Skip to content
Log in
Sponsored Artificial Intelligence: A Modern Approach, Global Edition Artificial Intelligence: A Modern Approach, Global Edition

Author

Mitra Bhargeshbhai Patel

View on Semantic Scholar

2 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Open access Aug 2026

A Graph Database Approach to Multi-Hop Fraud Pattern Analysis

Modern financial fraud rarely occurs in isolation; bad actors increasingly rely on coordinated networks spanning accounts, devices, merchants, synthetic identities, and network identifiers. A transaction that appears entirely legitimate when evaluated individually often reveals suspicious behavior once its broader relational context is analyzed. This paper presents a graph-database-driven framework engineered for multi-hop fraud pattern analysis, representing financial entities as nodes and their interactions as explicit relationships. Built on a Neo4j architecture, the system integrates bounded multi-hop traversals, path analysis, structural centrality measures, and Louvain community detection accessible via an interactive web interface. The framework specifically targets five recurring investigation topologies: shared device or hardware infrastructure, shared network identifiers, intermediary pass-through chains, circular money transfers, and indirect exposure to flagged entities. To mitigate false positives, an explainable risk scoring model is introduced, combining structural topology with pattern-based indicators to ensure connectivity alone is not treated as definitive proof of fraud. We present an end-to-end operational architecture encompassing real-time data ingestion, graph construction, automated alert generation, and web-based visualization. Finally, a reproducible evaluation protocol is established to measure multi-hop query latency across varying depths, graph scale-up performance, analytical execution runtime, pattern coverage, and predictive accuracy (precision, recall, F1-score, and ROC-AUC). This work provides a focused, relationship-centric methodology aimed at delivering transparent, evidence-backed insight for fraud investigations.

Mitra Bhargeshbhai Patel, Khyati Bane, Shreyas Patel et al. · 0 citations
Open access Jul 2026

Beyond the Signature: Machine Learning for Adaptive Cyber Threat Intelligence

Modern cyberattacks are increasingly dynamic, multi-stage, and difficult to recognize with static signatures alone. Machine learning (ML) provides a complementary approach by learning patterns from large volumes of security telemetry and identifying behavior that may indicate compromise. This paper presents an integrated framework for applying ML across the cyber threat intelligence lifecycle, from data ingestion and preprocessing to model training, deployment, continuous monitoring, and response. It discusses supervised classification and anomaly detection, together with specialized security functions such as web filtering, dynamic sandboxing, behavioral analysis, deceptive-domain detection, and email protection. The paper also emphasizes a human-in-the-loop model in which automated systems prioritize evidence while analysts validate important decisions. Finally, it considers data drift, concept drift, adversarial manipulation, privacy, and retraining. The proposed approach treats ML as one layer of a broader defense system, combining automated pattern recognition with threat context and human expertise to improve detection speed, reduce alert fatigue, and support adaptive cyber defense.

Mitra Bhargeshbhai Patel, Bindi Bhatt, Dharvi Soni et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.