Deep Image: A precious image based deep learning method for online
malware detection in IoT Environment
- URL: http://arxiv.org/abs/2204.01690v1
- Date: Mon, 4 Apr 2022 17:56:55 GMT
- Title: Deep Image: A precious image based deep learning method for online
malware detection in IoT Environment
- Authors: Meysam Ghahramani, Rahim Taheri, Mohammad Shojafar, Reza Javidan,
Shaohua Wan
- Abstract summary: In this paper, a different view of malware analysis is considered and the risk level of each sample feature is computed.
In addition to the usual machine learning criteria namely accuracy and FPR, a proposed criterion based on the risk of samples has also been used for comparison.
The results show that the deep learning approach performed better in detecting malware.
- Score: 12.558284943901613
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The volume of malware and the number of attacks in IoT devices are rising
everyday, which encourages security professionals to continually enhance their
malware analysis tools. Researchers in the field of cyber security have
extensively explored the usage of sophisticated analytics and the efficiency of
malware detection. With the introduction of new malware kinds and attack
routes, security experts confront considerable challenges in developing
efficient malware detection and analysis solutions. In this paper, a different
view of malware analysis is considered and the risk level of each sample
feature is computed, and based on that the risk level of that sample is
calculated. In this way, a criterion is introduced that is used together with
accuracy and FPR criteria for malware analysis in IoT environment. In this
paper, three malware detection methods based on visualization techniques called
the clustering approach, the probabilistic approach, and the deep learning
approach are proposed. Then, in addition to the usual machine learning criteria
namely accuracy and FPR, a proposed criterion based on the risk of samples has
also been used for comparison, with the results showing that the deep learning
approach performed better in detecting malware
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