Results 1 to 10 of about 2,781,085 (213)

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   +2 more sources

Malware Detection Techniques

2022 4th Novel Intelligent and Leading Emerging Sciences Conference (NILES), 2022
Khaled Fawzy Mohamed, Marianne A. Azer
openaire   +2 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   +2 more sources

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   +2 more sources

Malware Analysis and Detection

Proceedings of the Second International Conference on AI-ML Systems, 2022
Hemant Rathore, Mohit Sewak
openaire   +2 more sources

Adversarial Examples for Malware Detection

2017
Machine learning models are known to lack robustness against inputs crafted by an adversary. Such adversarial examples can, for instance, be derived from regular inputs by introducing minor—yet carefully selected—perturbations.
Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, Patrick McDaniel   +4 more
openaire   +1 more source

A comprehensive survey on deep learning based malware detection techniques

Computer Science Review, 2023
Sibi Chakkaravarthy Sethuraman
exaly  

A Survey of Android Malware Detection with Deep Neural Models

ACM Computing Surveys, 2021
Yang Xiang, Junyang Qiu
exaly  

Malware Detection Issues, Challenges, and Future Directions: A Survey

Applied Sciences (Switzerland), 2022
Fuad A Ghaleb, Anazida Zainal, Bander Al-Rimy   +2 more
exaly  

EfficientNet convolutional neural networks-based Android malware detection

Computers and Security, 2022
Vinayakumar Ravi, Tuan Pham
exaly