UG Research

Impact of Social Media on Family Bondings (2022-23)

• HCI, Sociology, Human Psychology
• PI: Prof. Dr. A B M Alim Al Islam, CSE, BUET We are examining if the use of social media in Bangladesh contributes to a breakdown in communication between family members. Data has been collected using semi-structured face-to-face interviews. The Thematic technique will be used to collect and evaluate data from the recorded audio scripts of our interviewees in order to identify a pattern.

South Asian Public Digital Service Centers and the Risk to User Privacy (2019)

• HCI, Privacy This study looked at 19 digital service centers in Bangladesh. The findings showed that customers of these centers were vulnerable to privacy breaches due to a lack of infrastructure, local power politics, a lack of knowledge, and inadequate protection mechanisms.

Android Malware Detection Based on System Calls Using NLP and Machine Learning Algorithms (2022)

• ML, Security and Privacy
• PI: Prof. Dr. Md Shohrab Hossain, CSE, BUET
• Collaborator: A S M Ahsan-Ul Haque, SDE, Amazon.com, M.Sc, University of Virginia [2022], B.Sc in CSE, BUET [2017] I wanted to extend one of Dr. Hossain’s previous research where he used 1-step transition probability between the system calls to detect malware apk. As the prior model cannot capture the order or structure of system calls, it lacks semantic information. So after conducting a literature review to identify the limitations of the existing method for evading System Call-based Intrusion Detection System (IDS), I proposed a Machine Learning based robust dynamic method to detect malware apks, that can automatically execute the code routines as well as generate the user behavior of the android app.

  Objectives & Findings:

  • Generate user behavior during the system call retrieval from a virtual android device
  • Use a universal sentence encoder to represent each system call with an equivalent vector of 512 dimensions.
  • Finally, a random forest classifier with 100 estimators is used to ‘accurately’ classify our data. Along with the Random Forest (RF) model, we ran experiments with other models, such as Logistic Regression, Multilayer Perceptron (MLP) and XGBoost to compare our results.

Data Warehouse Design for Health Sectors and Outbreak Prediction (2021-22)

• Deep Learning, Data mining and Information Systems
• PI: Prof. Dr. Abu Syed Md. Latiful Haque, CSE, BUET

I have developed a clinical big data platform prototype-NCDW, integrating ambient data from 34 weather stations of Bangladesh Meteorological Department (BMD) as a proof of concept and solved the fundamental obstacle for data-driven communicable and non-communicable disease research, including record-linkage, privacy, and security, standardization, and interoperability. I submitted the conceptual design of my proposed system to Bangladesh Space Research and Remote Sensing Organization (SPARRSO) and secured their “Research Fellowship”. This platform enhance descriptive, diagnostic,predictive, and prescriptive analysis and research for a wide variety of diseases.

Main Objectives:

• Estimate the size of the NCDW and facilitates regional and national decision support, intelligent disease analysis, knowledge discovery, and data-driven research
• Develop a model to predict the number of cases of a given month of a given district. Forecast a disease outbreak

Read my thesis here !!!