I am 2nd year PhD student in Computer Science at UCR (University of California Riverside), and working under the supervision of Professor Michalis Faloutsos at MAVERICS Lab (Modeling and Analysis of Vital EmeRging Information and Complex Systems) as Graduate Student Researcher.

My research interest includes Cyber Security, Machine Learning, Data Mining, and NLP. Currently, my research work is focused on analyzing Behavioral Analysis of the users across different security forums and coding platforms (like GitHub).

Research

1. URLytics: Profiling Forum Users from their Posted URLs.

   By Treves, B., Masud, M.R. and Faloutsos, M.

   In 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (Accepted)
   Abstract PDF

   Online forums contain a substantial amount of data, but very few studies have focused on mining the URLs posted by users. How can we fully leverage these posted URLs to extract as much information as possible about forum users? We perform a systematic study for extracting as much information as possible about forum users via their URL posting behavior. Within this study we develop a series of tools to analyze the data. Given a forum, we extract the following information: (a) basic statistics and a profile of the forum, (b) a profile for each user based on their referral to accounts in other platforms, (c) identification of communities within the forum, and (d) detection of malicious behavior. Most prior works focus on analyzing the text found in user posts rather than on URLs themselves, as we do here. In our study, we analyze three online security forums and find interesting results: (a) we identify 7% of the users posting social media links on other platforms, (b) we detect 148 groups of users that engage in communities on external social media platforms, (c) we expose 139 malicious users that collectively posted 328 malicious URLs. Additionally, we identify 17 groups with membership spanning across multiple forums, and discover numerous other groups that engage in coordinated malicious behavior. Our work is a significant step towards an all-encompassing system for profiling forum users at large.

   
   
   
2. PIMan: A Comprehensive Approach for Establishing Plausible Influence among Software Repositories.

   By Rokon, M.O.F., Islam, R., Masud, M.R. and Faloutsos, M.

   In 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (Accepted)
   Abstract PDF

   How can we quantify the influence among repos- itories in online archives like GitHub? Determining repository influence is an essential building block for understanding the dynamics of GitHub-like software archives. The key challenge is to define the appropriate representation model of influence that captures the nuances of the concept and considers its diverse manifestations. We propose PIMan, a systematic approach to quantify the influence among the repositories in a software archive by focusing on the social level interactions. As our key novelty, we introduce the concept of Plausible Influence which considers three types of information: (a) repository level interactions, (b) author level interactions, and (c) temporal considerations. We evaluate and apply our method using 2089 malware repositories from GitHub spanning approximately 12 years. First, we show how our approach provides a powerful and flexible way to generate a plausible influence graph whose density is determined by the Plausible Influence Threshold (PIT), which is modifiable to meet the needs of a study. Second, we find that there is a significant collaboration and influence among the repositories in our dataset. We identify 28 connected components in the plausible influence graph (P IT = 0.25) with 7% of the components containing at least 15 repositories. Furthermore, we find 19 repositories that influenced at least 10 other repositories directly and spawned at least two “families” of repositories. In addition, the results show that our influence metrics capture the manifold aspects of the interactions that are not captured by the typical repository popularity metrics (e.g. number of stars). Overall, our work is a fundamental building block for identifying the influence and lineage of the repositories in online software platforms.

   
   
   

Projects

Experience

1. Graduate Student Researcher University of California, Riverside Jun 2022 - Present

• Current research on User Disambiguation across security forums and Open Source Software Platforms
• Developed frontend of SourceFinder which provides access to the largest database of malware GitHub repositories
  Data Mining Pandas ReactJS

2. Application Developer BGD e-GOV CIRT, Bangladesh Jul 2020 - Aug 2021

• Contributed to the frontend of Boithok, a Video Conferencing Platform used by several ministries of Bangladesh
• Automated server deployment cycle which reduced the downtime of the system
• Added new feature of downloading meeting attendance report which minimized the manual overhead
  Jitsi-Meet ReactJS Python Docker Bash

3. Software Engineer Kona Software Lab Ltd, Bangladesh Jul 2018 - Jul 2020

• Contributed to the development of transaction management pipeline of Kona Blockchain Platform
• Introduced new mechanism of template based smart contract deployment to prevent insecure transaction requests
• Developed a PoC of Real Estate Management System based on Kona Blockchain Platform
• Implemented lottery service interface Module of a Lottery Game Management System Ethereum Solidity Java Spring Boot

Education

Ph.D. in Computer Science, Sep 2021 - Present

University of California, Riverside
PhD Advisor: Dr. Michalis Faloutsos, Professor, Department of CS, UCR

B.Sc. in Computer Science and Engineering, 2017

Bangladesh University of Engineering and Technology
Thesis Supervisor: Dr. Md. Monirul Islam, Professor, Department of CSE, BUET