arXiv subjects

Tarek Ramadan

Publications and source records attributed to Tarek Ramadan.

3 recordsLinked to original sources

arXiv2026

The Silent Spill: Measuring Sensitive Data Leaks Across Public URL Repositories

A large number of URLs are made public by various platforms for security analysis, archiving, and paste sharing -- such as VirusTotal, URLScan.io, Hybrid Analysis, the Wayback Machine, and RedHunt. These services may unintentionally expose links containing sensitive information, as reported in some news articles and blog posts. However, no large-scale measurement has quantified the extent of such exposures. We present an automated system that detects and analyzes potential sensitive information leaked through publicly accessible URLs. The system combines lexical URL filtering, dynamic rendering, OCR-based extraction, and content classification to identify potential leaks. We apply it to 6,094,475 URLs collected from public scanning platforms, paste sites, and web archives, identifying 12,331 potential exposures across authentication, financial, personal, and document-related domains. These findings show that sensitive information remains exposed, underscoring the importance of automated detection to identify accidental leaks.

cs.CR↗

arXiv2024

Novel Representation Learning Technique using Graphs for Performance Analytics

The performance analytics domain in High Performance Computing (HPC) uses tabular data to solve regression problems, such as predicting the execution time. Existing Machine Learning (ML) techniques leverage the correlations among features given tabular datasets, not leveraging the relationships between samples directly. Moreover, since high-quality embeddings from raw features improve the fidelity of the downstream predictive models, existing methods rely on extensive feature engineering and pre-processing steps, costing time and manual effort. To fill these two gaps, we propose a novel idea of transforming tabular performance data into graphs to leverage the advancement of Graph Neural Network-based (GNN) techniques in capturing complex relationships between features and samples. In contrast to other ML application domains, such as social networks, the graph is not given; instead, we need to build it. To address this gap, we propose graph-building methods where nodes represent samples, and the edges are automatically inferred iteratively based on the similarity between the features in the samples. We evaluate the effectiveness of the generated embeddings from GNNs based on how well they make even a simple feed-forward neural network perform for regression tasks compared to other state-of-the-art representation learning techniques. Our evaluation demonstrates that even with up to 25% random missing values for each dataset, our method outperforms commonly used graph and Deep Neural Network (DNN)-based approaches and achieves up to 61.67% & 78.56% improvement in MSE loss over the DNN baseline respectively for HPC dataset and Machine Learning Datasets.

cs.LG↗

arXiv2021

Comparative Code Structure Analysis using Deep Learning for Performance Prediction

Performance analysis has always been an afterthought during the application development process, focusing on application correctness first. The learning curve of the existing static and dynamic analysis tools are steep, which requires understanding low-level details to interpret the findings for actionable optimizations. Additionally, application performance is a function of an infinite number of unknowns stemming from the application-, runtime-, and interactions between the OS and underlying hardware, making it difficult, if not impossible, to model using any deep learning technique, especially without a large labeled dataset. In this paper, we address both of these problems by presenting a large corpus of a labeled dataset for the community and take a comparative analysis approach to mitigate all unknowns except their source code differences between different correct implementations of the same problem. We put the power of deep learning to the test for automatically extracting information from the hierarchical structure of abstract syntax trees to represent source code. This paper aims to assess the feasibility of using purely static information (e.g., abstract syntax tree or AST) of applications to predict performance change based on the change in code structure. This research will enable performance-aware application development since every version of the application will continue to contribute to the corpora, which will enhance the performance of the model. Our evaluations of several deep embedding learning methods demonstrate that tree-based Long Short-Term Memory (LSTM) models can leverage the hierarchical structure of source-code to discover latent representations and achieve up to 84% (individual problem) and 73% (combined dataset with multiple of problems) accuracy in predicting the change in performance.

cs.LG↗