About

Dr. Ajit Kumar has completed his Ph.D. in Computer Science and Engineering from the Department of Computer Science, Pondicherry University in May 2018. His Ph.D. thesis titled "A Framework for Malware Detection with Static Features using Machine Learning Algorithms" focused on Malware detection using machine learning. He has received his Bachelor of Computer Application (BCA) from IGNOU in the year 2009 and Master of Computer Science in the year 2011, from Pondicherry University. He has also received Post Graduate Diploma in Statistical and Research Methods from Pondicherry University in 2015 and Post Graduate Diploma in Information Security from IGNOU in 2016. His area of interest includes Information Security, Malware detection using Machine learning. He qualified UGC-NET for Lecturer exam in 2014, besides UGC-NET he has also qualified three states (Rajasthan, Andhra Pradesh, and Tamilnadu) SET (State Eligibility Test) lectureship exam. He is the first rank holder in the national Ph.D. entrance exam of Pondicherry University in the year 2012. He has published 6 research articles in peer-reviewed International Journal and has also presented and published his research in International IEEE and Elsevier conferences. He is fond of Python programming and love to train others in Python Programming.

Citations -115, h-index - 5, i10-index- 4 (Based on Google Scholar data (September 2020)

Language:
Hindi,English,Oriya
Professional Skills
Python programming
90%
Machine Learning,Data Science,Data Mining
85%
Backend Development(django,flask)
85%
Network Security, Cryptography
80%
Malware Detection
85%
Version Control(GIT, Github)
90%
Linux
80%
TensorFlow, Sk-learn, caffe, pytorch
80%
Publications
Work Experience
Assistant Professor
[July 2019 to March 2020]
Assistant Professor at VIT Bhopal

Subjects Taught (Theory & LABs):

  • CSD4002- Ethical Hacking
  • HUM2003- Foundations of Privacy and Security
  • CSE4012- Software Defined Netwok
  • MCA2002- Internet and Web Programming
  • CSD6003- Mobile-Forensic
  • CSE3011- Python-Programming
Assistant Professor
[June 2018 to June 2019]
Assistant Professor at Sri Sri University

Subjects Taught (Theory & LABs):

  • BCS101- Programming Fundamental with C.
  • BCS201- Object Oriented Programming.
  • BCS204- Data Structure.
  • BCS105- [LAB] Programming Fundamental with C.
  • BCS206- [LAB] Object Oriented Programming.
  • BCS207- [LAB] Data Structure.
Assistant Professor
(Contract)[Jul 2017-Dec 2017]
Central University Of Tamilnadu, Thiruvarur,Tamilnadu.
  • Taught MSCT31- Network and System Security for M.Sc students.
  • Conducted MSCP31- Network and System Security Lab for M.Sc students.
Research Assistant[Aug 2011- Feb 2012]
Smart and Secure Enviornment Lab(Pondicherry University)
  • Studied extensive literature on Network Stegnography.
Software Engineer[Jan 2011- Jul 2011]
Maventic Softwares Bangalore, KA
  • Learned ABAP programming language and worked with SAP workstation.
  • Worked on my M.Sc. project which was a POS (Point of Sale) module of real time company project.
Education

2012 - 2018

Doctor of Philosophy
A Framework for Malware Detection with Static Features using Machine Learning Algorithms

Pondicherry University

Ph.D. Thesis

Lectureship Quailifications
Qualified for
  • UGC-NET (62.86%), June 2014
  • Tamilnadu State Eligibility Test (69.71%), 2012
  • Andhra Pradesh State Eligibility Test (70.28%), 2012
  • Rajasthan State Eligibility Test (78.74%), 2012

Jul 2009 - May 2011

Master's Degree
Master of Science

Pondicherry University, Puducherry

Completed Master's in Computer Science [CGPA:8.0/10]

2006 - 2009

Bachelor's Degree
Bachelor of Computer Applications

Indira Gandhi National Open University

Completed B.C.A. with 68%

2015 - 2016

PGDIS
PG Diploma in Information Security

Indira Gandhi National Open University

Completed PGDIS with 74.8%

2014 - 2015

PGDSRM
PG Diploma in Statistical & Research Methods

Pondicherry University

Completed PGDSRM with 80%

Spring Graduation- 2016

MOOCs
Python Specialization, Programming For Everybody

University of Michigan(Coursera)

Completed Successfully

Other Activities
Research and Teaching (Guest Faculty)
Department of Computer Science,Pondicherry University
  • Study malware detection methods and incorporate machine learning to develop a non-signature based malware detection solution. Used different Python frameworks and modules such as sklearn,yara, Pefile etc.
  • Taught fundamental of Data mining course to MCA students as a semester paper, Python programming and help students with practical using sklearn Python machine learning framework.
Talks
Delivered talks
  • Given a talk in the One Day workshop on Digital India Initiatives organized by Department of Computer Science, Central University of Tamilnadu on 7 October 2017
  • Served as a resource person in the National Workshop on Software Tools for Digital Document Management Organized by Department of Computer Science and Engineering, National Institute of Technology Puducherry, Karaikal on 12 November 2016.
  • Delivered a lecture on the topic Blended and flipped classroom Tools in the one week webinar on Online Educational Resources for Effective Remote Teaching organized by Department of Computer Science, Pondicherry University,10-14 August 2020.
  • Delivered a talk on Use of Machine Learning for Malware Detection in the International Webinar on Artificial Intelligence organized by SHoDH, NIT Rourkela during August 26-27, 2020.
  • Served as a resource person for two days (4-5 September) in the International FDP on Tools for Technical Writing and Research Data Analysis, 1-5 September, 2020.
Our Research Dataset
Portable Executable Dataset
A learning model to detect maliciousness of portable executable using integrated feature set
  • Please write an email (provided in the contacts section of this page) to get the raw malware and benign samples used in the aforementioned research work. Please do put CC to co-authors (Dr. G. Aghila and Dr.K.S.Kuppusamy) and supply information about you and purpose of use.
  • Altough you can get the final feature set and scripts from the Github link. ClaMP:Classification of Malware with PE headers
FAMOUS Dataset
FAMOUS: Forensic Analysis of MObile devices Using Scoring of application permissions
  • Please write an email (provided in the contacts section of this page) to get the raw malware and benign samples used in the aforementioned research work. Please do put CC to co-authors (Dr. G. Aghila and Dr.K.S.Kuppusamy) and supply information about you and purpose of use.
  • FAMOUS Tool (Source code and details)
References
Contact Me

Phone

+(91)8903144954

Email

ajitkumar.[p]ondicherry[u]niversity[at]gmail.com