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Deep Learning and Visualization for Identifying Malware Families
IEEE Transactions on Dependable and Secure Computing, 2021The growing threat of malware is becoming more and more difficult to ignore. In this paper, a malware feature images generation method is used to combine the static analysis of malicious code with the methods of recurrent neural networks (RNN) and convolutional neural networks (CNN).
Guosong Sun, Quan Qian
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Advanced 3D Visualization of Android Malware Families
2021The number of attacks aimed at compromising smartphones in general, and Android devices in particular, is acknowledged as one of the main security concerns of these devices. Accordingly, a great effort has been devoted in recent years to deal with such incidents. However, scant attention has been paid to study the application of different visualization
Nuño Basurto +5 more
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Proceedings of the 8th International Symposium on Visualization for Cyber Security, 2011
We propose a simple yet effective method for visualizing and classifying malware using image processing techniques. Malware binaries are visualized as gray-scale images, with the observation that for many malware families, the images belonging to the same family appear very similar in layout and texture.
Lakshmanan Nataraj +3 more
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We propose a simple yet effective method for visualizing and classifying malware using image processing techniques. Malware binaries are visualized as gray-scale images, with the observation that for many malware families, the images belonging to the same family appear very similar in layout and texture.
Lakshmanan Nataraj +3 more
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Neural Visualization of Android Malware Families
2016Due to the ever increasing amount and severity of attacks aimed at compromising smartphones in general, and Android devices in particular, much effort have been devoted in recent years to deal with such incidents. However, scant attention has been devoted to study the interplay between visualization techniques and Android malware detection.
Alejandro González +2 more
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A static and dynamic visual debugger for malware analysis
2012 18th Asia-Pacific Conference on Communications (APCC), 2012The number of viruses and malware has grown dramatically over the last few years, and this number is expected to grow in all likelihood. Due to the increasing amount of malicious software circulated over the Internet, it is almost impossible to reverse engineering all binary executable software line by line as it is very challenging and time consuming.
Chan Lee Yee +3 more
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MalViz: an interactive visualization tool for tracing malware
Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis, 2018This demonstration paper introduces MalViz, a visual analytic tool for analyzing malware behavioral patterns through process monitoring events. The goals of this tool are: 1) to investigate the relationship and dependencies among processes interacted with a running malware over a certain period of time, 2) to support professional security experts in ...
Vinh The Nguyen 0001 +2 more
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Geographical Visualization of Malware Download for Anomaly Detection
2012 Seventh Asia Joint Conference on Information Security, 2012We study a linkage between attacks in cyberspace and incidents in our real world. For example, the Internet had been closed down in Egypt for preventing protests against President Hosni Mubarak. Meanwhile, for more than two weeks we have observed that no port-scan packet were sent from Egypt to Japan.
N. Hiroguchi +3 more
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Mobile malware visual analytics and similarities of Attack Toolkits (Malware gene analysis)
2013 International Conference on Collaboration Technologies and Systems (CTS), 2013We use Normalized Compression Distance (NCD) (owing to its capabilities to perform similarity measure of unstructured data) to enumerate code similarity between malicious Android apps and visualize their clusters. Our classification methods and visual analytics can help the antivirus community to ensure that a variant of a known malware can still be ...
Anand Paturi +3 more
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A Visualization-Based Analysis on Classifying Android Malware
2019Since the introduction of the Android mobile platform, the state of mobile malware has evolved in both attack sophistication and its ability to evade detection. Given the right combination of elements, the detection of malicious applications may be found among those that pose no threat, yet the threats that exist across these malware types reveal ...
Rory Coulter +3 more
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Malware Visualization Based on Deep Learning
2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2021Zhuojun Ren, Ting Bai
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