This interdisciplinary study develops a hybrid classification method that integrates qualitative analysis with classifier design for text and image analysis of online behavioral big data, which we call Qualitatively Augmented Text Classifier Algorithms (QATCA). This method will be developed and evaluated on an existing cyber intelligence platform that analyzes Dark Web activity undetectably and autonomously. The application of the method relies on computer support that not only aids qualitative analysis and classifier design but also ensures the integration of both components. Efficient and effective classification of online behavioral big data is an important tool for cybersecurity. The output of this study will contribute to the analytical techniques addressing (1) cyber conflict and warfare issues (e.g., monitoring of cyber-crime and cyber-terror activities and the potential criminal and terrorist behaviors); (2) security mechanisms, methodologies, and strategies (e.g., monitoring of online information leakages); and (3) online social networks and subversive behavior (e.g., automatic sentiment trend analysis).
Principal Investigators leading this project are Prof. Dov Te’eni and Dr. Inbal Yahav of Tel-Aviv University’s Coller School of Management, and Prof. David Schwartz of Bar-Ilan University’s Social Intelligence Lab.
The Blavatnik ICRC mission is to create a more secure world through science. The Center has demonstrated considerable achievements in research and outreach alike. However, ever-evolving cyber technologies will evoke new challenges as well as opportunities.
The Social Intelligence Lab of Bar-Ilan University is dedicated to leading edge research on social uses of information and communication technologies, the underlying nature of socially generated data, and algorithms that use both social data and network effects to improve society.