Results 121 to 130 of about 16,685 (199)

Android malware detection using hybrid ANFIS architecture with low computational cost convolutional layers. [PDF]

PeerJ Comput Sci, 2022
Atacak İ, Kılıç K, Doğru İA.
europepmc   +1 more source

Reassessing feature-based Android malware detection in a contemporary context. [PDF]

PLoS One
Muzaffar A, Ragab Hassen H, Zantout H, Lones MA.   +3 more
europepmc   +1 more source

A Hybrid Analysis-Based Approach to Android Malware Family Classification. [PDF]

Entropy (Basel), 2021
Ding C, Luktarhan N, Lu B, Zhang W.
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
europepmc   +1 more source

Beyond the virus: a first look at coronavirus-themed Android malware. [PDF]

Empir Softw Eng, 2021
Wang L, He R, Wang H, Xia P, Li Y, Wu L, Zhou Y, Luo X, Sui Y, Guo Y, Xu G.   +10 more
europepmc   +1 more source

Graph-augmented multi-modal learning framework for robust android malware detection. [PDF]

Sci Rep
Tanveer MU, Munir K, Alyamani HJ, Hassan SR, Sheraz M, Chuah TC.   +5 more
europepmc   +1 more source

MaSS-Droid: Android Malware Detection Framework Using Multi-Layer Feature Screening and Stacking Integration. [PDF]

Entropy (Basel)
Zhang Z, Han Q, Shi Z.
europepmc   +1 more source

Android malware classification based on random vector functional link and artificial Jellyfish Search optimizer. [PDF]

PLoS One, 2021
Elkabbash ET, Elkabbash ET, Mostafa RR, Barakat SI.   +3 more
europepmc   +1 more source

JDroid: Android malware detection using hybrid opcode feature vector. [PDF]

PeerJ Comput Sci
Arslan RS.
europepmc   +1 more source

GSIDroid: A Suspicious Subgraph-Driven and Interpretable Android Malware Detection System. [PDF]

Sensors (Basel)
Huang H, Huang W, Jiang F.
europepmc   +1 more source