Advancing Machine Learning Strategies for Power Consumption-Based IoT Botnet Detection. [PDF]
Wakili AA +4 more
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BlockDroid: detection of Android malware from images using lightweight convolutional neural network models with ensemble learning and blockchain for mobile devices. [PDF]
Şafak E +3 more
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Improving malware detection performance using hybrid deep representation learning with heuristic search algorithms. [PDF]
Anuradha A, Chouhan AS, Srinivas Rao S.
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Digital twins for building industrial metaverse. [PDF]
Lyu Z, Fridenfalk M.
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A hierarchical deep learning framework with doubly regularized loss for robust malware detection and family categorization. [PDF]
Abed Alsaedi S +6 more
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BioTrojans: viscoelastic microvalve-based attacks in flow-based microfluidic biochips and their countermeasures. [PDF]
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Design and Modeling of a Terahertz Transceiver for Intra- and Inter-Chip Communications in Wireless Network-on-Chip Architectures. [PDF]
Paudel B, Li XJ, Seet BC.
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Trustworthy Hardware: Identifying and Classifying Hardware Trojans
Computer, 2010For reasons of economy, critical systems will inevitably depend on electronics made in untrusted factories. A proposed new hardware Trojan taxonomy provides a first step in better understanding existing and potential threats.
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On Parametric Hardware Trojans
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