Call for Papers -- AI and Machine Learning-Based Techniques for Anomaly Detection in Large Datasets

2024-08-23

The identification and extraction of anomalous components from datasets have always been central to anomaly detection. Various strategies, ranging from statistical to machine learning (ML) approaches, have been employed to uncover these abnormalities. Among these, ML has risen to prominence, offering sophisticated methods for modeling and detecting complex anomalies. Initially, statistical anomaly detection methods formed the backbone of this field, creating statistical frameworks to model the typical behavior of data and applying inference tests to identify deviations.

In today’s interconnected and data-driven world, modern enterprises are increasingly reliant on real-time data analysis to maintain operational integrity. Rapid changes in data, particularly in cybersecurity contexts, necessitate swift detection and response to anomalies. Anomalies can signify critical issues such as cyberattacks, system failures, and process malfunctions, making their timely detection vital for mitigating risks. However, the sheer scale and complexity of contemporary datasets make manual analysis impractical, further underscoring the need for advanced, automated anomaly detection systems.

Anomaly detection in high-dimensional data presents unique challenges, as increasing data complexity and volume complicate the detection process. Traditional data mining algorithms often struggle under the curse of scalability, making them less effective for modern large-scale applications. Statistical models have attempted to address these issues through various approaches, including exposure-based, aggregate-based, detach-based, specific gravity-based, and classification-based methods.

Machine learning provides a powerful toolkit for anomaly detection, particularly through unsupervised and semi-supervised techniques. The choice between these methods depends on the extent of labeled data available. For example, supervised learning is well-suited to datasets with ample labeled data. In manufacturing, particularly semiconductor production, the detection of anomalies is crucial for maintaining quality. Yet, many current approaches rely heavily on rudimentary excursion detection methods and manual sensor data analysis. More advanced ML algorithms can offer a more comprehensive and precise approach to anomaly detection, thereby enhancing production quality.

This special issue seeks to advance the field of anomaly detection by showcasing cutting-edge AI and machine learning approaches specifically tailored for large, complex datasets. It will explore the integration of emerging technologies, such as deep learning, blockchain, and edge computing, to enhance the effectiveness and efficiency of anomaly detection systems. The issue will also address critical challenges related to scalability, interpretability, and security, offering solutions to ensure robust and reliable systems.

Possible topics include, but are not limited to:

  • Optimized Sequential Minimal Optimization Algorithms and Enhanced K-Mean Clustering for High-Dimensional Anomaly Detection in Machine Learning
  • Adaptive Streaming Data Outlier Detection Using Reinforcement Learning and Advanced Ensemble Techniques
  • Comprehensive Meta-Analysis of Machine Learning-Based Anomaly Detection Techniques Across Diverse Datasets
  • Scalable Anomaly Detection in Portable Wireless Networks Using Advanced Semi-Supervised Deep Learning Models
  • Innovative Approaches in Machine Learning for Anomaly Detection in Next-Generation Industrial Control Networks
  • Advanced ML Techniques for Dynamic Load Estimation and Anomaly Detection in the Context of Cybersecurity Threats
  • Multi-Agent Systems for Anomaly Detection in Industry 4.0: Leveraging Federated Learning and Distributed AI
  • Robust Anomaly Detection in Cyber-Physical Systems: Enhancing Machine Learning Techniques for Critical Infrastructure Protection
  • In-Depth Analysis of Big Data Analytics in IoT-Enabled Smart Healthcare: Leveraging Machine Learning for Anomaly Detection
  • Vulnerability Analysis of Automotive IoT Networks: Utilizing Deep Learning for Proactive Anomaly Detection
  • Comprehensive Privacy Preservation Strategies in Healthcare Data Using Advanced Machine Learning Models

 

Guest Editors:
Dr. Tariq Sadad (Lead GE), Department of Computer Science, University of Engineering and Technology, Mardan 23200, Pakistan
Dr. Gulzar Mehmood (Co-GE), Department of Computer Scienc, Iqra National University, Swat, Pakistan
Dr. Farhat Ullah (Co-GE), School of Automation, Control Sciences and Engineering, China University of Geosciences, Wuhan, China

 

Important Dates:
Deadline of Submission: February 20, 2025
Author Notification: April 15, 2025
Deadline for Revised Papers: June 05, 2025
Final Acceptance: August 25, 2025