Results 81 to 90 of about 4,213 (184)

Stateless Malware Packet Detection by Incorporating Naive Bayes with Known Malware Signatures

Applied Computational Intelligence and Soft Computing, 2014
Malware detection done at the network infrastructure level is still an open research problem ,considering the evolution of malwares and high detection accuracy needed to detect these threats.
Ismahani Ismail, Sulaiman Mohd Nor, Muhammad Nadzir Marsono   +2 more
doaj   +1 more source

Hybrid Input Model Using Multiple Features From Surface Analysis for Malware Detection

IEEE Access
Many malware detection models have been proposed to protect computers from the ever- increasing number of malware attacks. The features that are obtained from surface analysis and machine learning are often used for malware detection.
Mamoru Mimura, Satoki Kanno
doaj   +1 more source

MIDALF—multimodal image and audio late fusion for malware detection

EURASIP Journal on Information Security
Malware detection remains a critical challenge in cybersecurity due to the rapid evolution of sophisticated malware and adversarial threats. Traditional detection systems struggle to adapt to dynamic malware behavior and are particularly vulnerable to ...
Setia Juli Irzal Ismail, Hendrawan, Budi Rahardjo, Tutun Juhana, Yasuo Musashi   +4 more
doaj   +1 more source

GNSTAM: Integrating Graph Networks With Spatial and Temporal Signature Analysis for Enhanced Android Malware Detection

IEEE Access
The sophistication of Android malware poses significant threats to user security and privacy. Traditional detection methods struggle with rapid malware evolution and benign application diversity, leading to high false positive rates and limited ...
Yogesh Kumar Sharma, Deepak Singh Tomar, R. K. Pateriya, Surendra Solanki   +3 more
doaj   +1 more source

Detection of unseen malware threats using generative adversarial networks and deep learning models

Scientific Reports
The fast advancement of malware makes it an urgent problem for cybersecurity, as perpetrators consistently devise obfuscation methods to avoid detection.
Chirag Joshi, Jashvant Kumar, Gaurav Kumawat   +2 more
doaj   +1 more source

Logically Explainable Malware Detection. [PDF]

Malware detection is a challenging application due to the rapid evolution of attack techniques, and traditional signature-based approaches struggle with the high volume of malware samples. Machine learning approaches face such limitation, but lack a clear interpretability, whereas interpretable models often underperform.
Anthony, Peter, Giannini, Francesco, Diligenti, Michelangelo, Gori, Marco, Homola, Martin, Balogh, Stefan, Mojžiš, Ján   +6 more
openaire   +2 more sources

A Study on Malware and Malware Detection Techniques [PDF]

International Journal of Education and Management Engineering, 2018
openaire   +1 more source

Self-Organizing Neural Grove for Malware Detection in IoT Edge Devices. [PDF]

Sensors (Basel)
Inoue H, Komura T, Hashimoto I.
europepmc   +1 more source

A Survey on Malware and Malware Detection Systems

International Journal of Computer Applications, 2013
Ali M. A. Abuagoub, Ali Selamat, Imtithal A.Saeed   +2 more
openaire   +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