Results 231 to 240 of about 134,256 (265)

API2Vec: Learning Representations of API Sequences for Malware Detection

International Symposium on Software Testing and Analysis, 2023
Analyzing malware based on API call sequence is an effective approach as the sequence reflects the dynamic execution behavior of malware.Recent advancements in deep learning have led to the application of these techniques for mining useful information ...
Lei Cui, Jiancong Cui, Yuede Ji, Zhiyu Hao, Lun Li, Zhenquan Ding   +5 more
semanticscholar   +1 more source

A comprehensive survey on machine learning approaches for malware detection in IoT-based enterprise information system

Enterprise Information Systems, 2022
The Internet of Things (IoT) is a relatively new technology that has piqued academics’ and business information systems’ attention in recent years. The Internet of Things establishes a network that enables smart devices in an organisational information ...
Akshat Gaurav, B. Gupta, P. Panigrahi
semanticscholar   +1 more source

Static Multi Feature-Based Malware Detection Using Multi SPP-net in Smart IoT Environments

IEEE Transactions on Information Forensics and Security
With the steady increase in the demand for Internet of Things (IoT) devices in diverse industries, such as manufacturing, medical care, and transportation infrastructure, the production of malware tailored for Smart IoT environments is also increasing ...
Jueun Jeon, Byeonghui Jeong, Seungyeon Baek, Young-Sik Jeong   +3 more
semanticscholar   +1 more source

Attention-Based Multidimensional Deep Learning Approach for Cross-Architecture IoMT Malware Detection and Classification in Healthcare Cyber-Physical Systems

IEEE Transactions on Computational Social Systems, 2023
A literature survey shows that the number of malware attacks is gradually growing over the years due to the growing trend of Internet of Medical Things (IoMT) devices. To detect and classify malware attacks, automated malware detection and classification
Vinayakumar Ravi, T. Pham, M. Alazab
semanticscholar   +1 more source

Image-Based malware classification using ensemble of CNN architectures (IMCEC)

Computers & security, 2020
Both researchers and malware authors have demonstrated that malware scanners are unfortunately limited and are easily evaded by simple obfuscation techniques.
Danish Vasan, M. Alazab, Sobia Wassan, Babak Safaei, Zheng Qin   +4 more
semanticscholar   +1 more source

Malware

2007
The Trojan horse can be used in cyber-warfare and cyber-terrorism, as recent attacks in the field of industrial espionage have shown. To coordinate methods of defence a categorisation of the threat posed by Trojan horses in the shape of a list of tuples is proposed.
Stefan Kiltz, Andreas Lang, Jana Dittmann   +2 more
openaire   +2 more sources

Dynamic Android Malware Category Classification using Semi-Supervised Deep Learning

2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), 2020
Due to the significant threat of Android mobile malware, its detection has become increasingly important. Despite the academic and industrial attempts, devising a robust and efficient solution for Android malware detection and category classification is ...
Samaneh Mahdavifar, A. A. Kadir, Rasool Fatemi, Dima Alhadidi, A. Ghorbani   +4 more
semanticscholar   +1 more source

A Survey of Android Malware Detection with Deep Neural Models

ACM Computing Surveys, 2020
Deep Learning (DL) is a disruptive technology that has changed the landscape of cyber security research. Deep learning models have many advantages over traditional Machine Learning (ML) models, particularly when there is a large amount of data available.
Junyang Qiu, Jun Zhang, Wei Luo, Lei Pan, S. Nepal, Yang Xiang   +5 more
semanticscholar   +1 more source

Malware

2006
In the two decades since its first significant appearance, malware has become the most prominent and costly threat to modern IT systems. This chapter examines the nature of malware evolution. It highlights that, as well as the more obvious development of propagation techniques, the nature of payload activities (and the related motivations of the ...
Steven Furnell, Jeremy Ward
openaire   +1 more source

Malware behavior image for malware variant identification

2014 International Symposium on Biometrics and Security Technologies (ISBAST), 2014
Several methods have been devised by researchers to facilitate malware analysis and one of them is through malware visualization. Malware visualization is a field that focuses on representing malware features in a form of visual cues that could be used to convey more information about a particular malware.
Syed Zainudeen Mohd Shaid, Mohd Aizaini Maarof   +1 more
openaire   +1 more source