Results 181 to 190 of about 12,725 (233)

MalHAPGNN: An Enhanced Call Graph-Based Malware Detection Framework Using Hierarchical Attention Pooling Graph Neural Network. [PDF]

Sensors (Basel)
Guo W, Du W, Yang X, Xue J, Wang Y, Han W, Hu J.   +6 more
europepmc   +1 more source

ISAnWin: inductive generalized zero-shot learning using deep CNN for malware detection across windows and android platforms. [PDF]

PeerJ Comput Sci
Tayyab UE, Khan FB, Khan A, Durad MH, Khan FA, Ali A.   +5 more
europepmc   +1 more source

CyberSentinel: A Transparent Defense Framework for Malware Detection in High-Stakes Operational Environments. [PDF]

Sensors (Basel)
Basak M, Han MM.
europepmc   +1 more source

Deep learning hybridization for improved malware detection in smart Internet of Things. [PDF]

Sci Rep
Almazroi AA, Ayub N.
europepmc   +1 more source

AMDDLmodel: Android smartphones malware detection using deep learning model. [PDF]

PLoS One
Aamir M, Iqbal MW, Nosheen M, Ashraf MU, Shaf A, Almarhabi KA, Alghamdi AM, Alghamdi AM, Bahaddad AA.   +8 more
europepmc   +1 more source

PermQRDroid: Android malware detection with novel attention layered mini-ResNet architecture over effective permission information image. [PDF]

PeerJ Comput Sci
Kılıç K, Doğru İA, Toklu S.
europepmc   +1 more source

Visualization and deep-learning-based malware variant detection using OpCode-level features

Future Generation Computer Systems, 2021
Malicious software (malware) is a major threat to the systems and networks’ security. Although anti-malware products are used to protect systems and networks against malware attacks, obfuscated malware that is capable of evading analysis and detection by
Abdulbasit A Darem, Jemal Abawajy, Aaisha Makkar   +2 more
exaly   +2 more sources

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Polymorphic Malware Detection

2016 6th International Conference on IT Convergence and Security (ICITCS), 2016
The most regular method of detecting malware relies on signature-based detection. Polymorphic malware pose a serious threat to modern computing. The challenge faced with this type of malware is that there is difficult to Antivirus (AV) technology to detect them. This polymorphic malware can't be detected by AV scanners because of mutated code by itself.
Nur Syuhada Selamat, Fakariah Hani Mohd Ali, Noor Ashitah Abu Othman   +2 more
openaire   +1 more source

Detection of packed malware

Proceedings of the First International Conference on Security of Internet of Things, 2012
Packing is the most popular obfuscation technique used by malware writers' community in present scenario. The traditional signature-based anti-virus software had played a major role in malware detection, until the dawn of the trend of packed malware.
Dhruwajita Devi, Sukumar Nandi
openaire   +1 more source

A Survey on malware detection techniques

2021 International Conference on Information Technology (ICIT), 2021
Malware programs have become a serious threat as it was developed to damage computer systems, spread over the network or Internet connections. Researchers are making great efforts to produce anti-malware systems with practical ways to detect malware protection and malware detection of computer systems.Two basic approaches were proposed: based on the ...
Tibra Alsmadi, Nour Alqudah
openaire   +1 more source