | Article |
| id | doaj-art-98f1e2c7db0e4bdfbd9609bc39ddbfdf |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-98f1e2c7db0e4bdfbd9609bc39ddbfdf2025-08-20T03:11:21ZengIEEEIEEE Access2169-35362025-01-0113743357435810.1109/ACCESS.2025.355078110924165From Static to AI-Driven Detection: A Comprehensive Review of Obfuscated Malware TechniquesSaranya Chandran0https://orcid.org/0009-0001-4396-4921Sreelakshmi R. Syam1Sriram Sankaran2Tulika Pandey3Krishnashree Achuthan4https://orcid.org/0000-0003-2618-0882Center for Cybersecurity Systems and Networks, Amrita Vishwa Vidyapeetham, Amritapuri, IndiaCenter for Cybersecurity Systems and Networks, Amrita Vishwa Vidyapeetham, Amritapuri, IndiaCenter for Cybersecurity Systems and Networks, Amrita Vishwa Vidyapeetham, Amritapuri, IndiaNational E-Governance Division, Ministry of Electronics and Information Technology, Government of India, New Delhi, IndiaCenter for Cybersecurity Systems and Networks, Amrita Vishwa Vidyapeetham, Amritapuri, IndiaThe frequency of cyber attacks targeting individuals, businesses, and organizations globally has escalated in recent years. The evolution of obfuscated malware, designed to evade detection, has been unprecedented, employing new and sophisticated mechanisms to breach systems, steal sensitive data, and disrupt operations. This work advances research on obfuscated malware detection by offering a comprehensive review of studies conducted over the past decade on multiple platforms. In addition, the diversity of obfuscation techniques and the effectiveness of detection methods, such as static, dynamic, hybrid, and AI, are presented in a comparative manner. Furthermore, memory forensics, an often underexplored area, is of paramount importance for real-time analysis and the detection of advanced obfuscated malware. Hybrid analysis, which amalgamates the strengths of various approaches, emerges as a robust solution against obfuscated malware detection. The role of AI in detecting advanced ransomware, spyware, and fileless malware by enabling real-time detection and adaptive defenses against these increasingly prevalent threats is presented. In addition, a novel framework is proposed, combining Generative AI and digital twins to simulate and predict malware behavior, offering enhanced detection capabilities. This study synthesizes the findings of 76 approaches for the detection of obfuscated malware, incorporates cutting-edge technologies, and identifies open research challenges, such as ensuring scalability, enhancing generalization across platforms, and reducing resource requirements for constrained environments to guide future advancements in obfuscated malware detection.https://ieeexplore.ieee.org/document/10924165/Androidartificial intelligencedeep learningdigital twinsgenerative AIhybrid detection methods |
| spellingShingle | Saranya Chandran Sreelakshmi R. Syam Sriram Sankaran Tulika Pandey Krishnashree Achuthan From Static to AI-Driven Detection: A Comprehensive Review of Obfuscated Malware Techniques IEEE Access Android artificial intelligence deep learning digital twins generative AI hybrid detection methods |
| title | From Static to AI-Driven Detection: A Comprehensive Review of Obfuscated Malware Techniques |
| title_full | From Static to AI-Driven Detection: A Comprehensive Review of Obfuscated Malware Techniques |
| title_fullStr | From Static to AI-Driven Detection: A Comprehensive Review of Obfuscated Malware Techniques |
| title_full_unstemmed | From Static to AI-Driven Detection: A Comprehensive Review of Obfuscated Malware Techniques |
| title_short | From Static to AI-Driven Detection: A Comprehensive Review of Obfuscated Malware Techniques |
| title_sort | from static to ai driven detection a comprehensive review of obfuscated malware techniques |
| topic | Android artificial intelligence deep learning digital twins generative AI hybrid detection methods |
| url | https://ieeexplore.ieee.org/document/10924165/ |
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