Research Interests

Secure peer-to-peer systems, security in cloud computing, application security, data security, network security and privacy, data mining and algorithms.

Research Experience

Ph.D.

Research Assistant, Software Security Lab & Data and Applications Security Lab

Department of Computer Science, University of Texas at Dallas

Research area: Secure peer-to-peer Systems and Cloud Computing security.

Details: Currently I'm working on a NSF-funded project related to decentralized P2P network that supports integrity and confidentiality labeling of shared data where security labels are global but the implementation does not require a centralized label server. A notion of data ownership privacy is also enforced, whereby peers can share data without revealing which data they own. The network employs a reputation-based trust management system to assess and update data labels, and to store and retrieve labels safely in the presence of malicious peers. The security labeling scheme preserves the efficiency of network operations too. My future work most probably is going to be Cloud Computing where we will try to apply our current project's security techniques.

M.S.

Graduate Research Assistant, The Information Security Lab (iSec)

Department of Computer Science and Engineering, University of Texas at Arlington

Research area: Network security and privacy.

Details: I worked particularly on anonymity in large-scale peer-to-peer systems. I was involved in a NSF-funded network security project Salsa developing new architectures for online privacy. I, as a part of the team, designed dynamic algorithms for managing the complex system for Salsa. Then I implemented them and verified in simulation. We have submitted the entire project work for Network Protocols and Algorithms journal.

Thesis Title: The Dynamics of Salsa: A Structured Approach to Large-Scale Anonymity.

Abstract: Anonymity provides a technical solution for protecting one's online privacy. Highly distributed peer-to-peer (P2P) anonymous systems should have better distribution of trust and more scalability than centralized approaches, but existing systems are vulnerable to attacks as they require nodes to have global knowledge of the system. To overcome these problems and to provide more secure, distributed organization for P2P anonymity systems, prior work proposed the Salsa system. Salsa is designed to select nodes to be used in anonymous circuits randomly from the full set of nodes, even though each node has knowledge of only a small subset of the network. It uses randomness, redundancy and bounds checking while performing lookups to prevent malicious nodes from returning false information without detection. In this thesis, we further investigate the dynamics of the Salsa system and propose to handle important system-level functionalities without giving advantages to attackers. We propose algorithms for joining and leaving of a node, splitting a group when its size reaches a maximum threshold, merging of two groups when a group?s size reaches a minimum threshold and updating global contacts of nodes locally. We have introduced more randomness in the lookup procedure and made bounds checking more flexible. Finally, we implemented these dynamic events and developed a complete continuous time simulator for Salsa. Using this simulator, we present simulation results that show that still Salsa continues to have good lookup success in a dynamic environment with modest overheads in the system. These results also demonstrate the stability of Salsa in the presence of many peers joining and leaving.

B.Sc.

Department of Computer Science and Engineering, University of Dhaka, Bangladesh

Research area: Data Mining.

Details: Actually this was the part of my final year project in my undergrad. I, leading a team of three members and under the supervision of my advisor, worked on some frequent pattern generation algorithms, simulated them, compared and analyzed them. We also worked on a medical image classification technique. We published three international conference papers on our research work. 

Journal Paper

[J.1] S. M. Khan, N. Mallesh, A. Nambiar and M. Wright, The Dynamics of Salsa: A Robust Structured P2P
        System , in International Journal of Network Protocols and Algorithms (NPA), December, 2010.

Conference Papers

[C.1] S. M. Khan, M. R. Islam, , M. U. Chowdhury, Medical Image Classification Using an Efficient Data Mining

        Technique, in Proceedings of International Conference on Machine Learning and Applications (ICMLA'04),

         Louisville, KY, USA, December, 2004.

[C.2] M. R. Islam, S. M. Khan, M. Asad-uz-zaman, S. S. K. Robin, A Comparative Study among Three

        Algorithms for Frequent Pattern Generation, in Proceedings of International Conference on Machine

        Learning and Applications (ICMLA'04), Louisville, KY, USA, December, 2004.

[C.3] M. A. H. Khan, M. A. N. Siddique, M. Asad-uz-zaman, S. M. Khan, S. S. K. Robin, A different approach to

        barrier synchronization mechanism for the BSP model on message-passing architectures, in Proceedings

        of International Conference on Virtual Systems and Multimedia (VSMM 2004), Softopia, Ogaki City, Japan,

        November, 2004.

[C.4] M. R. Islam, S. M. Khan, S. S. K. Robin, M. Asad-uz-zaman, A Comparative Study among Algorithms for

        Frequent Pattern Generation in Data Mining, in Proceedings of IASTED International Conference on

        Artificial Intelligence and Soft Computing (ASC 2004), Marbella, Spain, September, 2004.

Workshop

[W.1] S. M. Khan, M. Wright, Salsa: a Structured Approach to Large-Scale Anonymity, a poster presentation in

         the DIMACS/DyDan Workshop on Mathematical and Computational Methods for Information Security,

         Houston, Texas, USA, December, 2007.