Results 111 to 120 of about 46,394 (182)

FL-MalDrift: a federated learning framework for malware detection under local concept drift. [PDF]

Sci Rep
Patel A, Tomar DS, Pateriya RK, Haripriya R.   +3 more
europepmc   +1 more source

A Kullback-Liebler divergence-based representation algorithm for malware detection. [PDF]

PeerJ Comput Sci, 2023
Aboaoja FA, Zainal A, Ghaleb FA, Alghamdi NS, Saeed F, Alhuwayji H.   +5 more
europepmc   +1 more source

Vulnerability and Malware Detection

International Journal for Research in Applied Science and Engineering Technology
The increasingly linked digital world oftoday has made cybersecurity a top priority. The rise in cyber dangers, notably vulnerabilities and malware, poses major risks to individuals, organizations, and governments. This review article offers a thorough analysis of the approachesand instruments currently in use for malware and vulnerability detection ...
openaire   +1 more source

Integrating NLP and Ensemble Learning into Next-Generation Firewalls for Robust Malware Detection in Edge Computing. [PDF]

Sensors (Basel)
Moila RL, Velempini M.
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

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

Android malware detection with MH-100K: An innovative dataset for advanced research. [PDF]

Data Brief, 2023
Bragança H, Rocha V, Barcellos L, Souto E, Kreutz D, Feitosa E.   +5 more
europepmc   +1 more source

Deep Android Malware Detection

, 2017
In this paper, we propose a novel android malware detection system that uses a deep convolutional neural network (CNN). Malware classification is performed based on static analysis of the raw opcode sequence from a disassembled program. Features indicative of malware are automatically learned by the network from the raw opcode sequence thus removing ...
McLaughlin, Niall; id_orcid 0000-0002-0917-9145, Martinez del Rincon, Jesus; id_orcid 0000-0002-9574-4138, Kang, BooJoong, Yerima, Suleiman, Miller, Paul, Sezer, Sakir, Safaeisemnani, Yeganeh, Trickel, Erik, Zhao, Ziming, Doupé, Adam, Joon Ahn, Gail   +10 more
openaire   +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

File-level malware detection using byte streams. [PDF]

Sci Rep, 2023
Jeong YS, Mswahili ME, Kang AR.
europepmc   +1 more source