Machine Learning for Network Attacks Classification and Statistical Evaluation of Machine Learning for Network Attacks Classification and Adversarial Learning Methodologies for Synthetic Data Generation

Authors: Iakovos-Christos Zarkadis, Christos Douligeris | Published: 2026-03-18

rSDNet: Unified Robust Neural Learning against Label Noise and Adversarial Attacks

Authors: Suryasis Jana, Abhik Ghosh | Published: 2026-03-18

DDH-based schemes for multi-party Function Secret Sharing

Authors: Marc Damie, Florian Hahn, Andreas Peter, Jan Ramon | Published: 2026-03-18

Caging the Agents: A Zero Trust Security Architecture for Autonomous AI in Healthcare

Authors: Saikat Maiti | Published: 2026-03-18

80種類のAIエージェントのセキュリティ脅威を網羅的に整理「AIエージェント攻撃手法と対策一覧 …

ニュースソース: prtimes.jp公開日: 2026-03-17ニュース記事を読む: AIにより推定された記事に関連する負の影響 AIセキュリティマップ では、AIが情報システムに与える影響(情報システム的側面)、人や社会に与える影響(...

Federated Computing as Code (FCaC): Sovereignty-aware Systems by Design

Authors: Enzo Fenoglio, Philip Treleaven | Published: 2026-03-18

Network- and Device-Level Cyber Deception for Contested Environments Using RL and LLMs

Authors: Abhijeet Sahu, Shuva Paul, Rochard Macwan | Published: 2026-03-18

Deanonymizing Bitcoin Transactions via Network Traffic Analysis with Semi-supervised Learning

Authors: Shihan Zhang, Bing Han, Chuanyong Tian, Ruisheng Shi, Lina Lan, Qin Wang | Published: 2026-03-18

SAMSEM — A Generic and Scalable Approach for IC Metal Line Segmentation

Authors: Christian Gehrmann, Jonas Ricker, Simon Damm, Deruo Cheng, Julian Speith, Yiqiong Shi, Asja Fischer, Christof Paar | Published: 2026-03-17

Rotated Robustness: A Training-Free Defense against Bit-Flip Attacks on Large Language Models

Authors: Deng Liu, Song Chen | Published: 2026-03-17