Advances in Machine Learning for Anomaly Detection
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".
Deadline for manuscript submissions: 30 November 2025 | Viewed by 72
Special Issue Editor
Interests: machine learning with applications in computer vision, cybersecurity, IoT, wireless communications, energy systems, transportation systems, socioeconomic systems, and environmental systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Anomaly detection has become a significant research area with important system reliability, security, and public safety applications, due to the massive amount of data being generated in many real-world systems, which humans cannot effectively monitor. Anomaly detection algorithms are crucial for flagging unusual activity in monitored systems for further review by human operators.
In emerging applications such as video surveillance, autonomous driving, and high-dimensional time series monitoring, significant challenges emerge due to the complex nature of certain anomaly types, the complexity of the data, and system requirements.
This Special Issue aims to address modern challenges that anomaly detection algorithms face in emerging applications such as interaction-based complex anomalies in graph data (e.g., video, social media), the high-dimensionality of data (e.g., time series, video), and real-time detection requirements (e.g., autonomous driving, cyberattack detection).
We encourage the submission of research articles contributing new methods/datasets, as well as survey articles reviewing the literature, to this Special Issue. Topics of interest include, but are not limited to, the following:
- Detecting interaction-based complex anomalies in non-Euclidean data such as social networks, video, etc.
- Anomaly detection in high-dimensional time series data.
- Anomaly detection for autonomous vehicles.
- Intrusion detection for emerging cyberattacks.
- Adversarial machine learning attacks against anomaly detection algorithms.
- Defenses to protect anomaly detection algorithms from adversarial machine learning attacks.
- Detection of adversarial machine learning attacks against artificial intelligence (AI) models such as large language models (LLMs) and vision–language models (VLMs).
- Continual learning for anomaly detection.
- Multimodal anomaly detection.
- Real-time anomaly detection.
- Anomaly detection in cyber-physical systems such as smart grid and intelligent transportation systems.
- Computationally efficient anomaly detection algorithms for edge intelligence (e.g., smartphones, small robots, etc.).
- Datasets pertaining to the above-mentioned challenges.
Dr. Yasin Yılmaz
Guest Editor
Manuscript Submission Information
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Keywords
- anomaly detection
- machine learning
- AI
- time series data
- high-dimensional data
- video surveillance
- computer vision
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