当前位置: X-MOL 学术Ad Hoc Netw. › 论文详情
Malware detection in industrial internet of things based on hybrid image visualization and deep learning model
Ad Hoc Networks ( IF 3.490 ) Pub Date : 2020-05-07 , DOI: 10.1016/j.adhoc.2020.102154
Hamad Naeem; Farhan Ullah; Muhammad Rashid Naeem; Shehzad Khalid; Danish Vasan; Sohail Jabbar; Saqib Saeed

Now the Industrial Internet of Things (IIoT) devices can be deployed to monitor the flow of data, the source of collection and supervision on a large scale of complex networks. It implements large networks for sending and receiving data connected by smart devices. Malware threats, which are primarily targeted at conventional computers linked to the Internet, can also be targeted at IoT machines. Therefore, a smart protection approach is needed to protect millions of IIoT users against malicious attacks. On the other hand, existing state-of - the-art malware identification methods are not better in terms of computational complexity. In this paper, we design architecture to detect malware attacks on the Industrial Internet of Things (MD-IIOT). For an in-depth analysis of malware, a methodology is proposed based on color image visualization and deep convolution neural network. The findings of the proposed method are compared to former approaches to malware detection. The experimental results indicate that the proposed method's predictive time and detection accuracy are higher than that of previous machine learning and deep learning methods.
更新日期:2020-05-07

 

全部期刊列表>>
Springer化学材料学
骄傲月
如何通过Nature平台传播科研成果
跟Nature、Science文章学绘图
隐藏1h前已浏览文章
中洪博元
课题组网站
新版X-MOL期刊搜索和高级搜索功能介绍
ACS材料视界
x-mol收录
南开大学
朱守非
廖良生
郭东升
汪铭
伊利诺伊大学香槟分校
徐明华
中山大学化学工程与技术学院
试剂库存
天合科研
down
wechat
bug