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Nanda Rani

Nanda Rani contributes to research discovery and scholarly infrastructure.

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Cryptography and Security Artificial Intelligence

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preprint2026arXiv

On the Security of Research Artifacts

Research artifacts are widely shared to support reproducibility, and artifact evaluation (AE) has become common at many leading conferences. However, AE mainly checks whether artifacts work as claimed and can be reproduced. It largely overlooks potential security risks. Since these artifacts are publicly released and reused, they may unintentionally create opportunities for misuse and raise concerns about safe and responsible sharing. We study 509 research artifacts from top-tier security venues and find that many contain insecure code patterns that may introduce potential attack vectors. We propose a taxonomy for context-aware security assessment to enable structured analysis of such risks. We perform static analysis and examine the resulting findings, filtering false positives and identifying real security risks. Our analysis shows that 41.60% of the prevalent findings may pose security concerns under practical usage. To support scalable analysis, we introduce SAFE (Security-Aware Framework for Artifact Evaluation), a first step toward an autonomous framework that analyzes tool-reported findings by considering code semantics, execution context, and practical exploitability. SAFE achieves 84.80% accuracy and 84.63% F1-score in distinguishing security and non-security risks. Overall, our results show that security is also important in AE for promoting safe and responsible research sharing. The source code is available at: https://github.com/nanda-rani/SAFE

preprint2022arXiv

Leveraging Machine Learning for Ransomware Detection

The current pandemic situation has increased cyber-attacks drastically worldwide. The attackers are using malware like trojans, spyware, rootkits, worms, ransomware heavily. Ransomware is the most notorious malware, yet we did not have any defensive mechanism to prevent or detect a zero-day attack. Most defensive products in the industry rely on either signature-based mechanisms or traffic-based anomalies detection. Therefore, researchers are adopting machine learning and deep learning to develop a behaviour-based mechanism for detecting malware. Though we have some hybrid mechanisms that perform static and dynamic analysis of executable for detection, we have not any full proof detection proof of concept, which can be used to develop a full proof product specific to ransomware. In this work, we have developed a proof of concept for ransomware detection using machine learning models. We have done detailed analysis and compared efficiency between several machine learning models like decision tree, random forest, KNN, SVM, XGBoost and Logistic Regression. We obtained 98.21% accuracy and evaluated various metrics like precision, recall, TP, TN, FP, and FN.

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