Results 141 to 150 of about 4,213 (184)

A Malware Detection Approach Using Malware Images and Autoencoders

2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), 2020
Most 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, Xiaofei Xing, Haroon Elahi, Guojun Wang 0001, Hai Jiang 0003   +4 more
openaire   +1 more source

Time Detection of Malware Threads

2021
IGA/CebiaTech/2021 ...
Strmiska, Martin, Měsíček, Pavel, Pekař, Libor, Jašek, Roman   +3 more
openaire   +2 more sources

Detection of Smartphone Malware

2011
Due 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.
openaire   +3 more sources

Malware detection based on ontology

2017 International Conference on Machine Learning and Cybernetics (ICMLC), 2017
Malware 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, Yu-Xin Ding, Jing-Zhi Jiang, Rong Zeng   +3 more
openaire   +1 more source

Malware detection using malware image and deep learning

2017 International Conference on Information and Communication Technology Convergence (ICTC), 2017
These 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, Sungwook Jang, Youngsoo Kim 0002, Jong-Hyun Kim   +3 more
openaire   +1 more source

On benign features in malware detection

Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering, 2020
This paper investigates the problem of classifying Android applications into malicious and benign. We analyze the performance of a popular malware detection tool, Drebin, and show that its correct classification decisions often stem from using benign rather than malicious features for making predictions.
Michael Cao, Sahar Badihi, Khaled Ahmed 0004, Peiyu Xiong, Julia Rubin   +4 more
openaire   +1 more source

Malware Detection Techniques

2022 4th Novel Intelligent and Leading Emerging Sciences Conference (NILES), 2022
Khaled Fawzy Mohamed, Marianne A. Azer
openaire   +1 more source

Malware Detection in Ubiquitous Environments

2012
Today, almost every environment (e.g. airports, home, office, etc.) are populated with a high number of heterogeneous devices like smart-phones, sensors, laptops, tablets or hotspots. Taking advantage of the different communications capabilities that these devices have, researches have been studying how to make people collaborate in spontaneous or ...
Manuel García-Cervigón, Roberto Morales   +1 more
openaire   +1 more source

Malware Analysis and Detection

Proceedings of the Second International Conference on AI-ML Systems, 2022
Hemant Rathore, Mohit Sewak
openaire   +1 more source

Vigenère scores for malware detection

Journal of Computer Virology and Hacking Techniques, 2017
Previous 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, Fabio Di Troia, Mark Stamp 0001   +2 more
openaire   +1 more source