Zobrazeno 1 - 10
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pro vyhledávání: '"DUBIN, A"'
In condensed-matter physics, supersolids refer to many-body quantum states breaking translational symmetry while exhibiting off-diagonal long-range order. This combination is debated for commensurate crystals, however it is accessible in lattice pote
Externí odkaz:
http://arxiv.org/abs/2410.17162
Autor:
Gilkarov, Daniel, Dubin, Ran
The potential for exploitation of AI models has increased due to the rapid advancement of Artificial Intelligence (AI) and the widespread use of platforms like Model Zoo for sharing AI models. Attackers can embed malware within AI models through steg
Externí odkaz:
http://arxiv.org/abs/2409.19310
Ensuring the security of cloud environments is imperative for sustaining organizational growth and operational efficiency. As the ubiquity of cloud services continues to rise, the inevitability of cyber threats underscores the importance of preemptiv
Externí odkaz:
http://arxiv.org/abs/2409.12726
The increasing popularity of online services has made Internet Traffic Classification a critical field of study. However, the rapid development of internet protocols and encryption limits usable data availability. This paper addresses the challenges
Externí odkaz:
http://arxiv.org/abs/2407.16539
In causal inference, properly selecting the propensity score (PS) model is an important topic and has been widely investigated in observational studies. There is also a large literature focusing on the missing data problem. However, there are very fe
Externí odkaz:
http://arxiv.org/abs/2406.12171
How to deal with missing data in observational studies is a common concern for causal inference. When the covariates are missing at random (MAR), multiple approaches have been provided to help solve the issue. However, if the exposure is MAR, few app
Externí odkaz:
http://arxiv.org/abs/2406.08668
Many observational studies feature irregular longitudinal data, where the observation times are not common across individuals in the study. Further, the observation times may be related to the longitudinal outcome. In this setting, failing to account
Externí odkaz:
http://arxiv.org/abs/2405.15740
Web applications and APIs face constant threats from malicious actors seeking to exploit vulnerabilities for illicit gains. These threats necessitate robust anomaly detection systems capable of identifying malicious API traffic efficiently despite li
Externí odkaz:
http://arxiv.org/abs/2405.11258
Application Programming Interface (API) Injection attacks refer to the unauthorized or malicious use of APIs, which are often exploited to gain access to sensitive data or manipulate online systems for illicit purposes. Identifying actors that deceit
Externí odkaz:
http://arxiv.org/abs/2405.11247
This paper presents RADAR-Robust Adversarial Detection via Adversarial Retraining-an approach designed to enhance the robustness of adversarial detectors against adaptive attacks, while maintaining classifier performance. An adaptive attack is one wh
Externí odkaz:
http://arxiv.org/abs/2404.12120