This Workshop was held in conjunction with the 2018 IEEE International Conference on Big Data (IEEE Big Data 2018), December 13, 2018, Seattle, WA, USA
Workshop schedule for December 13th, 2018: BigCyber2018-Detailed
Security analysts need to process high velocity and veracious data for early, ideally left of an exploit, detection of cybersecurity events, such as attacks, data-theft, etc. The problem is challenging given the constantly evolving threat landscape. Even with advanced monitoring, sophisticated persistent attackers can spend as many as 146 days in a system before being detected. Existing systems’ lack of unified organizational view causes information flooding and overwhelms a security analyst with false alarms. We need techniques that reduces an analyst’s cognitive load.
Big data crossing the organizational boundary even in mid-sized environments, need to be mined, examined, analyzed to create ‘Analyst Augmentation Systems’ which will aid security analysts in their day to day operations.
This workshop aims to bring together researchers from Cybersecurity and Big Data to help further homeland security’s missions of anticipation, interdiction, prevention, preparedness, and response. We invite submissions in areas (but not limited to) related to knowledge extraction from cybersecurity intelligence big datasets, fast analysis of security datasets for relevant information, and using this knowledge for various cybersecurity activities like early attack detection, mitigation, remediation, and forensics.
Submit a paper to the workshop here.
Tweet to us at @BigCyber2018.
Program Chairs and co-chairs
Co-chair: Bhavani Thuraisingham, Executive Director of the Cyber Security Research Institute and Louis A. Beecherl, Jr. Distinguished Professor, University of Texas, Dallas, USA .
Co-chair: Claudia Pearce, United States Department of Defense, USA.
Program Committee Members
The program committee includes cybersecurity experts and researchers from throughout the globe –
- Ranjan Bose, Director, Center of Excellence in Cyber Systems and Information Assurance, Microsoft Chair Professor, Indian Institute of Technology, Delhi, India.
- George Roelke, Innovation Area Lead, Cyber, The MITRE Corporation
- Yelena Yesha, Director, Center Director of Center of Accelerated Real Time Analytics (CARTA) I/UCRC, Director of the National Science Foundation’s Center for Hybrid Multicore Productivity and Research (CHMPR) and the Professor of Computer Science and Electrical Engineering, University of Maryland Baltimore County, USA.
- Kouichi Sakurai, Kyushu University, Japan.
- Vijay Atluri, Research Director, Center for Information Management, Integration and Connectivity, Rutgers University, USA.
- Seung Geol Choi, Assistant Professor, United States Naval Academy, Annapolis, USA.
- Youngja Park, IBM T.J. Watson Research Center, USA
- Wenjia Li, Assistant Professor, New York Institute of Technology, USA
Sudip Mittal (UMBC, USA)
Sandeep Nair (UMBC, USA)
Nilavra Pathak (UMBC, USA)
Big Data Stream Analytics for Cyber Security
Professor Latifur Khan
Department of Computer Science, University of Texas at Dallas, USA
Data streams are continuous flows of data. Examples of data streams include network traffic, sensor data, call center records and so on. Data streams demonstrate several unique properties that together conform to the characteristics of big data (i.e., volume, velocity, variety and veracity) and add challenges to data stream mining. In this talk we will present an organized picture on how to handle various data mining techniques in data streams. In addition, we will present a number of stream classification applications such as adaptive website fingerprinting, textual stream analytics (political actor identification over textual stream), attack trace classification and secure data analytics using trusted execution environment (TEE).
This research was funded in part by NSF, NASA, Air Force Office of Scientific Research (AFOSR), NSA, IBM Research, HPE and Raytheon.
Dr. Latifur Khan is currently a full Professor (tenured) in the Computer Science department at the University of Texas at Dallas, USA where he has been teaching and conducting research since September 2000. He received his Ph.D. degree in Computer Science from the University of Southern California (USC) in August of 2000.
Dr. Khan is an ACM Distinguished Scientist and received Fellow of SIRI (Society of Information Reuse and Integration) award in Aug, 2018. He has received prestigious awards including the IEEE Technical Achievement Award for Intelligence and Security Informatics and IBM Faculty Award (research) 2016.
Dr. Latifur Khan has published over 250 papers in premier journals such as VLDB, Journal of Web Semantics, IEEE TDKE, IEEE TDSC, IEEE TSMC, and AI Research and in prestigious conferences such as AAAI, IJCAI, CIKM, ICDE, ACM GIS, IEEE ICDM, IEEE BigData, ECML/PKDD, PAKDD, ACM Multimedia, ACM WWW, ICWC, ACM SACMAT, IEEE ICSC, IEEE Cloud and INFOCOM. He has been invited to give keynotes and invited talks at a number of conferences hosted by IEEE and ACM. In addition, he has conducted tutorial sessions in prominent conferences such as SIGKDD 2017, 2016, IJCAI 2017, AAAI 2017, SDM 2017, PAKDD 2011 & 2012, DASFAA 2012, ACM WWW 2005, MIS2005, and DASFAA 2007.
Currently, Dr. Khan’s research area focuses on big data management and analytics, data mining and its application over cyber security, complex data management including geo-spatial data and multimedia data. His research has been supported by grants from NSF, the Air Force Office of Scientific Research (AFOSR), DOE, NSA, IBM and HPE. More details can be found at: www.utdallas.edu/~lkhan/
Workshop schedule for December 13th, 2018: BigCyber2018-Detailed
- Robustness of Compressed Convolutional Neural Networks, Arie Wahyu Wijayanto, Jun Jin Choong, Kaushalya Madhawa, and Tsuyoshi Murata
- Determining Viability of Deep Learning on Cybersecurity Log Analytics, Casey Lorenzen, Rajeev Agrawal, and Jason King
- Data Security and Privacy Protection in Public Cloud, Yue Shi
- Mobile Phone User Behavior Prediction base on multivariable Linear Regression Model, Yang Qin, Peiling Yuan, and Qin Zhang
- Identifying Bipartite Subgraphs for Community Detection in Very Large Scale Cyber Networks, Harsha Deshmukh and John Springer
- A Framework for Making Effective Responses to Cyberattacks, Nicholas Herald and Michael David
- Analyzing False Positive Source Code Vulnerabilities Using Static Analysis Tools, Foteini Cheirdari and George Karabatis,
- Reputation-Aware Data Fusion and Malicious Participant Detection in Mobile Crowdsensing, Yujian ‘Charles’ Tang, Samia Tasnim, Niki Pissinou, S. S. Iyengar, and Abdur Shahid