Please use this identifier to cite or link to this item:
|metadata.conference.dc.title:||Malware Analysis and Detection Approaches: A Survey that Drives to Deep Learning in Analyzing Program Execution Flow.|
|metadata.conference.dc.description.abstract:||the increasing growing number of malware attacks poses serious threats to the private data as well as to the expensive computer resources. To detect malware and their associated families, Anti- Virus(AV) companies commonly rely on signatures, such as strings and regular expressions. However, the recent malware attacks in the last few years including the resurgence of ransomware proved that signature-based methods are error-prone and can be easily evaded by intelligent malware programs. This paper presents a survey of traditional and state of-the-art models developed for malware analysis and detection. Further, the presented approaches for efficient classification of malware and their behavior facilitates in provision of basic insights for the researchers working in the domain of malware analysis through deep learning.|
|metadata.conference.dc.conference.name:||International Conference on Information and Communication Technology|
|Appears in Collections:||Conference Papers|
Files in This Item:
|Malware analysis and detection.pdf||58.75 kB||Adobe PDF||View/Open Request a copy|
Items in UniKL IR are protected by copyright, with all rights reserved, unless otherwise indicated.