Doctorate
Ph.D. — Computer Science
University of Jammu. Thesis: “To Study the Performance, Scalability and Reliability of Snort and the Contemporary Intrusion Detection Systems.”
Academic & professional profile
Dr. Alka Gupta
Computer Science Researcher · Data Scientist
My doctoral research examined the performance, scalability and reliability of network intrusion detection systems. My subsequent professional work has focused on data engineering and applied analytics. I am interested in returning to teaching and research, bringing practical computing experience into the classroom.
Profile
Research foundations and applied practice
My Ph.D. work at the University of Jammu focused on evaluating network intrusion detection systems, including Snort and its contemporaries. It addressed experimental performance, scalability and reliability rather than treating intrusion detection as a purely theoretical problem.
In industry I have worked in data science and analytics, with interests in telemetry processing, time-series information and cloud data platforms. I now aim to combine an experimental research approach with practical computing applications.
Doctoral research topic is recorded in the University of Jammu Ph.D. scholars register.
Academic record
Education & distinctions
Qualifications and distinctions recorded in my earlier academic biodata.
Postgraduate
M.Tech. — Computer Science & Engineering
Shri Mata Vaishno Devi University (2011–2013). Gold Medalist; Chancellor's Award for Distinction; Infosys Foundation Prize.
Undergraduate
B.E. — Computer Science & Engineering
Government College of Engineering and Technology, Jammu (2006–2010). Gold Medalist.
National examinations
UGC-NET / JRF & GATE
Qualified UGC-NET (June 2014) and was awarded a UGC Junior Research Fellowship. Qualified GATE in 2011.
Scholarly work
Research & selected publications
Completed doctoral work concerned intrusion detection and network-system performance. Current research interests in dependable IoT data systems, applied machine learning and edge–cloud analytics are future directions, not claims of completed results.
- Detecting attacks in high-speed networks: Issues and solutionsAlka Gupta and Lalit Sen Sharma · Information Security Journal: A Global Perspective, 29(2), 51–61 (2020)DOI ↗
- A categorical survey of state-of-the-art intrusion detection system—SnortAlka Gupta and Lalit Sen Sharma · International Journal of Information and Computer Security, 13(3/4), 337–356 (2020)DOI ↗
- Performance Evaluation of Snort and Suricata Intrusion Detection Systems on Ubuntu ServerAlka Gupta and Lalit Sen Sharma · Proceedings of ICRIC 2019, Springer, 811–821 (2019)DOI ↗
- Performance Analysis and Comparison of Snort on Various PlatformsAlka Gupta and Lalit Sen Sharma · International Journal of Computer Information Systems and Industrial Management Applications, 12, 23–32 (2020)Journal ↗
- Mitigation of DoS and Port Scan Attacks Using SnortAlka Gupta and Lalit Sen Sharma · International Journal of Computer Sciences and Engineering, 7(4), 248–258 (2019)DOI ↗
This is a selected, linked publication list, not an assertion that it is the complete bibliography. Researcher identifier: ORCID 0000-0003-3087-4617.
Professional experience
Data science & applied computing
Technical interests and areas of professional work, without unsupported commercial performance statistics or client-specific assurances.
Grok Digital
August 2022 – Present
Full-time, on-roll
Employed in the designation of Consultant, with a functional focus on data science and analytics. Professional work described in my portfolio includes data pipelines, time-series telemetry, cloud analytics, forecasting and data-access services.
Client and earlier employer labels are not shown here until the underlying employment chronology is verified.
Selected areas of applied work
Data engineering
Time-series telemetry and cloud pipelines
Integration of operational data stores with cloud analytics systems, with attention to repeatable ingestion and data quality.
Analytics
Sensor and logistics analytics
Working with telemetry and operational records to examine risk indicators, time-dependent events and actionable summaries.
Predictive methods
Forecasting and anomaly detection
Applied interests in statistical baselines and neural-network methods for time-series analysis. No performance claims are made without documented evaluations.
Integration
Data services and APIs
Connecting data platforms to applications using query services and APIs, with attention to access control and reproducible transformations.
Academic direction
Teaching & future research interests
Teaching interests
Computer networks, databases, data engineering, introductory machine learning and applied cybersecurity. My aim is to relate technical principles to experiments and working systems.
Research interests
Dependable IoT data pipelines, edge–cloud integration, evaluation of analytics workflows and applied machine learning for operational data. These topics represent directions for further investigation.
Academic collaboration & professional enquiries
I welcome enquiries concerning teaching, research collaboration and technically substantive data-science work.