arXiv subjects

Prabal Manhas

Publications and source records attributed to Prabal Manhas.

2 recordsLinked to original sources

arXiv2025

Aura-CAPTCHA: A Reinforcement Learning and GAN-Enhanced Multi-Modal CAPTCHA System

We present Aura-CAPTCHA, a multi-modal verification system that integrates Generative Adversarial Networks (GANs), Reinforcement Learning (RL), and behavioral analysis to create adaptive challenges resistant to classical deep-learning attacks. Our system synthesizes unique visual stimuli via GAN-based generation alongside synchronized audio challenges, while an RL agent adjusts difficulty based on real-time user interaction patterns. A hybrid classifier combining heuristic rules and machine learning distinguishes human from bot interactions. We position Aura-CAPTCHA relative to well-established baselines (text-based schemes, Google reCAPTCHA v2, audio alternatives, and modern invisible risk-analysis systems) and evaluate it against documented state-of-the-art attacks, including convolutional-neural-network solvers, object-detection pipelines (YOLO), and recent agentic vision-language models. Experimental results indicate that Aura-CAPTCHA improves human success rates and lowers classical bypass rates compared to static challenge-based baselines, although, like all explicit-challenge systems, it remains vulnerable to emerging large-model agents. We discuss these limitations transparently and outline future directions toward cognitive-gap-based defenses.

cs.LG↗

arXiv2025

Efficient and Scalable Self-Healing Databases Using Meta-Learning and Dependency-Driven Recovery

Modern database management systems (DBMS) face significant challenges in maintaining performance and availability under dynamic workloads. This paper proposes a novel self-healing framework that integrates Model-Agnostic Meta-Learning (MAML) for few-shot anomaly detection, Graph Neural Networks (GNNs) for dependency-driven cascading failure prediction, and multi-objective Reinforcement Learning (RL) for autonomous recovery. Unlike existing database tuning systems that focus primarily on offline configuration optimization, our framework enables real-time, end-to-end self-healing by rapidly adapting to unseen workload patterns with minimal labeled data. We introduce dynamic GNN-based dependency modeling that captures workload-dependent relationships between database components, enabling proactive cascade prevention. A scalarized multi-objective RL formulation balances latency, resource utilization, and cost during recovery, while SHAP-based explainability ensures operational transparency. Evaluations on Google Cluster Data and TPC benchmarks demonstrate 90.5\% anomaly detection F1-score with 5-shot adaptation, 90.1\% cascade prediction accuracy, and 85.1\% latency reduction in recovery actions, outperforming strong baselines including Isolation Forest, LSTM autoencoders, static GCN, and standard RL methods.

cs.DB↗