Process control block information dataset: Towards android malware detection. [PDF]
Alawneh H, Alkofahi H.
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Transfer learning and AI technology for family school community collaborative model research in university network security management. [PDF]
Feng Q, Chen Y.
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Enhancing virtual physically unclonable function security through neuron-criticality analysis and lightweight encryption. [PDF]
Khan R +4 more
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Efficient feature ranked hybrid framework for android Iot malware detection. [PDF]
Saeed NH +3 more
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Image Super-Resolution Reconstruction Network Based on Structural Reparameterization and Feature Reuse. [PDF]
Li T, Jin X, Liu Q, Liu X.
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OntoSecAI: Ontology-driven security automation for AI-enabled systems. [PDF]
Ullah U, Haleem M, Ullah A.
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The Lucy Thermal Emission Spectrometer (L'TES) Instrument. [PDF]
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Enhancing ransomware defense: deep learning-based detection and family-wise classification of evolving threats. [PDF]
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Exploring possible vulnerabilities for making hardware Trojans helps designers to improve the security and trust of integrated circuits (ICs). This paper discusses the hardware Trojan possibility in SRAM to evaluate the security of SRAM and evaluates the effectiveness of existing detection methods.
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Implementing hardware Trojans: Experiences from a hardware Trojan challenge
2011 IEEE 29th International Conference on Computer Design (ICCD), 2011Hardware Trojans have become a growing concern in the design of secure integrated circuits. In this work, we present a set of novel hardware Trojans aimed at evading detection methods, designed as part of the CSAW Embedded System Challenge 2010. We introduced and implemented unique Trojans based on side-channel analysis that leak the secret key in the ...
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