Anik Tahabilder

I am a PhD student in Department of Computer Science at Wayne State University. I am working in Big Data Research Lab lab under the supervision of Professor Dr. Shiyong Lu . My current research focuses on blockchain smart contract security, smart contract audit tools, and machine learning. I hold a Bachelor's degree in Electrical and Electronic Engineering from Pabna University of Science and Technology, an MS in Technology from Western Carolina University, an MS in Computer Science from Wayne State University, and I spent one year in the Ph.D. program in Robotics at Stevens Institute of Technology. I am currently pursuing a Ph.D. in Computer Science at Wayne State University.

I pronounce my name as Anik Tahabilder.

Recent News
  • Awarded MS in CS, Fall 2024!

Education

  • Ph.D. in CS at Wayne State University (Fall 2021 - present)
  • MS in CS at Wayne State University (Fall 2021 - Win 2024)
  • MS in EE at Western Carolina University (Fall 2018 - Summer 2020)
  • B.Sc. in EEE at Pabna University of Science and Technology (2011 - 2016)

    • Research Projects

  • Smart Contract Audit Tool using Machine Learning: This project focuses on developing an AI-powered Smart Contract Audit Tool that leverages Machine Learning (ML) to enhance the security and reliability of blockchain-based smart contracts. The tool employs static and dynamic analysis techniques to detect vulnerabilities such as reentrancy attacks, integer overflows, access control issues, and gas optimization inefficiencies. It integrates Natural Language Processing (NLP) and deep learning models to analyze and explain contract logic, providing detailed security insights and automated audit reports. Designed for Ethereum Virtual Machine (EVM)-compatible blockchains, it ensures comprehensive vulnerability detection while maintaining high accuracy. The platform features Web3 authentication, allowing users to verify their identity using a connected wallet before submitting smart contracts for analysis. Additionally, Explainable AI (XAI) techniques enhance transparency, making security findings more interpretable for developers. This tool aims to improve the security, efficiency, and auditability of smart contracts, enabling blockchain developers to deploy safer and more reliable decentralized applications (DApps).
  • BGP Hijacking Prevention Leveraging Blockchain Technology: This project introduces a blockchain-powered security solution to mitigate BGP hijacking attacks and enhance the integrity of global internet routing. By leveraging decentralized trust mechanisms, cryptographic route verification, and smart contract-based policy enforcement, the system ensures that only authenticated and authorized entities can announce and modify BGP routes. Immutable on-chain logging provides transparency and accountability, reducing the risk of unauthorized route manipulations. This approach eliminates reliance on centralized authorities, strengthening the security and reliability of the Border Gateway Protocol (BGP) while ensuring a more resilient and tamper-proof internet infrastructure.

    • Experience

  • GTA, Dept. of Computer Science at Wayne State University (Fall 2021 - present)
  • GRA, Dept. of Computer Engineering at Stevens Institute of Technology (Fall 2020 - Summer 2021
  • GTA, Dept. of Engineering and Technology at Western Carolina University (Fall 2018 - Summer 2020)

    • Certification

  • Blockchain Specialization by University at Buffalo (Coursera)

