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Ezhil Kalaimannan

photo of Dr. Ezhil Kalaimannan

Assistant Professor

Education:
B.E., Anna University (Chennai, India), 2006
M.S., University of Alabama in Huntsville, 2009
Ph.D., University of Alabama in Huntsville, 2014

Contact:
Phone (850) 473-7005
Office: Bldg 4, Room 241
Email: ekalaimannan@uwf.edu

Research Interests: Dr. Kalaimannan’s current research focuses on the security of cyber-physical systems (Electrical Smart Power Grids) and fingerprinting networked operating systems (OS’s) and Internet of Things (IoT) devices. Dr. Kalaimannan’s research has been published in prestigious journals such as Computers & Operations Research by Elsevier, Access by IEEE, Internet of Things (IoT) by IEEE, Security and Privacy by Wiley, and Security and Privacy by IEEE.

Current and Previous Projects:

Crime Scene Investigation [CSI] in Digital Forensics

In this research, we propose to develop efficient computational models and heuristic algorithms which can improve the overall effectiveness of a crime scene investigation procedure in Digital Forensics. The procedure of evidence analysis can be classified into two basic categories: Parallel and Sequential. In the parallel scenario, as soon as the people at the scene acquire the first evidence, it is being sent to the lab for analysis. In the meantime, people at the scene continue to acquire additional evidences. Examination of this evidence in a lab would help the people at the crime scene determine what additional evidences need to be collected and sent to the lab for further investigation. This process continues until the deadline of the investigation is reached. However, in the sequential scenario, the investigation in lab starts after all the evidences at the scene have been acquired. In view of the aforementioned scenarios, Mixed Integer Linear Programming [MILP] models for maximizing the overall effectiveness obtained from a crime scene investigation are developed. In addition, the computational complexity of these models are analyzed to validate their nature of NP-hardness through extensive experiments.

Intrusion Detection System [IDS] Alarm Analysis

Under this research, we propose to develop efficient computational models and heuristic algorithms to solve the problem of selecting optimal IDS alarms to be investigated in order to minimize total expected cost of investigations and the loss in value for non-investigations. Securing and defending computing networks has become a matter of growing importance attracting the attention of both practitioners and researchers. Among the suite of tools available to network managers to monitor and secure their networks are IDS; software and hardware systems designed and programmed to automate the process of monitoring networks and analyzing them for potential breaches. One of the major challenges presented by IDSs, is how do network managers prioritize and commit resources to investigate notification by an IDS of potential threats to the network. By developing MILP models for this problem, a novel is method is presented to illustrate how network managers can optimally allocate their limited resources for investigating IDS notifications.​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​

Publications:

  • Gupta, J.N.D., Kalaimannan, E, and Yoo, S.M. “A Sequential Investigation Model for Solving Time Critical Digital Forensic Cases Involving a Single Investigator," In: Choo KK., Morris T., Peterson G. (eds) National Cyber Summit (NCS) Research Track NCS 2019, Advances in Intelligent Systems and Computing, Vol 1055, Springer, Cham, 2020.
  • Mishra, A., Reichherzer, T., Kalaimannan, E., Wilde, N and Ramirez, R. “Trade-off’s involved in the choice of cloud service configurations when building secure, scalable, and efficient Internet-of-Things networks," International Journal of Distributed Sensor Networks, Sage Journal, Vol. 16, No. 2, pp. 1550147720908199, 2020.
  • Hopkins, S., Kalaimannan, E and John, C. “Foundations for Research in Cyber-Physical System Cyber Resilience using State Estimation," Proceedings of the 2020 IEEE SoutheastCon, pp. 1-2, Raleigh, NC, 2020.
  • Hopkins, S., Kalaimannan, E and John, C. “Sub-Erroneous Outlier Detection of Cyber Attacks in a Smart Grid State Estimation System," Proceedings of the 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), pp. 0447-0454, New York, NY, 2020.
  • Tetarave, S.K., Tripathy, S., Kalaimannan, E., John, C and Srivastava, A. “A Routing Table Poisoning Model for Peer-to-Peer (P2P) Botnets," Access, IEEE, Vol. 7, No. 1, pp. 67983-67995, 2019.
  • Hopkins, S, and Kalaimannan, E. “Towards establishing a security engineered SCADA framework," Journal of Cybersecurity Technology, Taylor & Francis, Vol. 3, No. 1, pp. 47-59, 2019.