Faculty Details

• PhD in Design & Development of a Host Based Intrusion Detection Systems with Classification of Alerts, Sikkim Manipal University, Sikkim, India, 2020
• M.Tech in Computer Science and Engineering, Sikkim Manipal Institute of Technology, 2013

• Associate Professor at Sikkim Manipal Institute of Technology, Teaching, April 2024 to till date.
• Assistant Professor - SG at Sikkim Manipal Institute of Technology, Teaching, 02-06-2020 to March 2024
• Assistant Professor - I at Sikkim Manipal Institute of Technology, Teaching, 14-08-2015 to 01-06-2020
• Assistant Professor - II at Sikkim Manipal Institute of Technology, Teaching, 15-07-2010 to 13-08-2015
• System Analyst at Sikkim Manipal University, Software Development, 15-07-2010 to 13-08-2015

AREAS OF INTEREST, EXPERTISE AND RESEARCH

• Area of Interest Data Mining, Machine Learning, Wireless Sensor Network


• Area of Expertise Pattern Recognition, Machine Learning, Pattern Recognition


• Area of Research Machine Learning, Intrusion Detection, Biomedical Engineering, Signal Processing

PROFESSIONAL AFFILIATION & CONTRIBUTION

• ACM Computer Science Teachers Association (CSTA). Membership Number: 6415916, Url: www.csta.acm.org
• International Association of Engineers. Membership Number: 138857, Url: www.iaeng.org
• International Association of Computer Science and Information Technology (IACSIT). Membership Number: 80348183, Url: www.iacsit.org

Dr. Ranjit Panigrahi received his Master of Technology in Computer Sciences & Engineering at Sikkim Manipal Institute of Technology, Sikkim and PhD degree in Computer Applications from Sikkim Manipal University. At present, he is deputed as Assistant Professor - Selection Grade in Department of Computer Applications in Sikkim Manipal Institute of Technology, Sikkim, India. His area of research includes machine learning, biomedical engineering. He is also a certified Microsoft Technology Specialist. At Sikkim Manipal Institute of technology his roles and responsibilities include -

• Teaching BCA and MCA students
• Teacher Guardian of 15 students.
• Advisor - SMIT IT Council
• Member - ERP Implementation Committee

Big Data & Edge Intelligence for Enhanced Cyber Defense: Principles and Research

This book calls chapters based on recent state-of-the art edge artificial intelligence approaches used for enhanced cyber defense mechanisms to handle big data. Chapters should provide a glimpse of computational intelligence techniques and automated reasoning models capable of fast training and timely data processing of cybersecurity big data. Apart from edge artificial intelligence techniques, this book also calls chapters related to reviews of recent cyber threats and attacks and their countermeasures. Chapters solicited for this book are -

Chapter 1. A Brief introduction to cyber threats and related countermeasures

This chapter will be dealt with a brief introduction of Cyber Defense Systems that has been outlined highlighting the modern threats and attacks in various network environments such as host-based network-based, smart grid. An emphasis will be given to categorize and presents a detailed taxonomy of cyber threats according to the source of origination and location. A brief introduction of the latest available defense mechanisms is also discussed in this chapter. As an introductory chapter, these topics enlighten the readers with a brief idea of current threats found in the cyber arena.

Chapter 2. Edge Artificial Intelligence - An Overview

This chapter will be a brief introduction to Edge Artificial Intelligence. The chapter aims to deliver advantages of EdgeAI; viz., real-time operations, reduced power consumption, decreased data communication cost, and increased privacy. It is also intended to deliver the role of EdgeAI to handle cybercrimes and related threats. The role of EdgeAI will also be analyzed looking forward, how effectively it allows the network topology to be fault-tolerant even in the influence of attacks.

Chapter 3. Reviews of EdgeAI based Cyber Defense Mechanisms

This chapter will provide a brief survey of state-of-art EdgeAI based Cyber Defense Mechanisms (CDMs). Various CDMs are explored based on detection type and approaches. Emphasis will be given to those methods that integrate signature based CDMs in the process of detection. Moreover, many modern embedded EdgeAI based methods will be explored that acts as the front line of defense in the network environment. Further, a detailed taxonomy of CDMs datasets that are huge and contains recent attack trends will be explored elaborating the target environment.

