Results 91 to 100 of about 81,305 (190)

CSMC: A Secure and Efficient Visualized Malware Classification Method Inspired by Compressed Sensing

Sensors
With the rapid development of the Internet of Things (IoT), the sophistication and intelligence of sensors are continually evolving, playing increasingly important roles in smart homes, industrial automation, and remote healthcare.
Wei Wu, Haipeng Peng, Haotian Zhu, Derun Zhang   +3 more
doaj   +1 more source

HoneyBow:an automated malware collection tool based on the high-interaction honeypot principle

Tongxin xuebao, 2007
Malware has become one of the severest threats to the public Internet.To deal with the malware breakout ef-fectively as early as possible,an automated malware collection solution must be implemented as a precondition.An automated malware collection tool ...
ZHUGE Jian-wei1, HAN Xin-hui1, ZHOU Yong-lin2, SONG Cheng-yu1, GUO Jin-peng1, ZOU Wei1   +5 more
doaj   +2 more sources

Homology analysis of malware based on graph

Tongxin xuebao, 2017
Malware detection and homology analysis has been the hotspot of malware analysis.API call graph of malware can represent the behavior of it.Because of the subgraph isomorphism algorithm has high complexity,the analysis of malware based on the graph ...
Bing-lin ZHAO, Xi MENG, Jin HAN, Jing WANG, Fu-dong LIU   +4 more
doaj   +2 more sources

Malware

, 2016
Robert H. Sloan, Richard Warner
  +4 more sources

WSN malware infection model based on cellular automaton and static Bayesian game

Dianxin kexue, 2019
The theoretical model for the malware infection in wireless sensor networks (WSN) based on cellular automaton and static Bayesian game was studied.Firstly,the malware infection model of WSN based on cellular automaton was built.Secondly,the malware ...
Hong ZHANG, Shigen SHEN, Xiaojun WU, Qiying CAO   +3 more
doaj  

A deep learning-based IoT malware detection approach for electric vehicle charging stations. [PDF]

Sci Rep
Xia L, Chen Y, Han L.
europepmc   +1 more source

Malware detection in IoT networks with CNNs and integrated feature engineering. [PDF]

Sci Rep
Abd-Ellah MK, Alsayed NA, Elkomy OM, El-Hady WM.   +3 more
europepmc   +1 more source

Few-shot android malware classification with quantum-enhanced prototypical learning and drift detection. [PDF]

Sci Rep
Tawfik M, Tarazi H, Dalalah A, Bajeszeyadaljunaeidia, Shaban WM, Fathi IS.   +5 more
europepmc   +1 more source

Systematic Evaluation of Machine Learning and Deep Learning Models for IoT Malware Detection Across Ransomware, Rootkit, Spyware, Trojan, Botnet, Worm, Virus, and Keylogger. [PDF]

Sensors (Basel)
Maghanaki M, Keramati S, Chen FF, Shahin M.   +3 more
europepmc   +1 more source

Hierarchical malware detection, family identification, and variant attribution using CNN-based hybrid models on grayscale executable images. [PDF]

Sci Rep
Saxena M, Das T.
europepmc   +1 more source