arXiv Machine Learning

Towards Improved Anomaly Detection for Cloud Cybersecurity via Graph Neural Networks

arXiv:2606. 28923v1 Announce Type: new Abstract: Detecting security threats in an organization's cloud computing environment has become necessary due to the increased reliance on cloud infrastructure.

arXiv Machine Learning
Jul 31

ARES: Anomaly Recognition Model For Edge Streams

arXiv:2511. 22078v2 Announce Type: replace Abstract: Many real-world scenarios involving streaming information can be represented as temporal graphs, where data flows through dynamic changes in edges over time.

By Simone Mungari, Albert Bifet, Giuseppe Manco, Bernhard Pfahringer
arXiv Machine Learning
Jul 21

ClouDens: Operational Context-Aware Anomaly Detection for Large-scale Cloud System Monitoring

arXiv:2607. 18127v1 Announce Type: cross Abstract: With the rapid growth of cloud computing infrastructures in scale and complexity, network monitoring for Large-scale Cloud Systems (LCSs) has become increasingly challenging, requiring automated and reliable anomaly detection to maintain service availability.

By Thu T. H. Doan, Mohammad Saiful Islam, Andriy Miranskyy, Ngoc-Thanh Nguyen, Rogardt Heldal, Patrizio Pelliccione
Hugging Face Trending Papers
Aug 12

Machine Learning-Based Cyber Defense for Cloud Infrastructure: An Adaptive Deep Q-Network Architecture for Intelligent Intrusion Detection and Automated Threat Mitigation

With the increasing complexity of cyber assaults in cloud environments, adaptable security solutions are needed that can support real-time detection and autonomous response. In this paper, we propose a reinforcement learning-based dynamic cyber defense framework.

arXiv AI
6d ago

Machine Learning-Based Cyber Defense for Cloud Infrastructure: An Adaptive Deep Q-Network Architecture for Intelligent Intrusion Detection and Automated Threat Mitigation

arXiv:2608. 12190v1 Announce Type: cross Abstract: With the increasing complexity of cyber assaults in cloud environments, adaptable security solutions are needed that can support real-time detection and autonomous response.

By Md Yassir Mottalib, Md Yousuf, Eklachur Rahman Bhuiyan, S M Ahsan Habib, Sonjoy Kumar Dey, Md. Salahuddin Gazi, Molay Kumar Roy, Asaduzzaman Anik