- (940) 397-4191
- Assistant Professor
- Computer Science
- Bolin Hall
|The University of Memphis||Ph.D||2021|
|The University of Memphis||Masters of Science||2016|
|Khulna University of Engr. & Tech., Bangladesh||Bachelor of Science||2011|
|Employer||Position||Start Date||End Date|
|Midwestern State University||Assistant Professor||07/05/2021|
|The University of Memphis||Graduate Assistant||01/01/2015||05/09/2021|
|Samsung Electronics||Senior Software Engineer||03/01/2013||08/15/2014|
|Evatix Ltd||Software Engineer||06/03/2011||02/28/2013|
Interpretable Machine Learning in Cyber Security
Since traditional Machine Learning (ML) techniques use black-box models, the internal operation of the model is unknown to human. Due to the black-box nature of the ML model, the trustworthiness of their predictions is sometimes questionable. Interpretable Machine Learning (IML) is a way of dissecting the ML models to overcome this shortcoming and provide a more reasoned explanation of model predictions. I work on IML methods and how they can be incorporated to solve security problems like detecting cyber attacks.
I have published several papers on IML and still working on some existing projects. Please reach out if you are interested to work on IML with cyber security.
Machine Learning Ensemble Frameworks
Over the past two decades, Distributed Denial of Service (DDoS) attacks have been responsible for most of the catastrophic failures on the Internet causing a huge amount of disruption of services across all sectors of the economy. Almost every year this attack scores top among all other attacks in terms of the cost to the overall global economy. Machine Learning (ML)-based Intrusion Detection Systems (IDSs) heal the global economy with the goal of reducing the prevalence of cyber incidents, such as DDoS. In an ML classification problem, the feature selection process, aka feature engineering, is treated as a mandatory pre-processing phase that potentially reduces the computational complexity by identifying important or relevant features from the original dataset and results in the overall improvement of classification accuracy.
I have designed several ensemble ML frameworks and an ensemble framework for feature selection methods (EnFS) that combines the outputs of seven well-known feature selection methods using the majority voting (MV) technique and produces an optimal set of features. I experimented with these ensemble supervised ML frameworks using well-known intrusion detection datasets like NSL-KDD, UNSW-NB15, and CIC-IDS2017.
I am interested in applying these frameworks in various domains like medical, financial, agriculture, security, etc. I am open to discuss domains other than these and would like to collaborate. If you are interested please feel free to reach me out.
- Das, Saikat, et al., "Network Intrusion Detection and Comparative Analysis using Ensemble Machine Learning and Feature Selection" Revised version submitted in "IEEE Transactions on Network and Service Management" (Under Peer Review)
- Das, Saikat, et al., "DDoS Explainer using Interpretable Machine Learning" Submitted in "2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference" (Under Peer Review)
- Das, Saikat, et al., "Machine Learning Ensemble-Based Intrusion Detection for DDoS Attacks", Prepared for submission in "Elsevier Computers & Security"
- Das, Saikat, Mohammad Ashrafuzzaman, Sajjan Shiva, and Frederick T. Sheldon,"Network Intrusion Detection using Natural Language Processing and Ensemble Machine Learning" 2020 IEEE Symposium Series on Computational Intelligence. IEEE, 2020.
- Das, Saikat, Deepak Venugopal, Sajjan Shiva, and Frederick T. Sheldon, "Taxonomy and Survey of Interpretable Machine Learning Method" 2020 IEEE Symposium Series on Computational Intelligence. IEEE, 2020.
- Agarwal, Namita and Saikat Das "Interpretable Machine Learning Tools: A Survey" 2020 IEEE Symposium Series on Computational Intelligence. IEEE, 2020.
- Ashrafuzzaman, Mohammad, Saikat Das, Yacine Chakhchoukh, and Frederick T. Sheldon "Elliptic Envelope Based Detection of Stealthy False Data Injection Attacks in Smart Grid Control Systems" 2020 IEEE Symposium Series on Computational Intelligence. IEEE, 2020.
- Das, Saikat, Deepak Venugopal, Sajjan Shiva, and Frederick T. Sheldon. "Empirical evaluation of the ensemble framework for feature selection in DDoS attack." In 2020 7th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2020 6th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom), pp. 56-61. IEEE, 2020.
- Ashrafuzzaman, Mohammad, Saikat Das, Yacine Chakhchoukh, Sajjan Shiva, and Frederick T. Sheldon, "Detecting stealthy false data injection attacks in the smart grid using ensemble-based machine learning." Computers & Security 97 (2020): 101994.
- Ashrafuzzaman, Mohammad, Saikat Das, Yacine Chakhchoukh, Sajjan Shiva, and Frederick T. Sheldon, "Supervised Learning for Detecting Stealthy False Data Injection Attacks in the Smart Grid", Proceedings of the International Conference on Security and Management, SAM'20, July 2020, Las Vegas, USA. (Accepted)
- Das, Saikat, Deepak Venugopal, and Sajjan Shiva. "A Holistic Approach for Detecting DDoS Attacks by Using Ensemble Unsupervised Machine Learning." In Future of Information and Communication Conference, pp. 721-738. Springer, Cham, 2020.
- Das, Saikat, Ahmed M. Mahfouz, Deepak Venugopal, and Sajjan Shiva. "DDoS intrusion detection through machine learning ensemble." In 2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C), pp. 471-477. IEEE, 2019.
- Das, Saikat, Ahmed M. Mahfouz, and Sajjan Shiva. "A Stealth Migration Approach to Moving Target Defense in Cloud Computing." In Proceedings of the Future Technologies Conference, pp. 394-410. Springer, Cham, 2019.
- Das, Saikat, and Sajjan Shiva, "CoRuM: collaborative runtime monitor framework for application security.", 2018 IEEE/ACM International Conference on Utility and Cloud Computing Companion (UCC Companion), IEEE, 2018.
- Mesbah-Ul-Awal, Md, Muhammad Sheikh Sadi, and Saikat Das. "Component Criticality Analysis: An Efficient Approach towards Minimizing the Risks of System Software Failure." Physical Science International Journal (2014): 231-245.
- Mesbah-Ul-Awal, Md, and Saikat Das. "Component Criticality Approach towards Minimizing the Risks of System Failure." 2013 Third International Conference on Advanced Computing and Communication Technologies (ACCT). IEEE, 2013.