Covid sparks insights into ransomware prevention, earns the researcher a doctorate:
These days I see a lot of senior industry people go back to academics. Some wish to pursue a leadership program, some wish to pursue a specialized topic such as AI. And some wish to acquire a Ph.D. degree. One such person rising up to the highest academic pedestal is Dr. A Shiju Rawther.
Dr. Rawther’s has 24 years of technology experience across multiple large organizations. He holds a B.E. in Computer Science & Engineering from Bangalore University where he was a topper in his engineering college. This early academic success set the tone for his future, driving him to continuously expand the frontiers of knowledge.
Dr. Rawther quickly demonstrated his technical prowess early in his career. As the calendar pages flipped his career evolved to encompass managing software development, IT Infrastructure, network, data & analytics, and security operations. This wide exposure made him a versatile technologist with a blend of expertise across various domains and industry, helping him become a well-rounded leader in the tech space.
His appetite for continuous education manifested in his pursuit of a master’s in information management from the prestigious K.J. Somaiya Institute of Management & Research, affiliated with the Mumbai University. Despite the demands of a full-time career, he completed the program through part-time evening classes. His multi-faceted background and his continuous pursuit of knowledge paved the way for his successful transition into advanced research and cybersecurity, culminating in his PhD and, and becoming a beacon for others in the industry. He earned his PhD from the Hindustan Institute of Technology & Science, Chennai. Further, he was conferred with the Founder Dr. K.C.G. Verghese Award for the Best Researcher, a prestigious accolade that recognized him as the top researcher in the university across all disciplines of PhD scholars.
During the 36 months of his PhD research, Dr. Rawther published 12 papers in Scopus-indexed international journals, significantly contributing to the global discourse on cybersecurity. He also secured 3 patents with the Indian Patent Authority.
Dr. Rawther is currently working as Head – IT for a leading financial institution at Mumbai. Prior to this he had a very successful and steady career growth in reputed organizations viz, CARE Ratings, Poonawalla Finance, IIFL Finance, TransUnion CIBIL, Fullerton India Credit, PCS Technology, Gateway Terminals, Wipro InfoTech and Primus Telecommunications.
Learning from a Virus
It was during the covid-19 pandemic that his research journey took a distinctive turn. At the time, he was serving as the CIO at a finance company promoted by a leading vaccine manufacturing firm. During this period, the study of human infections and the progression of diseases through susceptible, infectious, and recoverable stages became a focal point of global attention.
Inspired by this, Dr. Rawther drew a parallel between human infection models and the propagation of cyberattacks, particularly ransomware. Using the Kermack-McKendrick mathematical model, which is traditionally applied to study infectious diseases, he ideated a similar approach for cybersecurity. His research aimed to model cyber threats in the same stages – Susception, Infection, and Recovery.
This groundbreaking idea formed the foundation of what became known as the Compartment Model. Dr. Rawther developed a prototype of this model using R for the core mathematical computations, and Python for data ingestion. His model simulated ransomware attacks and their identification, micro segmentation, time series & spread, offering a novel approach to understanding and mitigating cyber threats. He went further by comparing the compartment model with a probabilistic mathematical model to demonstrate its effectiveness. The comparison was validated using an entropy model, which measures uncertainty and data loss. To assess the accuracy of his findings, a confusion matrix was employed to prove the false and true positives. The model was tested over multiple iterations on a network of 100×100 nodes, showing consistent success in predicting and preventing ransomware attacks.
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