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Survey of Transformer-Based Malicious Software Detection Systems.

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Titel: Survey of Transformer-Based Malicious Software Detection Systems.
Autoren: Alshomrani, Mohammed, Albeshri, Aiiad, Alturki, Badraddin, Alallah, Fouad Shoie, Alsulami, Abdulaziz A.
Quelle: Electronics (2079-9292); Dec2024, Vol. 13 Issue 23, p4677, 34p
Schlagwörter: TRANSFORMER models, MACHINE learning, MALWARE, DEEP learning, CYBERTERRORISM, RANSOMWARE
Abstract: In the recent past, the level of cyber threats has changed drastically, leading to the current transformation of the cybersecurity landscape. For example, emerging threats like Zero-day and polymorphic malware cannot be detected by conventional detection methods like heuristic and signature-based methods, which have proven useful in the identification of malware. In view of this shift in the cybersecurity paradigm, this study proposes to discuss the utilization of transformer models to improve malware detection effectiveness and the accuracy and efficiency in detecting malicious software. In this regard, this study adopts the application of transformers in identifying different forms of malicious software: ransomware, spyware, and trojans. Transformers are endowed with the ability to handle sequential data and capture intricate patterns. By employing deep learning techniques and conducting thorough contextual analysis, these models enhance the detection process by identifying subtle indications of compromise, which traditional methods may overlook. This research also explains the challenges and limitations related to the application of transformer-based models in real-world cybersecurity settings, which include computing requirements and large-scale labeled datasets' requirements. By the end, the article suggests potential future research avenues in order to improve and integrate these models into cybersecurity systems. [ABSTRACT FROM AUTHOR]
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Datenbank: Complementary Index
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Abstract:In the recent past, the level of cyber threats has changed drastically, leading to the current transformation of the cybersecurity landscape. For example, emerging threats like Zero-day and polymorphic malware cannot be detected by conventional detection methods like heuristic and signature-based methods, which have proven useful in the identification of malware. In view of this shift in the cybersecurity paradigm, this study proposes to discuss the utilization of transformer models to improve malware detection effectiveness and the accuracy and efficiency in detecting malicious software. In this regard, this study adopts the application of transformers in identifying different forms of malicious software: ransomware, spyware, and trojans. Transformers are endowed with the ability to handle sequential data and capture intricate patterns. By employing deep learning techniques and conducting thorough contextual analysis, these models enhance the detection process by identifying subtle indications of compromise, which traditional methods may overlook. This research also explains the challenges and limitations related to the application of transformer-based models in real-world cybersecurity settings, which include computing requirements and large-scale labeled datasets' requirements. By the end, the article suggests potential future research avenues in order to improve and integrate these models into cybersecurity systems. [ABSTRACT FROM AUTHOR]
ISSN:20799292
DOI:10.3390/electronics13234677