Results 161 to 170 of about 1,811,594 (209)

Improving malware detection performance using hybrid deep representation learning with heuristic search algorithms. [PDF]

Sci Rep
Anuradha A, Chouhan AS, Srinivas Rao S.
europepmc   +1 more source

MH-1M: A 1.34 Million-Sample Multi-Feature Android Malware Dataset with Rich Metadata. [PDF]

Sci Data
Bragança H, Kreutz D, Rocha V, Assolin J, Feitosa E.   +4 more
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A bio inspired hybrid optimization framework for efficient real time malware detection. [PDF]

Sci Rep
Abualhaj MM, Al-Mimi H, Al-Zyoud M, Al-Khatib SN, Daoud MS, Al-Aqrabi H, Anbar M, Shalaldeh A.   +7 more
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YATSIDroid: an android malware detection framework based on artificial immune system. [PDF]

Sci Rep
Mahindru A, Arora H, Jamel L, Kumar A, Marwaha S, Moges DM, Hajjej F.   +6 more
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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

Enhancing internet of things intrusion detection through high-performance boosting ensemble learning. [PDF]

Sci Rep
Yakovyna V, Fadieiev A.
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BERT-spaCy hybrid NLP and blockchain-enhanced adaptive CTI for IOC extraction and threat prediction. [PDF]

Sci Rep
Mishra S, Alfahidah RA, Alharbi F.
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A Contrastive Dual-Task Framework for Few-Shot Traffic Classification in IoT Networks. [PDF]

Sensors (Basel)
Lu Z, Chen M, Cui S, Zhao B, Zheng Y.
europepmc   +1 more source

Privacy-preserving intrusion detection in IoT smart homes using a federated hybrid 1D-CNN-LSTM model with explainable AI. [PDF]

Sci Rep
Abdelhady G, Hussein KW, Gad IAA.
europepmc   +1 more source

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EntropyVis: Malware classification

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2017
Malware writers often develop malware with automated measures, so the number of malware has increased dramatically. Automated measures tend to repeatedly use significant modules, which form the basis for identifying malware variants and discriminating malware families.
Zhuojun Ren, Guang Chen
openaire   +2 more sources