Zobrazeno 1 - 10
of 299
pro vyhledávání: '"Watters, Paul A."'
Autor:
Susnjak, Teo, McIntosh, Timothy R., Barczak, Andre L. C., Reyes, Napoleon H., Liu, Tong, Watters, Paul, Halgamuge, Malka N.
In this study, we explored the progression trajectories of artificial intelligence (AI) systems through the lens of complexity theory. We challenged the conventional linear and exponential projections of AI advancement toward Artificial General Intel
Externí odkaz:
http://arxiv.org/abs/2407.03652
Autor:
McIntosh, Timothy R., Susnjak, Teo, Liu, Tong, Watters, Paul, Nowrozy, Raza, Halgamuge, Malka N.
This study investigated the integration readiness of four predominant cybersecurity Governance, Risk and Compliance (GRC) frameworks - NIST CSF 2.0, COBIT 2019, ISO 27001:2022, and the latest ISO 42001:2023 - for the opportunities, risks, and regulat
Externí odkaz:
http://arxiv.org/abs/2402.15770
Autor:
McIntosh, Timothy R., Susnjak, Teo, Arachchilage, Nalin, Liu, Tong, Watters, Paul, Halgamuge, Malka N.
The rapid rise in popularity of Large Language Models (LLMs) with emerging capabilities has spurred public curiosity to evaluate and compare different LLMs, leading many researchers to propose their own LLM benchmarks. Noticing preliminary inadequaci
Externí odkaz:
http://arxiv.org/abs/2402.09880
This comprehensive survey explored the evolving landscape of generative Artificial Intelligence (AI), with a specific focus on the transformative impacts of Mixture of Experts (MoE), multimodal learning, and the speculated advancements towards Artifi
Externí odkaz:
http://arxiv.org/abs/2312.10868
This position paper explores the broad landscape of AI potentiality in the context of cybersecurity, with a particular emphasis on its possible risk factors with awareness, which can be managed by incorporating human experts in the loop, i.e., "Human
Externí odkaz:
http://arxiv.org/abs/2310.12162
Publikováno v:
Future Internet 2023, 15(6), 214
Malware authors apply different techniques of control flow obfuscation, in order to create new malware variants to avoid detection. Existing Siamese neural network (SNN)-based malware detection methods fail to correctly classify different malware fam
Externí odkaz:
http://arxiv.org/abs/2110.13409
Publikováno v:
In Child Abuse & Neglect September 2024 155
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