A Structured Survey of Anomaly Types and Detection Models in IoT Frameworks - PhD. Comprehensive Exam by: Atefeh Gilvari

Friday, June 13, 2025 - 11:30

 

 

The School of Computer Science would like to present…  

 

A Structured Survey of Anomaly Types and Detection Models in IoT Frameworks

PhD. Comprehensive Exam by: Atefeh Gilvari

 

Date:  Friday, June 13, 2025

Time: 11:30 AM

Location: Essex Hall, Room 122

 

Abstract:

In dynamic Internet of Things (IoT) environments, traditional anomaly detection surveys often treat all anomalies as a unified concept, overlooking the distinct characteristics and detection challenges posed by specific anomaly types. This paper presents a structured survey and comparative analysis of anomaly detection methods, organized by key anomaly categories such as drift, novelty, bias, noise, constant-value, and stuck-at anomalies. Each anomaly type is formally defined, with its theoretical foundation, modelling challenges, and applicable parametric, non-parametric, and neural network-based methods systematically reviewed. We analyze how model effectiveness varies across anomaly types and identify state-of-the-art techniques best suited for each. A novel contribution of this work is a taxonomy-driven mapping of models to anomaly types, designed to support targeted method selection and system design in resource-constrained IoT deployments. Our findings emphasize the need for interpretable, adaptive, and type-aware anomaly detection systems and outline open challenges in unified benchmarking, cross-type detectors, and ontology development for anomaly classification.

 

Keywords:

Large-Scale Sensor Networks, IoT, Anomaly Type, Anomaly Classification, Anomaly Detection

 

PhD Doctoral Committee:

External Reader: Dr. Ning Zhang

Internal Reader: Dr. Boubakeur Boufama

Internal Reader: Dr. Arunita Jaekel

Advisor(s): Dr. Ziad Kobti, Dr. Narayan Kar

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