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Pablo Moriano photo

Pablo Moriano

Staff Scientist | AI | ML | Cybersecurity

Contact

865.341.2768 | MORIANO@ORNL.GOV

All Publications

Adaptive Anomaly Detection for Identifying Attacks in Cyber-Physical Systems: A Systematic Literature Review
A traffic accident dataset for Chattanooga, Tennessee
Spatiotemporal features of traffic help reduce automatic accident detection time
A comprehensive guide to CAN IDS data and introduction of the ROAD dataset
Robustness of community structure under edge addition
CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-Level
DEVELOPING AND DEPLOYING SECURITY APPLICATIONS FOR IN-VEHICLE NETWORKS...
Graph-based machine learning improves just-in-time defect prediction
CANShield: Signal-based Intrusion Detection for Controller Area Networks
Detecting CAN Masquerade Attacks with Signal Clustering Similarity
Using bursty announcements for detecting BGP routing anomalies
Time-Based CAN Intrusion Detection Benchmark
On the Robustness of Network Community Structure Under Addition of Edges
Lessons Learnt from Comparing WhatsApp Privacy Concerns Across Saudi and Indian Populations

Key Links

Curriculum Vitae

Organizations

Computing and Computational Sciences Directorate
Computer Science and Mathematics Division
Mathematics in Computation Section
Systems and Decision Sciences Group

Related News

News

Moriano named ACM senior member

News

Moriano awarded early career fellowship for underrepresented groups

Research Highlight

A Novel Data-Driven Forensic Framework for Detecting Advanced Cyberattacks in CPS

Research Highlight

Leveraging Advanced AI Machinery to Detect Intelligent Attacks in CPS
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