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Malware Visualization for Fine-Grained Classification
IEEE Access, 2018 Due to the rapid rise of automated tools, the number of malware variants has increased dramatically, which poses a tremendous threat to the security of the Internet.
Jianwen Fu +4 more
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Memory Visualization-Based Malware Detection Technique
Sensors, 2022 Advanced Persistent Threat is an attack campaign in which an intruder or team of intruders establishes a long-term presence on a network to mine sensitive data, which becomes more dangerous when combined with polymorphic malware.
Syed Shakir Hameed Shah +2 more
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Malware homology determination using visualized images and feature fusion [PDF]
PeerJ Computer Science, 2021 The family homology determination of malware has become a research hotspot as the number of malware variants are on the rise. However, existing studies on malware visualization only determines homology based on the global structure features of executable,
Xuejin Zhu +3 more
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Mathematics, 2020 The importance of cybersecurity has recently been increasing. A malware coder writes malware into normal executable files. A computer is more likely to be infected by malware when users have easy access to various executables.
Sejun Jang, Shuyu Li, Yunsick Sung
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Malware Detection Based on Code Visualization and Two-Level Classification
Information, 2021 Malware creators generate new malicious software samples by making minor changes in previously generated code, in order to reuse malicious code, as well as to go unnoticed from signature-based antivirus software.
Vassilios Moussas, Antonios Andreatos
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Malware Analysis Using Visualized Image Matrices [PDF]
The Scientific World Journal, 2014 This paper proposes a novel malware visual analysis method that contains not only a visualization method to convert binary files into images, but also a similarity calculation method between these images.
KyoungSoo Han, BooJoong Kang, Eul Gyu Im
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IEEE Access, 2023 In recent studies, convolutional neural networks (CNNs) are mostly used as dynamic techniques for visualization-based malware classification and detection.
Mohamad Mulham Belal +1 more
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An Efficient DenseNet-Based Deep Learning Model for Malware Detection
Entropy, 2021 Recently, there has been a huge rise in malware growth, which creates a significant security threat to organizations and individuals. Despite the incessant efforts of cybersecurity research to defend against malware threats, malware developers discover ...
Jeyaprakash Hemalatha +4 more
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MalView: Interactive Visual Analytics for Comprehending Malware Behavior
IEEE Access, 2022 Malicious applications are usually comprehended through two major techniques, namely static and dynamic analyses. Through static analysis, a given malicious program is parsed, and some representative artifacts (e.g., control-flow graphs) are produced ...
Huyen N. Nguyen +5 more
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Detection and Visualization of Android Malware Behavior [PDF]
Journal of Electrical and Computer Engineering, 2016 Malware analysts still need to manually inspect malware samples that are considered suspicious by heuristic rules. They dissect software pieces and look for malware evidence in the code.
Oscar Somarriba +4 more
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