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Detection of Mobile Malware in the Wild
Computer, 2012New techniques for detecting the presence of mobile malware can help protect smartphones from potential security threats.
Chandramohan, M., Tan, H.B.K.
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A Malware Detection Approach Using Malware Images and Autoencoders
2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), 2020Most machine learning-based malware detection systems use various supervised learning methods to classify different instances of software as benign or malicious. This approach provides no information regarding the behavioral characteristics of malware. It also requires a large amount of training data and is prone to labeling difficulties and can reduce
Xiang Jin +4 more
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Malware detection using malware image and deep learning
2017 International Conference on Information and Communication Technology Convergence (ICTC), 2017These days a lot of malware are generated. In order to deal with the new malware, we need new ways to detect malware. In this paper, we introduce a method to detect malware using deep learning. First, we generate images from benign files and malware. Second, by using deep learning, we train a model to detect malware.
Sunoh Choi +3 more
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Detection of Smartphone Malware
2011Due to technological progress, mobile phones evolved into technically and functionally sophisticated devices called smartphones. Providing comprehensive capabilities, smartphones are getting increasingly popular not only for the targeted users but all. Since 2004, several malwares appeared targeting these devices.
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Malware detection based on ontology
2017 International Conference on Machine Learning and Cybernetics (ICMLC), 2017Malware in form of Internet worms, computer viruses, and Trojan horses poses a major threat to the security of networked systems. So how to describe the behavior knowledge of malware is an interesting and meaningful work. In recent years, different ontology technologies have been proposed to represent domain knowledge.
Xiao-Ling Xia +3 more
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Vigenère scores for malware detection
Journal of Computer Virology and Hacking Techniques, 2017Previous research has applied classic cryptanalytic techniques to the malware detection problem. Specifically, scores that are based on simple substitution cipher cryptanalysis have been considered. In this research, we analyze two malware scoring techniques based on the classic Vigenere cipher.
Suchita Deshmukh +2 more
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Code Graph for Malware Detection
2008 International Conference on Information Networking, 2008When an application program is executed for the first time, the results of its execution are not always predictable. Since the host will be damaged by a malware as soon as it is executed, detecting and blocking the malware before its execution is the most effective means of protection.
Kyoochang Jeong, Heejo Lee
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2022 4th Novel Intelligent and Leading Emerging Sciences Conference (NILES), 2022
Khaled Fawzy Mohamed, Marianne A. Azer
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Khaled Fawzy Mohamed, Marianne A. Azer
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From Plagiarism to Malware Detection
2013 15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, 2013We have often seen how malware families evolve over time: the malware authors add new features, change the order of functions, modify some strings or add random useless code. They do all that to evade detection. In a similar way, computer science students that copy homework will change variable and function names, rephrase comments or even replace some
Ciprian Oprisa +2 more
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Malware Analysis and Detection
Proceedings of the Second International Conference on AI-ML Systems, 2022Hemant Rathore, Mohit Sewak
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