NIST UNIVERSITY
Institute Park, Berhampur, Odisha-761008, India

Ashalata Panigrahi

Dr. Ashalata Panigrahi

Associate Professor

MCA (SCHOOL OF ENGINEERING)

ashalata.panigrahi@nist.edu
7855001224
OCT - 206

Education

M.Sc in Mathematics
Berhampur University
1993-06-10
M.Ed in Education
Berhampur University
1998-05-15
MCA in Distributed Database System
Indira Gandhi National Open University ( IGNOU)
2004-06-18
M.Tech in Data Mining, Soft Computing
Berhampur University
2008-05-10
Ph.D. in Computer Science in Soft Computing
Berhampur University
2015-12-09

Work Experience

Part Time Lecturer
Uma Charan Patnaik Engineering School
02/03/2005 - 31/08/2006
Part Time Lecturer
Government Polytechnic
Lecturer
SMIT
Associate Professor
SMIT
01/09/2006 - 30/07/2014
Guest Faculty for MCA
Khallikote Autonomous College
05/08/2016 - 06/05/2017
Associate Professor
Roland Institute of Technology
17/07/2019 - 12/04/2021
Professor
Roland Institute of Technology
13/04/2021 - 31/01/2024

Research Interests

  • Areas of interest: Cybersecurity, Artificial Intelligence, Machine Learning

Publications

  1. Panigrahi, A. and Patra, M.R., 2017. Network intrusion detection model based on fuzzy-rough classifiers. In Handbook of neural computation (pp. 109-125). Academic Press.
  2. Panigrahi, A. and Patra, M.R., 2024. Evaluating the efficacy of decision tree-based machine learning in classifying intrusive behaviour of network users. International Journal of Advanced Technology and Engineering Exploration, 11(114), p.736.
  3. Panigrahi, A., Augmenting Search based Feature Selection to Enhance Efficacy of Bayesian Classifiers for Building Network Intrusion Detection Models.
  4. Panigrahi, A. and Patra, M.R., 2024. Application of Neural Network-Based Techniques to Network Intrusion Detection. In Machine Learning for Real World Applications (pp. 131-150). Singapore: Springer Nature Singapore.
  5. Panigrahi, A. and Patra, M.R., 2018. A layered approach to network intrusion detection using rule learning classifiers with nature-inspired feature selection. In Progress in Computing, Analytics and Networking: Proceedings of ICCAN 2017 (pp. 215-223). Singapore: Springer Singapore.
  6. Panigrahi, A. and Patra, M.R., 2015, December. Performance Evaluation of Rule Learning Classifiers in Anomaly Based Intrusion Detection. In Computational Intelligence in Data Mining—Volume 2: Proceedings of the International Conference on CIDM, 5-6 December 2015 (pp. 97-108). New Delhi: Springer India.
  7. Panigrahi, A. and Patra, M.R., 2015, February. An evolutionary computation based classification model for network intrusion detection. In International Conference on Distributed Computing and Internet Technology (pp. 318-324). Cham: Springer International Publishing.
  8. Panigrah, A. and Patra, M.R., 2016. Fuzzy rough classification models for network intrusion detection. Transactions on machine learning and artificial intelligence, 4(2), pp.7-22.
  9. Panigrahi, A. and Ranjan, M., IJNSA7315nsa02.pdf.
  10. Patro, S.K., Nayak, C.K., Panigrahi, A. and Mali, S., 2025, May. Efficacy of Tree-Based Classification Models for Intrusion Detection in IoT Networks. In 2025 International Conference in Advances in Power, Signal, and Information Technology (APSIT) (pp. 1-6). IEEE.
  11. Panigrahi, A. and Patra, M.R.,, 2024, Evaluating the efficacy of decision tree-based machine learning in classifying intrusive behaviour of network users
  12. Panigrahi, A. and Patra, M.R., 2024, Application of Neural Network-Based Techniques to Network Intrusion Detection
  13. Maharana, M, and Panigrahi, A., 2025, IDS Model for Detecting IoT Network Attacks
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