    • Publications

    • [1] A. Tahabilder, A. A. Mamun, N. Rahman, P. K. Ghosh, "Co-optimal PMU Placement for Complete Monitoring of Distributed Generations Installed System," Recent Advances in Power Systems: Select Proceedings of EPREC 2020, pp. 477-483, 2021, Springer Singapore.
    • [2] M. Kowsher, A. Tahabilder, M. Z. I. Sanjid, N. J. Prottasha, M. M. H. Sarker, "Knowledge-base Optimization to Reduce the Response Time of Bangla Chatbot," ICIEV & icIVPR, pp. 1-6, 2020, IEEE.
    • [3] M. Kowsher, A. Tahabilder, M. M. H. Sarker, M. Z. I. Sanjid, N. J. Prottasha, "Lemmatization Algorithm Development for Bangla NLP," ICIEV & icIVPR, pp. 1-8, 2020, IEEE.
    • [4] A. Al Mamun, M. S. Hossain, P. P. Em, A. Tahabilder, R. Sultana, M. A. Islam, "Small Intestine Bleeding Detection Using Color Threshold and Morphological Operation in WCE Images," International Journal of Electrical and Computer Engineering, vol. 11, no. 4, pp. 3040, 2021, IAES.
    • [5] M. Kowsher, M. J. Uddin, A. Tahabilder, N. J. Prottasha, M. Ahmed, K. R. Alam, T. Sultana, "BnVec: Towards the Development of Word Embedding for Bangla Language Processing," International Journal of Engineering & Technology, vol. 10, no. 2, pp. 95, 2021.
    • [6] M. Kowsher, N. J. Prottasha, A. Tahabilder, K. Habib, M. Abdur-Rakib, M. S. Alam, "Predicting the Appropriate Mode of Childbirth Using Machine Learning Algorithm," International Journal of Advanced Computer Science and Applications, vol. 12, no. 5, pp. 700-708, 2021.
    • [7] M. Kowsher, I. Hossen, A. Tahabilder, N. J. Prottasha, M. M. H. Sarker, N. Ahasan, M. I. Hoque, "Sdsv: Angle Measurement for Supervised Classification," Procedia Computer Science, vol. 189, pp. 216-223, 2021, Elsevier.
    • [8] A. Musha, A. A. Mamun, A. Tahabilder, B. Jahan, R. Sultana, M. Akter, "An Automated Coronavirus Disease Detection Approach Using a Deep Neural Network from X-ray Images," Computational Intelligence in Pattern Recognition: Proceedings of CIPR 2021, pp. 427-438, 2022, Springer Singapore.
    • [9] M. Kowsher, A. Tahabilder, N. J. Prottasha, M. Abdur-Rakib, M. Moyez Uddin, P. Saha, "Bangla Topic Classification Using Supervised Learning," Computational Intelligence in Pattern Recognition: Proceedings of CIPR 2021, pp. 505-518, 2022, Springer Singapore.
    • [10] M. Kowsher, M. J. Uddin, A. Tahabilder, M. R. Amin, M. F. Shahriar, M. S. Islam, "Banglalm: Data Mining Based Bangla Corpus for Language Model Research," 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), pp. 1435-1438, 2021, IEEE.
    • [11] M. Kowsher, I. Hossen, A. Tahabilder, N. J. Prottasha, K. Habib, Z. R. M. Azmi, "Support Directional Shifting Vector: A Direction Based Machine Learning Classifier," Emerging Science Journal, vol. 5, no. 5, pp. 700-713, 2021.
    • [12] M. Kowsher, A. Tahabilder, M. Z. I. Sanjid, N. J. Prottasha, M. S. Uddin, M. A. Hossain, M. A. K. Jilani, "LSTM-ANN & BiLSTM-ANN: Hybrid Deep Learning Models for Enhanced Classification Accuracy," Procedia Computer Science, vol. 193, pp. 131-140, 2021, Elsevier.
    • [13] M. Kowsher, A. Das, M. M. H. Sarker, A. Tahabilder, M. Z. I. Islam, "SeqVectorizer: Sequence Representation in Vector Space," Proceedings of the 4th International Conference on Networking, Information Systems & Security, pp. 1-6, 2021.
    • [14] A. Al Mamun, M. J. Hossen, A. Tahabilder, A. Musha, R. Hasnat, S. K. Saha, "Acute Lymphoblastic Leukemia Detection Approach Using Color Threshold and Morphological Techniques," International Journal of Electrical and Computer Engineering, vol. 12, no. 4, pp. 3692, 2022, IAES.
    • [15] A. Musha, A. A. Mamun, A. Tahabilder, M. J. Hossen, B. Jahan, S. Ranjbari, "A Deep Learning Approach for COVID-19 and Pneumonia Detection from Chest X-ray Images," International Journal of Electrical & Computer Engineering, vol. 12, no. 4, 2022.
    • [16] A. Al Mamun, P. P. Em, M. J. Hossen, A. Tahabilder, B. Jahan, "Efficient Lane Marking Detection Using Deep Learning Technique with Differential and Cross-Entropy Loss," International Journal of Electrical & Computer Engineering, vol. 12, no. 4, 2022.
    • [17] A. Al Mamun, P. P. Em, M. J. Hossen, B. Jahan, A. Tahabilder, "A Deep Learning Approach for Lane Marking Detection Applying Encode-Decode Instant Segmentation Network," Heliyon, vol. 9, no. 3, 2023, Elsevier.
    • [18] A. A. Mamun, P. P. Em, J. Hossen, A. Tahabilder, B. Jahan, "A Comprehensive Review on Lane Marking Detection Using Deep Neural Networks," Sensors, vol. 22, no. 19, pp. 7682, 2022, MDPI.
    • [19] R. Hasnat, A. A. Mamun, A. Musha, A. Tahabilder, "A Review on Heart Diseases Prediction Using Artificial Intelligence," International Conference on Machine Intelligence and Emerging Technologies, pp. 41-54, 2022, Springer Nature.
    • [20] C. Bai, J. Liu, A. Tahabilder, M. M. Imran, S. Lu, D. Che, "A Generic Efficient Scientific Workflow Engine for the Optimizations of Run-Time Execution," 2023 IEEE International Conference on Software Services Engineering (SSE), pp. 98-103, 2023, IEEE.

    Contact

    📧 Email: tahabilderanik@gmail.com, gj9994@wayne.edu

    📍 Office: Room #3105, 5057 Woodward Ave, Detroit, MI 48202

    📞 Phone: (828) 283-1045

    🐦 Twitter (X): @atahabilder1

    💼 LinkedIn: LinkedIn Profile

    📖 Google Scholar: Google Scholar Profile

    🌐 Web of Science: Web of Science Profile