Research
IIDS
Research
Research Projects
Post-Quantum Cyber Security Considerations for Financial and Banking Industry
Quantum computing poses a growing threat to current cryptographic systems, with urgent implications for India’s Banking, Financial Services, and Insurance (BFSI) sector. This study, based on responses from 118 CISOs and CTOs, reveals low preparedness for quantum risks—despite widespread concern about their near-term impact. Key vulnerabilities include limited awareness of Post-Quantum Cryptography (PQC) and lack of migration planning.
To address this, the study proposes a quantum-resilient PQC migration framework designed for minimal disruption and long-term security. It also calls for immediate policy action, industry collaboration, and capacity building. The findings underscore the need for a quantum-ready cybersecurity strategy to protect India’s financial infrastructure.
Fact-Checking India: Identifying the Spread of Fake News and Policy Recommendations for Combating Misinformation
Prof. Manish Gangwar, Prof. Arani Roy, Dr. Shruti Mantri, Vineet Kumar
The study examines the alarming rise of fake news and deepfake technologies in India. It highlights social media as the primary vector for misinformation, offering a deep dive into user behavior and the broader societal impact of digital falsehoods. Beyond identifying the problem, the report provides actionable policy recommendations to curb the spread of misinformation, emphasizing the urgent need for stronger safeguards to protect information integrity in the digital age.
Telecom SIM Subscription Fraud: Global Trends, Risk Assessments, and Recommendations
Prof. Manish Gangwar, Dr. Shruti Mantri, Stephen Raveendra, IPS, Kalmeshwar Shingnevar, IPS
Subscription fraud remains a pressing challenge in the telecom sector, with identity theft and fraudulent SIM activations posing serious security and financial risks. A recent IIDS study, conducted in collaboration with the Telangana State Police Center of Excellence for Cyber Security, examines global fraud trends and assesses the effectiveness of risk mitigation strategies. The report advocates a multi-layered, risk-based approach to identity verification, leveraging intelligent analytics, predictive decision policies, and knowledge-based authentication for real-time fraud detection and regulatory compliance. The Key recommendations include strengthening online identification methods through phone authentication, address validation, and one-time password verification. These insights aim to help law enforcement and telecom providers enhance security frameworks and combat subscription fraud more effectively.
The Piracy-Malware Nexus in India: A Perceptions and Experience and Empirical Analysis
Dr. Paul A Watters, Dr. Shruti Mantri, Prof. Manish Gangwar
A recent IIDS study uncovers the strong link between digital piracy and cyber threats in India, revealing that consumers vastly underestimate the risks. Through surveys and empirical analysis using Google’s VirusTotal, the study found that while users perceive piracy sites as only twice as risky as mainstream websites, actual malware exposure is up to 15 times higher. Young adults (18-24) emerged as the most vulnerable group, engaging in risky online behavior with low awareness of cybersecurity threats. The findings highlight the urgent need for stricter enforcement against piracy syndicates and targeted awareness campaigns to educate consumers—particularly younger users—on the hidden dangers of piracy sites and how to adopt safer digital habits.
Un-Reported Crime Against Women: Data Based Insights and Recommendations
This research examines violence against women in India by comparing official NCRB data with crowdsourced reports from Safecity, highlighting the widespread issue of underreporting. Focusing on Delhi, Mumbai, and Hyderabad, it explores gaps in reporting crimes such as ogling, stalking, and sexual assault while assessing the effectiveness of current safety measures. The study advocates for better reporting systems, stronger legal protections against digital crimes, the use of technology for real-time support, and greater community involvement to shift societal attitudes. It also calls for ongoing research and policy adaptations to address the evolving nature of gender-based violence.
The Role of Classed Wording in Perpetuating Class-Based Inequalities in the Workplace
Professor Pooja Mishra
Despite extensive research efforts to understand the origins of class-based inequalities in the workplace, there remains to be a greater understanding of the institutional-level mechanisms contributing to reinforcing these disparities. Previous studies have focused on individual-level factors such as self-selection into specific occupations, lack of proactive behaviour, and low self-efficacy. In this project, investigators researched the influence of a crucial institutional-level context, namely the hiring process, on the challenges individuals from socio-economically disadvantaged backgrounds face when attempting to enter high-status occupations. They proposed that using classed wording, characterized by terminology aligning with the independent norms of upper socio-economic strata, represents a new mechanism contributing to existing class-based inequalities in the workplace.
Ongoing Projects
TSRTC
The Dynamic Reallocation Project is designed to make bus services more efficient by redistributing underutilized buses from less crowded routes to those experiencing higher demand, all within the same timeframe. By analyzing real-world data on ridership patterns, operational costs, and route specifics, the project seeks to develop a strategic framework that eases congestion without compromising service reliability. With a focus on data-driven scheduling and demand forecasting, the initiative aims to improve passenger convenience, minimize inefficiencies, and offer practical solutions for better public transport management.
Mule Account Detection Using Machine Learning Models
This study analyzes the account opening process for savings and current accounts in Indian banks, as per RBI guidelines, to identify patterns that distinguish genuine users from cybercriminals. The goal is to enhance fraud detection and reduce false rejections.
Dr. Avik Sarkar has leveraged Data Science to analyze drug delivery trends across public health centers in Punjab, submitting a report to the Punjab Health Systems Corporation (PHSC). Additionally, he conducted a study on Punjab's health insurance claims, presenting his findings to the Punjab Development Corporation (PDC).
This project aims to enhance patient care through a sophisticated conversational chatbot, leveraging Large Language Models (LLMs), voice recognition, multilingual support, and Knowledge Graphs.
As ESG practices gain momentum in corporate strategies, so do concerns about misleading claims. This project delves into Business Responsibility and Sustainability Reports (BRSR) from Indian companies, uncovering how to differentiate genuine sustainability efforts from deceptive practices.
Despite global agreements like the Paris Accord, many companies struggle to turn pledges into real environmental action. F.O.R.C.E. - Framework for Organizational Response to Climate Engagement introduces an AI-driven solution to ensure SEBI-mandated BRSR reports are compliant and truly reflective of sustainable impact.
In today's rapidly evolving business landscape, the demand for innovative solutions to enhance efficiency and drive growth has never been more critical. Recognizing this imperative, in this project, the team leveraged advanced technology to streamline operations and boost sales. The rationale behind this initiative is not merely operational efficiency but a fundamental shift towards data-driven decision-making.
In the evolving field of medical data management, unstructured data sources like clinical notes, electronic health records, and medical literature present significant opportunities for enhancing healthcare. This project focuses on developing a technological framework that aids healthcare professionals, particularly doctors and hospitals, in managing patient records and making informed clinical decisions. The team used framework like a chatbot-like UI for querying patient histories and a portal for accessing archival medical data, all integrated with large language models (LLMs) to provide contextually relevant, citation-supported responses.