FL-MalDrift: a federated learning framework for malware detection under local concept drift. [PDF]
Sci RepPatel A +3 more
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Latent topic-driven cyber intelligence model for tactics, techniques, and procedures (TTPs) detection using hybrid framework and Birch-inspired optimisation. [PDF]
Sci RepAlanazi MM, Wahab AWA, Idris MYI.
europepmc +1 more source
A multi-label visualisation approach for malware behaviour analysis. [PDF]
Sci RepUysal DT +4 more
europepmc +1 more source
Graph-augmented multi-modal learning framework for robust android malware detection. [PDF]
Sci RepTanveer MU +5 more
europepmc +1 more source
AI-Driven Cybersecurity in IoT: Adaptive Malware Detection and Lightweight Encryption via TRIM-SEC Framework. [PDF]
Sensors (Basel)Mutambik I.
europepmc +1 more source
An integrated evolution-aware meta-learning framework with adversarial morphological augmentation for zero-day threat detections. [PDF]
Front Artif IntellLanka K, Shaik K.
europepmc +1 more source
Detection of unseen malware threats using generative adversarial networks and deep learning models. [PDF]
Sci RepJoshi C, Kumar J, Kumawat G.
europepmc +1 more source
, 2018 Traditionally, techniques for computing on encrypted data have been proposed with privacy preserving applications in mind. Several current cryptosystems support a homomorphic operation, allowing simple computations to be performed using encrypted values. This is sufficient to realize several useful applications, including schemes for electronic voting [
John Bethencourt +2 more
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Creating a Malware Analysis Lab and Basic Malware Analysis
, 2018 In tying together information learned in the Information Assurance program at Iowa State this paper goes over an introduction to malware, basic malware analysis, and setting up a manual malware analysis lab. Malware is malicious software that causes harm.
Peppers, Joseph
openaire +3 more sources