Chapter 4 to Chapter 15. Contributory Chapters from prospective authors

All 11 chapters will be dealt with specific challenging areas pertaining to cyber defense through Edge AI. We are inviting chapters from you in the following 11 areas, including but not limited to

• Artificial Intelligence and Machine Learning for intrusion identification and prevention in vulnerable environments
• Improvising Digital forensic intelligence through EdgeAI techniques
• Digital Investigation, and forensic data analytics
• Proactive and Adaptive Defense of Network Infrastructure
• Artificial Intelligence (AI) based smart appliances and smart grid forensic
• Trustworthy infrastructures, services, and applications for sensing technologies
• Attacks detection and prediction based on deep and reinforcement learning
• Cryptocurrency-enabled crimes and countermeasures
• Cybersecurity Metrics and Assessment
• Bio-inspired cyber defense mechanisms
• AI-based security protocols to enhance cyber defense

Important Dates
Chapters Submission: August 30, 2021 (On or before)
Review Notification: September 30, 2021 (On or before)
Camera Ready Submission: October 30, 2021 (On or before)

Website | Indexing | Word Template | Submission Link

Do not hesitate to contact us at ranjit.panigrahi@gmail.com if you have any questions. Kindly mention "Big Data & Edge Intelligence for Enhanced Cyber Defense" on the subject line in all your email correspondences.

• Classification and Analysis of Facebook matrices dataset using supervised classifiers

  January 10, 2019 | Dr. Ranjit Panigrahi | Samarjeet Borah

  Social Network Analytics, Elsevier Inc. 2019, Pages 1-19, ISBN 9780128154588

  DOI: 10.1016/B978-0-12-815458-8.00001-3

• Rank of normalizers through TOPSIS with the help of supervised classifiers

  November 07, 2018 | Dr. Ranjit Panigrahi | Samarjeet Borah

  International Journal of Engineering & Technology, 7(3.24), 483-490.

• A detailed analysis of CICIDS2017 dataset for designing Intrusion Detection Systems

  October 18, 2018 | Dr. Ranjit Panigrahi | Samarjeet Borah

  International Journal of Engineering & Technology, 7(3.24), 479-482.

• Rank Allocation to J48 Group of Decision Tree Classifiers using Binary and Multiclass Intrusion Detection Datasets

  | Dr. Ranjit Panigrahi | Samrjeet Borah

  Procedia Computer Science, Elsevier, Volume 132, 2018, pp. 323-332, ISSN 1877-0509

• An enhanced intrusion detection system based on clustering

  December 22, 2017 | Dr. Ranjit Panigrahi | Borah, S. | Chakraborty, A.

  Advances in Intelligent Systems and Computing, (pp. 37-45). Springer

• An Enhanced Intrusion Detection System based on Clustering

  December 22, 2017 | Dr. Ranjit Panigrahi | Samarjeet Borah | Anindita Chakraborty

  Advances in Intelligent Systems and Computing, (pp. 37-45). Springer

• False Alarm Classification Techniques in Intrusion Detection System

  December 10, 2014 | Dr. Ranjit Panigrahi | Samarjeet Borah

  National Conference on Communication, Cloud and Big Data, ISBN 978-1-908368-03-4, ACCB Publishing, England

• An Integrated Approach of Intrusion Detection System (IAIDS) for Wireless Sensor Networks

  December 04, 2013 | Dr. Ranjit Panigrahi | Kalpana Sharma | M K Ghose

  International Conference on Computing Communication and Sensor Network 2013 CCSN 2013(2): 5-8, December 2013. Published by Foundation of Computer Science, New York, USA., ISBN: 973-93-80879-47-5

• An integrated approach of intrusion detection system (IAIDS) for wireless sensor networks

  December 01, 2013 | Dr. Ranjit Panigrahi | Kalpana Sharma | MK Ghose

  International Journal of Computer Applications, Vol 1 (2), Pg 5-8.

