Master of Science in Data Science (MSDS) student Chris Hainzl and his faculty advisor Dr. Sourav Dutta have published their research at the 12th IEEE International Conference on Cyber Security and Cloud Computing (IEEE CSCloud 2025). Their paper, titled \u201cDynamic Feature Clustering for Anomaly Detection in Streaming Cybersecurity Data,\u201d addresses one of the most pressing challenges in modern cybersecurity: detecting anomalous behavior in real time from continuous data streams such as firewall logs, intrusion detection systems, and network monitors.<\/p>\n
The research introduces Adaptive Feature Clustering for Streaming Data (AFCSD), a novel clustering approach that incrementally updates feature groupings by tracking covariance matrix drift. This dynamic method allows feature clusters to adapt as data distributions shift, enhancing both the accuracy and computational efficiency of anomaly detection in non-stationary environments.<\/p>\n
The publication of this work at a premier IEEE conference underscores the significant contributions Ramapo students and faculty are making in advancing scalable, real-time solutions to critical cybersecurity challenges.<\/p>\n
Congratulations to Chris and Dr. Dutta on this outstanding achievement!<\/p>\n","protected":false},"excerpt":{"rendered":"
Master of Science in Data Science (MSDS) student Chris Hainzl and his faculty advisor Dr. Sourav Dutta have published their research at the 12th IEEE International Conference on Cyber Security and Cloud Computing (IEEE CSCloud 2025). Their paper, titled \u201cDynamic Feature Clustering for Anomaly Detection in Streaming Cybersecurity Data,\u201d addresses one of the most pressing […]<\/p>\n","protected":false},"author":214,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[10],"tags":[],"class_list":["post-2058","post","type-post","status-publish","format-standard","hentry","category-cybersecurity"],"yoast_head":"\n