Results 131 to 140 of about 1,405 (158)
Some of the next articles are maybe not open access.
Visual attention for malware classification
Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV, 2022Amidst the extensive global integration of computer systems and augmented connectivity, there have been numerous difficulties within ensuring confidentiality, integrity and availability across all systems. Malware is an ever-present and persistent challenge for security systems of all sorts.
Alsadi N +5 more
openaire +2 more sources
Malware visualization and detection using DenseNets
Personal and Ubiquitous Computing, 2021Rapid advancement in the sophistication of malware has posed a serious impact on the device connected over the Internet. Malware writing is driven by economic benefits; thus, an alarming increase in malware variants is witnessed. Recently, a large volume of malware attacks are reported on Internet of Things (IoT) networks; as these devices are exposed ...
V. Anandhi, P. Vinod, Varun G. Menon
openaire +1 more source
Image visualization based malware detection
2013 IEEE Symposium on Computational Intelligence in Cyber Security (CICS), 2013Malware detection is one of the challenging tasks in Cyber security. The advent of code obfuscation, metamorphic malware, packers and zero day attacks has made malware detection a challenging task. In this paper we present a visualization based approach for malware detection.
Kesav Kancherla, Srinivas Mukkamala
openaire +1 more source
Visualizing a Malware Distribution Network
2016 IEEE Symposium on Visualization for Cyber Security (VizSec), 2016In this paper, we present a case study of visual analytics of a Malware Distribution Network (MDN), a connected set of maliciously compromised domains used to disseminate malicious software to victimize computers and users. We formally define the graph of an MDN to visualize top-level-domain (TLD) data collected from Google Safe Browsing reports in a ...
Sebastian Peryt +5 more
openaire +1 more source
Byte Visualization Method for Malware Classification
Proceedings of the 2020 5th International Conference on Machine Learning Technologies, 2020The exponential increase in the number of malware stems from the fact that attackers often create malware variants with automated tools. And automated tools generally tend to reuse similar function modules. It is essential, therefore, that security analysts distinguish malware families by recognizing similar modules.
Zhuojun Ren, Guang Chen, Wenke Lu
openaire +1 more source
Experiments with Malware Visualization
2013This paper proposes DotPlot visualizations [1,8] for comparing and clustering malware. We describe how to process and customize the malware memory images to get robust and scalable visualizations. We demonstrate the effectiveness of the visualizations for analysing, comparing and clustering malware.
Yongzheng Wu, Roland H. C. Yap
openaire +1 more source
Visualization techniques for efficient malware detection
2013 IEEE International Conference on Intelligence and Security Informatics, 2013Traditional tools for reverse engineering of binary and PE files are limited to heavy text base output, thus requiring skilled analysts to use them. In this paper, we present techniques that will visualize PE files, which will help analysts with basic skills to quickly understand their underlying structure.
John Donahue +2 more
openaire +1 more source
Computers & Security, 2022
With the ever-increasing threat of malware attacks, building an effective malware classifier to detect malware promptly is of utmost importance. Malware visualization approaches and deep learning techniques have proven effective in classifying sophisticated malware from benchmark datasets.
Conti M., Khandhar S., Vinod P.
openaire +4 more sources
With the ever-increasing threat of malware attacks, building an effective malware classifier to detect malware promptly is of utmost importance. Malware visualization approaches and deep learning techniques have proven effective in classifying sophisticated malware from benchmark datasets.
Conti M., Khandhar S., Vinod P.
openaire +4 more sources
Visualization Approach for Malware Classification with ResNeXt
2020 IEEE Congress on Evolutionary Computation (CEC), 2020The Internet has resulted in cyber-threats and cyber-crimes, which can occur anywhere at any time. Among various cyber threats, modern malware with applied metamorphosis and polymorphic technology is a concern as it can proliferate to advanced variants from its original shape.
Jin Ho Go +5 more
openaire +1 more source
Analysis of Visualization Techniques for Malware Detection
2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), 2020Due to the steady growth of various sophisticated types of malware, different malware analysis systems are becoming more and more demanded. While there are various automatic approaches available to identify and detect malware, the malware analysis is still time-consuming process.
Anastasia Kartel +2 more
openaire +1 more source