HICSS Symposium on Cybersecurity Big Data Analytics
Event submitted on Friday, November 29th 2019, approved by Content Team ✓
This event has been tagged as follows:
We recommend this for researchers and practitioners who want to learn more about Machine Learning. You can also share your most recent research to other professionals by submitting to their call for papers. This event is not specifically about Machine Learning because there are a lot of other topics so might want to check it out. Taking place in Hawaii, this conference certainly wins the stakes for being in one of the world’s most beautiful spots.
Conference Event Summary
The following description was either submitted by the Conference Organizer on Friday, November 29th 2019, or created by us.
This Symposium enables academics and practitioners to discuss the latest emerging cybersecurity big data research and challenges in domains such as processing and data collection, data handling, data analytics, machine learning, deep learning, and visualization; as well as to discuss and present potential cybersecurity big data research topics and methodologies of interest to the cybersecurity community.
Analytics and Machine Learning cover all forms that leverage or require Big Data for support, including defensive measures, potential threat identification applications, or deep learning opportunities. They also include, but are not limited to, techniques, methodologies, and impacts of real-time processing for incident detection and/or prevention, data review for incident and anomaly detection, post-incident response analytics, and IT audit-related analytics. Data handling covers research and case studies into processes and procedures.
Visualization will look at all aspects of research related to visualizing the data, such as temporal, geographical, threat, actor, event-based, and other data types. Topics related to data include areas such as logs, network traffic data (PCAP), system process data, system memory data, and even complete virtualized system snapshots.