learning
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ePrint Report: ClusterGuard: Secure Clustered Aggregation for Federated Learning with Robustness Yulin Zhao, Zhiguo Wan, Zhangshuang Guan Federated Learning (FL) enables collaborative model training while preserving data privacy by avoiding the sharing of raw data. However, in large-scale FL systems, efficient secure aggregation and dropout handling remain critical challenges. Existing state-of-the-art methods, such as those…
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arXiv:2412.16254v1 Announce Type: new Abstract: Adversarial attacks pose a significant threat to the reliability of pre-trained language models (PLMs) such as GPT, BERT, RoBERTa, and T5. This paper presents Adversarial Robustness through Dynamic Ensemble Learning (ARDEL), a novel scheme designed to enhance the robustness of PLMs against such attacks. ARDEL leverages the diversity of multiple…
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arXiv:2412.16264v1 Announce Type: new Abstract: Intrusion Detection Systems (IDS) are crucial for safeguarding digital infrastructure. In dynamic network environments, both threat landscapes and normal operational behaviors are constantly changing, resulting in concept drift. While continuous learning mitigates the adverse effects of concept drift, insufficient attention to drift patterns and excessive preservation of outdated knowledge can…
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arXiv:2412.16484v1 Announce Type: new Abstract: The vast majority of cybersecurity information is unstructured text, including critical data within databases such as CVE, NVD, CWE, CAPEC, and the MITRE ATT&CK Framework. These databases are invaluable for analyzing attack patterns and understanding attacker behaviors. Creating a knowledge graph by integrating this information could unlock significant insights. However,…
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Machine learning (ML) models are almost always developed in an offline setting, but they must be deployed into a production environment in order to learn from live data and deliver value. A common complaint among ML teams, however, is that deploying ML models in production is a complicated process. It is such a widespread issue…
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ePrint Report: Learning with Errors from Nonassociative Algebras Andrew Mendelsohn, Cong Ling We construct a provably-secure structured variant of Learning with Errors (LWE) using nonassociative cyclic division algebras, assuming the hardness of worst-case structured lattice problems, for which we are able to give a full search-to-decision reduction, improving upon the construction of Grover et al.…
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Hi, I am looking for datasets that contain CloudWatch Logs to practice threat hunting and incident response in the Cloud. I am aware of BOTSv3 but I am looking for recent practice datasets. Splunk does not release the latest BOTSv9. Thanks submitted by /u/KeySwim78 [link] [comments]
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Address Common Machine Learning Challenges With Managed MLflow – The New Stack
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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
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The threat landscape continues to evolve, and companies around the world face escalating risks heading into 2025. As AI enables more malware and phishing campaigns, and attacks become even more sophisticated across enterprises and supply chains, cybersecurity teams need to enhance their strategies to keep up with the dynamic and complex threat landscape. Here are…
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The arrival of AI has received a lukewarm welcome from teachers and parents – Copyright AFP/File EVARISTO SA
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FuzzDistill: Intelligent Fuzzing Target Selection using Compile-Time Analysis and Machine Learning
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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
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Unsurprisingly, lack of skills is cited as the biggest challenge. Issues around data governance and challenges around clear metrics follow the top challenge areas. All of these relate to the lack of experience with AI. As organisations embark on their journeys, they have to learn what is needed to ensure a successful project.
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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
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Jia-Lin Chan, Wai-Kong Lee, Denis C.-K Wong, Wun-She Yap, Bok-Min Goi ePrint Report Advancements in deep learning (DL) not only revolutionized many aspects in our lives, but also introduced privacy concerns, because it processed vast amounts of information that was closely related to our daily life. Fully Homomorphic Encryption (FHE) is one of the promising…