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of 695
pro vyhledávání: '"K.6.5"'
Autor:
Arora, Sunil, Hastings, John
This paper presents a multi-cloud networking architecture built on zero trust principles and micro-segmentation to provide secure connectivity with authentication, authorization, and encryption in transit. The proposed design includes the multi-cloud
Externí odkaz:
http://arxiv.org/abs/2411.12162
Autor:
Castro, Fernando Peralta
Cryptography, derived from Greek meaning hidden writing, uses mathematical techniques to secure information by converting it into an unreadable format. While cryptography as a science began around 100 years ago, its roots trace back to ancient civili
Externí odkaz:
http://arxiv.org/abs/2411.14451
Autor:
Wertenbroek, Rick, Dassatti, Alberto
Modern operating systems manage and abstract hardware resources, to ensure efficient execution of user workloads. The operating system must securely interface with often untrusted user code while relying on hardware that is assumed to be trustworthy.
Externí odkaz:
http://arxiv.org/abs/2411.00439
The audio watermarking technique embeds messages into audio and accurately extracts messages from the watermarked audio. Traditional methods develop algorithms based on expert experience to embed watermarks into the time-domain or transform-domain of
Externí odkaz:
http://arxiv.org/abs/2409.19627
Autor:
Chen, Jiachi, Zhong, Qingyuan, Wang, Yanlin, Ning, Kaiwen, Liu, Yongkun, Xu, Zenan, Zhao, Zhe, Chen, Ting, Zheng, Zibin
The emergence of Large Language Models (LLMs) has significantly influenced various aspects of software development activities. Despite their benefits, LLMs also pose notable risks, including the potential to generate harmful content and being abused
Externí odkaz:
http://arxiv.org/abs/2409.15154
We present a series of algorithms in tensor networks for anomaly detection in datasets, by using data compression in a Tensor Train representation. These algorithms consist of preserving the structure of normal data in compression and deleting the st
Externí odkaz:
http://arxiv.org/abs/2409.15030
This paper presents the design and implementation of a Federated Learning (FL) testbed, focusing on its application in cybersecurity and evaluating its resilience against poisoning attacks. Federated Learning allows multiple clients to collaborativel
Externí odkaz:
http://arxiv.org/abs/2409.09794
Autor:
Otal, Hakan T., Canbaz, M. Abdullah
The rapid evolution of cyber threats necessitates innovative solutions for detecting and analyzing malicious activity. Honeypots, which are decoy systems designed to lure and interact with attackers, have emerged as a critical component in cybersecur
Externí odkaz:
http://arxiv.org/abs/2409.08234
Fully Homomorphic Encryption (FHE) enables computations on encrypted data, preserving confidentiality without the need for decryption. However, FHE is often hindered by significant performance overhead, particularly for high-precision and complex dat
Externí odkaz:
http://arxiv.org/abs/2409.03568
With the emergence of widely available powerful LLMs, disinformation generated by large Language Models (LLMs) has become a major concern. Historically, LLM detectors have been touted as a solution, but their effectiveness in the real world is still
Externí odkaz:
http://arxiv.org/abs/2409.03291