Results 31 to 40 of about 24,379 (232)
BinSlayer: Accurate Comparison of Binary Executables [PDF]
, 2013 As the volume of malware inexorably rises, comparison of binary code is of increasing importance to security analysts as a method of automatically classifying new malware samples; purportedly new examples of malware are frequently a simple evolution of ...
Martial Bourquin +5 more
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Learning and classification of malware behavior
, 2022 S.108-125Malicious software in form of Internet worms, computer viruses, and Trojan horses poses a major threat to the security of networked systems. The diversity and amount of its variants severely undermine the effectiveness of classical signature ...
Düssel, P. +4 more
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Machine-Learning Classifiers for Malware Detection Using Data Features
Journal of ICT Research and Applications, 2021 The spread of ransomware has risen exponentially over the past decade, causing huge financial damage to multiple organizations. Various anti-ransomware firms have suggested methods for preventing malware threats.
Saleh Abdulaziz Habtor +1 more
doaj +1 more source
Research on the Construction of Malware Variant Datasets and Their Detection Method
Applied Sciences, 2022 Malware detection is of great significance for maintaining the security of information systems. Malware obfuscation techniques and malware variants are increasingly emerging, but their samples and API (application programming interface) sequences are ...
Faming Lu +4 more
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Morphological detection of malware [PDF]
2008 3rd International Conference on Malicious and Unwanted Software (MALWARE), 2008 In the field of malware detection, method based on syntactical consideration are usually efficient. However, they are strongly vulnerable to obfuscation techniques. This study proposes an efficient construction of a morphological malware detector based on a syntactic and a semantic analysis, technically on control flow graphs of programs (CFG).
Bonfante, Guillaume +2 more
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Journal of Computer Virology and Hacking Techniques, 2020 When training a machine learning model, there is likely to be a tradeoff between accuracy and the diversity of the dataset. Previous research has shown that if we train a model to detect one specific malware family, we generally obtain stronger results as compared to a case where we train a single model on multiple diverse families. However, during the
Samanvitha Basole +2 more
openaire +3 more sources
Applied Sciences, 2021 Malware are developed for various types of malicious attacks, e.g., to gain access to a user’s private information or control of the computer system. The identification and classification of malware has been extensively studied in academic societies and ...
Dong-Kyu Chae +4 more
doaj +1 more source
Packed malware variants detection using deep belief networks [PDF]
MATEC Web of Conferences, 2020 Malware is one of the most serious network security threats. To detect unknown variants of malware, many researches have proposed various methods of malware detection based on machine learning in recent years.
Zhang Zhigang +3 more
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Quantum Information Processing, 2006 When quantum communication networks proliferate they will likely be subject to a new type of attack: by hackers, virus makers, and other malicious intruders. Here we introduce the concept of "quantum malware" to describe such human-made intrusions. We offer a simple solution for storage of quantum information in a manner which protects quantum networks
Lian-Ao Wu, Daniel A. Lidar
openaire +2 more sources
Impact Analysis of Malware Based on Call Network API with Heuristic Detection Method [PDF]
, 2020 Malware is a program that has a negative influence on computer systems that don't have user permissions. The purpose of making malware by hackers is to get profits in an illegal way. Therefore, we need a malware analysis.
Suryati, One Tika +3 more
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