• Wireless Sensor Networks - Architecture, Security Requirements, Security Threats and its Countermeasures

  February 14, 2013 | Dr. Ranjit Panigrahi | Kalpana Sharma | M K Ghose

  Third International Conference on Computer Science & Information Technology (CCSIT), ISBN: 978-1921987-00-7, Feb 2013, pp. 107-115, 2013, DOI: 10.5121/csit.2013.3611.

• Rank Allocation to J48 Group of Decision Tree Classifiers Using Binary and Multiclass Intrusion Detection Datasets

  September 06, 2018 | Dr. Ranjit Panigrahi | Samarjeet Borah

  Procedia Computer Science, Elsevier, Volume 132, 2018, Pages 323-332, ISSN 1877-0509

• Performance analysis and subsequent ranking of supervised classifiers using binary and multiclass intrusion detection dataset

  2018 | Dr. Ranjit Panigrahi | Samarjeet Borah

  International Journal of Pure and Applied Mathematics, Volume: 119(12), 2018, pp. 15095-15162, ISSN 1314-3395

• An Enhanced Intrusion Detection System Based on Clustering

  2018 | Dr. Ranjit Panigrahi | Samarjeet Borah | Anindita Chakraborty

  Advances in Intelligent Systems and Computing, vol 564, 2018, Springer, Singapore

• Rank of normalizers through TOPSIS with the help of supervised classifiers

  January 01, 2018 | Dr. Ranjit Panigrahi | Samarjeet Borah

  International Journal of Engineering & Technology, 7(3.24), 483-490

• A detailed analysis of CICIDS2017 dataset for designing Intrusion Detection Systems

  January 01, 2018 | Dr. Ranjit Panigrahi | Samarjeet Borah

  International Journal of Engineering & Technology, 7(3.24), 479-482

• A detailed analysis of CICIDS2017 dataset for designing Intrusion Detection Systems

  January 01, 2018 | Dr. Ranjit Panigrahi | Samarjeet Borah

  International Journal of Engineering & Technology, 7(3.24), 479-482

• Dual-stage intrusion detection for class imbalance scenarios

  January 01, 2019 | Dr. Ranjit Panigrahi | Samarjeet Borah

  Computer Fraud & Security, 2019(12), 12-19

• Survivability prediction of patients suffering hepatocellular carcinoma using diverse classifier ensemble of grafted decision tree

  January 01, 2020 | Dr. Ranjit Panigrahi | Pramanik, M | Chakraborty, U. K | Bhoi, A. K

  International Journal of Computer Applications in Technology, 64(4), 349-360

• Decision-making on the existence of soft exudates in diabetic retinopathy

  January 01, 2020 | Dr. Ranjit Panigrahi | Reyana, A | Krishnaprasath, V. T | Kautish, R | Shaik, M

  International Journal of Computer Applications in Technology, 64(4), 375-381

• Performance Assessment of Supervised Classifiers for Designing Intrusion Detection Systems: A Comprehensive Review and Recommendations for Future Research

  January 01, 2021 | Dr. Ranjit Panigrahi | Borah, S | Bhoi, A. K | Ijaz, M. F | Pramanik, M | Jhaveri, R. H | Chowdhary, C. L

  Mathematics, 9(6), 690

• A Consolidated Decision Tree-Based Intrusion Detection System for Binary and Multiclass Imbalanced Datasets

  January 01, 2021 | Dr. Ranjit Panigrahi | Borah, S | Bhoi, A. K | Ijaz, M. F | Pramanik, M | Kumar, Y | Jhaveri, R. H

  Mathematics, 9(7), 751

• Classification and Analysis of Facebook Metrics Dataset Using Supervised Classifiers

  October 16, 2021 | Dr. Ranjit Panigrahi | Borah, S

  Social Network Analytics: Computational Research Methods and Techniques, Elsevier | Call For Chapter

• Applied Soft Computing Techniques and Applications

  January 01, 2021 | Dr. Ranjit Panigrahi | Panigrahi, R

  Series: Research Notes on Computing and Communication Sciences, ISBN: 9781774630297 | Book Publication