* Research Sites *
Dr. Dipankar Dasgupta
333 Dunn Hall
Memphis, TN 38152-3240
phone: (901) 678-4147
fax: (901) 678-1506


Elevated to IEEE Fellow(Batch of 2015)
Distinguished ACM Speaker
Recipient of 2012 Willard R. Sparks Eminent Faculty Award.

Advisory Board Member of MIT in Cyber Security

Editorial Board of journals


* Principal Investigator *
Dr. Dasgupta will Organize IEEE Symposium on Computational Intelligence in Cyber Security (CICS 2017) at Hawaii, USA from November 27-December 1, 2017. Program Committee Member of the 1st IEEE International Workshop on Cyber Resiliency Economics (CRE 2016) , Vienna, Austria, August 1-3, 2016. Prof. Dasgupta will give an invited talk at the Computer Science Department, University of Tennessee, Knoxville, TN, April 7, 2016     Prof. Dasgupta will present a research paper at 11th Annual Cyber and Information Security Research (CISR) Conference will be held at the conference center at Oak Ridge National Laboratory, Oak Ridge, TN, April 4 - 6, 2016.     Prof. Dasgupta will give invited talk at Regional Symposium "Graduate Education and Research in Information Security",'GERIS'16, on March 8, 2016, at Binghamton University,Binghamton, New York.     Announcement for the available position in Research Assitant Professor (in Cyber Security)     Prof. Dasgupta was interviewed by a local TV Channel (FOX 13) and telecast on Feb. 19, 2016. Click here for Video.     Organized "Cybersecurity Certificate Course" foundational program at FedEx Institute of Technology,UofM, February 1-5, 2016.     Prof. Dasgupta gave an invited talk on 5th International Conference on Fuzzy and Neural Computing, FANCCO-2015, December 16-19, 2015.     Cluster to Advance Cyber Security & Testing (CAST) hosted Cybersecurity Lightning Talks at the FedEx Institute of Technology, afternoon of December 3, 2015     CfIA Receives Cyber Security Training Grant from FEMA     UofM's CfIA Will Develop Course for Mobile Device Security and Privacy Issues     Prof. Dasgupta gave an invited talk on Adaptive Multi-Factor Authentication at the Department of Electrical Engineering and Computer Science and CASE Center, Syracuse University, Syracuse, NY 13224-5040 November 18, 2015     Organize a Symposium on Computational Intelligence in Cyber Security (CICS) at IEEE Symposium Series on Computational Intelligence (SSCI,), December 7-10, 2015 at Cap Town, South Africa     Gave keynote speech at St. Louis at Cyber Security workshop (STL-CyberCon), University of Missouri-St. Louis, November 20, 2015     Prof. Dasgupta attended the NIST-NICE conference at San Diego from November 1-4, 2015     Prof. Dasgupta gave an invited talk at 9th International Research Workshop on Advances and Innovations in Systems Testing at FedEx Institute of Technology, the University of Memphis, October 20, 2015     Our Cyber Security Team got a second position on Cyber Defense Competition @CANSec 2015, held on 24th October at University of Arkansas at Little Rock

Fault Detection in Manufacturing using an Immunologically Inspired Technique

Project Objective:

The objective of this research is to develop an efficient fault detection algorithm based on immunological principles. From an information-processing perspective, the immune system is a remarkable, parallel and distributed adaptive system. It uses learning, memory, and associative retrieval to solve recognition and classification tasks (of its defense mechanism). The proposed immunity-based fault detection algorithm is a probabilistic method that uses a negative selection mechanism to detect any changes in the normal behavior of a monitored system.

Proposed Research:

Manufacturers are always looking for ways to improve productivity without compromising on quality of manufacturing processes. To this end, much attention has been directed towards automated manufacturing. Detecting fault or anomaly in sensory measurements is a problem of great practical interest in many manufacturing and signal processing applications, where it is necessary to detect anomalies or imperfections in system or process behavior. For example, in drilling or high-speed milling industries, on-line monitoring of the tool breakage is a key component in unmanned machining operations. In safety-critical applications, it is essential to detect the occurrence of unnatural events as quickly as possible before any significant performance degradation results. This can be done by continuous monitoring of the system for changes from the normal behavior patterns.

We have experimented with several data sets including some real sensory data and the initial results are very encouraging. In one such experiments, we applied the algorithm for Tool Breakage Detection in a milling operation. In this implementation, the tool breakage detection problem is formulated as the problem of detecting temporal changes in the cutting force pattern that results from a broken cutter. That is, the new data patterns are monitored to check for whether or not the current pattern is different from the established normal pattern, where a difference implies a shift in the cutting force dynamics. The detection algorithm was successful in detecting the existence of broken teeth from simulated cutting force signals in a milling process.

The goal of this work is to develop an efficient detection algorithm that can be used to alert an operator to any changes in steady-state characteristics of a monitored system. This approach collects knowledge about the normal behavior of a system from an historical data set, and generates a set of detectors that probabilistically notice any deviation from the normal behavior of the system. The detection system can be updated by generating a new set of detectors as the normal system behavior shifts due to aging, system modifications, change in operating environments, etc.