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Dr. Kirubavathi G.

Assistant Professor, Department of Mathematics, School of Physical Sciences, Coimbatore

Qualification: Ph.D
g_kirubavathi@cb.amrita.edu
ORCID Profile
Google Scholar Profile
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Research Interest: Computer Networks, Network Security,Ethical Hacking, Malware Analysis, Intrusion detection Systems.

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Bio

Dr. Kirubavathi.G serves as Assistant Professor in the Department of Mathematics, School of Physical Sciences, and Coimbatore. Earlier, she worked as an Assistant professor in the Department of Applied Mathematics and Computational Sciences at PSG College of Technology for 5 years. She has published papers in various national and international journals. She is a Life member of Cryptology Research Society of India L/0381. She is a recognized PhD supervisor of Anna University. She is also a reviewer of various journals such as IEEE, Springer, and Elsevier.

Education

  • PhD in Computer Science
    Anna University Chennai
    Title of Theses: Analysis and detection of botnets using machine learning techniques
  • MCA
    Avinashilingam University, Second Proficiency in MCA
Publications

Journal Article

Year : 2023

Detection of IoT Botnet using Machine learning and Deep Learning Techniques

Cite this Research Publication : Regis Anne W, Kirubavathi G, Sridevi UK et al. Detection of IoT Botnet using Machine learning and Deep Learning Techniques, 02 March 2023, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-2630988/v1]

Year : 2023

Behavioural Based Detection of Android Ransomware Using Machine Learning Techniques

Cite this Research Publication : Kirubavathi G, Sreevarsan S, VARADHAN P et al. Behavioural Based Detection of Android Ransomware Using Machine Learning Techniques, 17 February 2023, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-2555218/v1]

Year : 2021

Botnet Detection Based On Network Traffic Flow Statistical Features and Model Based Clustering

Cite this Research Publication : G Kirubavathi, S Nalini, "Botnet Detection Based On Network Traffic Flow Statistical Features and Model Based Clustering", ICCAP 2021: Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, India Pages 286, 2021

Publisher : European Alliance for Innovation

Year : 2018

Structural analysis and detection of android botnets using machine learning techniques

Cite this Research Publication : Kirubavathi, G., &Anitha, R. (2018). Structural analysis and detection of android botnets using machine learning techniques. International Journal of Information Security, 17, 153-167

Publisher : International Journal of Information Security

Year : 2017

Analysis and detection of botnets using machine learning techniques

Cite this Research Publication : G Kirubavathi, R Anitha, "Analysis and detection of botnets using machine learning techniques", Anna University, 2017

Year : 2016

Botnet detection via mining of traffic flow characteristics

Cite this Research Publication : Kirubavathi, G., &Anitha, R. (2016). Botnet detection via mining of traffic flow characteristics. Computers & Electrical Engineering, 50, 91-101.

Publisher : Computers & Electrical Engineering

Year : 2013

HTTP botnet detection using hidden semi-Markov model with SNMP MIB variables

Cite this Research Publication : Venkatesh, G. K., Srihari, V., Veeramani, R., Karthikeyan, R. M., &Anitha, R. (2013). Http botnet detection using hidden semi-markov model with snmpmib variables. International Journal of Electronic Security and Digital Forensics, 5(3-4), 188-200

Publisher : International Journal of Electronic Security and Digital Forensics

Conference Proceedings

Year : 2014

Botnets: A study and analysis

Cite this Research Publication : Kirubavathi, G., &Anitha, R. (2014). Botnets: A study and analysis. In Computational Intelligence, Cyber Security and Computational Models: Proceedings of ICC3, 2013 (pp. 203-214). Springer India.

Publisher : Springer

Year : 2012

HTTP botnet detection using adaptive learning rate multilayer feed-forward neural network

Cite this Research Publication : Kirubavathi Venkatesh, G., &AnithaNadarajan, R. (2012). HTTP botnet detection using adaptive learning rate multilayer feed-forward neural network. In Information Security Theory and Practice. Security, Privacy and Trust in Computing Systems and Ambient Intelligent Ecosystems: 6th IFIP WG 11.2 International Workshop, WISTP 2012, Egham, UK, June 20-22, 2012. Proceedings 6 (pp. 38-48). Springer Berlin Heidelberg.

Publisher : Springer Berlin Heidelberg

Coursera Certifications
  1. Completed “TCP/IP and Advanced topics” by university of Colorado Systems offered through Coursera.
  2. Completed “Cybersecurity & The Internet of Things” by IBM offered through Coursera.
  3. Completed “Introduction to cybersecurity Tools &Cyber attacks” by University system of Georgia offered through Coursera.
  4. Completed “Introduction to TCP/ IP” by Yonsei University offered through Coursera.
  5. Completed “Cyber threat intelligence” by IBM offered through Coursera
  6. Completed “Penetration Testing, Incident Response and Forensics” by IBM offered through Coursera
  7. Completed “Fundamentals of Network Communication” by University of Colorado System offered through Coursera.
  8. Completed “The bits and bytes of Computer networking” by google offered through Coursera.
  9. Completed “Cyber Security & its Ten Domains” by University System of Georgia offered through Coursera.
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