Survey on Representation Techniques for Malware Detection System
TLDR
This review paper provides a detailed discussion and full reviews for various types of malware, malware detection techniques, various researches on them, malware analysis methods and different dynamic programming-based tools that could be used to represent the malware sampled.Abstract:
Malicious programs are malignant software’s designed by hackers or cyber offenders with a harmful intent to disrupt computer operation. In various researches, we found that the balance between designing an accurate architecture that can detect the malware and track several advanced techniques that malware creators apply to get variants of malware are always a difficult line. Hence the study of malware detection techniques has become more important and challenging within the security field. This review paper provides a detailed discussion and full reviews for various types of malware, malware detection techniques, various researches on them, malware analysis methods and different dynamic programming-based tools that could be used to represent the malware sampled. We have provided a comprehensive bibliography in malware detection, its techniques and analysis methods for malware researchers.read more
Citations
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Journal ArticleDOI
A hybrid deep learning image-based analysis for effective malware detection
TL;DR: A novel and unified hybrid deep learning and visualization approach for an effective detection of malware and its performance is measured by employing various similarity measures of malware behavior patterns as well as cost-sensitive deep learning architectures.
Journal ArticleDOI
A Survey on Malware Analysis Techniques: Static, Dynamic, Hybrid and Memory Analysis
TL;DR: A semantic and detailed survey of methods used for malware detection like signature-based and heuristic-based, and the importance of memory-based analysis in malware detection is discussed.
Journal ArticleDOI
Use of Data Visualisation for Zero-Day Malware Detection
TL;DR: The prime motivation of the proposal is to identify obfuscated malware using visualisation of the extended x86 IA-32 (opcode) similarity patterns, which are hard to detect with the existing approaches.
Proceedings ArticleDOI
Multi-scale Learning based Malware Variant Detection using Spatial Pyramid Pooling Network
TL;DR: Spatial pyramid pooling (SPP) based malware variant detection models are proposed and their performance is compared with the existing relevant works and it outperforms the existingrelevant works.
Journal ArticleDOI
Malware Detection: Issues and Challenges
Muchammad Naseer,Jack Febrian Rusdi,Nuruddeen Musa Shanono,Sazilah Salam,Zulkiflee Muslim,Nur Azman Abu,Iwan Abadi +6 more
TL;DR: This paper is about extracting and analyzing the latest detection techniques which had been conducted by various studies, and the current challenges of malware deployment from recent studies are emphasized.
References
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TL;DR: A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score.
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Proceedings ArticleDOI
Limits of Static Analysis for Malware Detection
TL;DR: A binary obfuscation scheme that relies on opaque constants, which are primitives that allow us to load a constant into a register such that an analysis tool cannot determine its value, demonstrates that static analysis techniques alone might no longer be sufficient to identify malware.
Journal ArticleDOI
A survey on automated dynamic malware-analysis techniques and tools
TL;DR: An overview of techniques based on dynamic analysis that are used to analyze potentially malicious samples and analysis programs that employ these techniques to assist human analysts in assessing whether a given sample deserves closer manual inspection due to its unknown malicious behavior is provided.