Dr. Muhammad Akram Shaikh
- Super User
- Wednesday, 13 November 2013 11:45
MSc (Software Development), BE (Mechanical Engineering), MBA (Marketing), Fellow (KOICA) Korea.
Prof. Dr. Muhammad Akram Shaikh is working as Director General in Pakistan Scientific & Technology Information Centre (PASTIC), a subsidiary of Pakistan Science Foundation under Ministry of Science & Technology. He remained Professor in the Department of Software Engineering, & Co-Director Institute of Information & Communication Technologies Mehran University of Engineering & Technology Jamshoro. He has received his B.E. from Mehran University (Pakistan) in 1993, MBA from University of Sindh in 1996, MSc. from University of Huddersfield (UK) in 2001, and Ph.D. from Tsinghua University (China) in 2008. He is the life time member of Pakistan Engineering Council. He started his professional career in 1993. He has 20 years of teaching, research & management/admin experience. He is author of more than 25 journal/conference papers of national/international repute. In addition he is also attached as editor/ co-editor/ reviewer of national/international journals, Session chair/ PC member of national/international conferences, member accreditation committee PEC, member national curriculum review committees of HEC, member technical review committee of PSF, member HEC digital library advisory board, and member board of studies of various universities of Pakistan.
His research areas of interest include Knowledge Engineering, Scientific & Technological Databases, Data Mining & Data Warehousing, Software Engineering, Automation & Control, Networks, Virtual Reality and Graphics.
Title: Network Structure Mining and its Application in Analyzing Covert Networks
Abstract: Knowing patterns of relationship in covert (illegal) networks is very useful for law enforcement agencies and intelligence analysts to investigate collaborations among criminals. Previous studies in network analysis have mostly dealt with overt (legal) networks with transparent structures. Unlike conventional data mining that extracts patterns based on individual data objects, network structure mining is especially suitable for mining a large volume of association data to analyze and discover hidden structural patterns, relationships, subgroups and important actors and thereby give disruption in covert networks. Covert networks share some features with conventional (real world) networks, but they are harder to identify because they mostly hide their illicit activities. In the study of covert networks and destabilization strategies thereof, much attention has been paid to the task of locating and isolating key individuals within the organization. The isolation act was considered successful if it resulted in the network being separated into disconnected subparts. After the September 11, 2001 attacks, social network analysis (SNA) has increasingly been used to study criminal networks. However, finding out who is related to whom on a large scale in a covert network is a complex problem. In this talk we will discussed how network structure mining is applied in the domain of covert networks using multi-disciplinary theoretical knowledge from social network analysis (SNA), graph theory, statistics, and web structural mining research and proposed some useful techniques.