Deep Learning and Machine Learning Techniques for Anomaly Detection in Cloud and IoT Environments
Session Chair (s)
Dr. Vangipuram Radhakrishna
Senior ACM Member, MIEEE
VNRVJIET (Autonomous), Department of Information Technology, Hyderabad, Telangana, INDIA
Regional Editor (East Asia, Recent Advances in Computer Science and Communications, Bentham Science)
Section Editor (AI & ML) for Current Chinese Computer Science, Coronaviruses
Email:radhakrishna_v@vnrvjiet.in, vangipuramradhakrishna@ieee.org
Dr Shadi Aljawarneh
Prof. of Software Engineering at Jordan University of Science and Technology
Editor of IJCAC, IGI-Global, USA
CM Senior Member
Email: saaljawarneh@just.edu.jo
Dr Lakshmeeswari Gondi
Associate Professor, Department of CSE, GITAM Institute of Technology, GITAM (Deemed to be University), Visakhapatnam, Andhra Pradesh, India
Email: gondi.lakshmeeswari@gmail.com, lgondi@gitam.edu
Gunupudi Rajesh Kumar
Sr Asst Professor, VNRVJIET (Autonomous)
Department of Information Technology, Hyderabad, Telangana, INDIA
Email: rajeshkumar_g@vnrvjiet.in
Session Description
Anomaly detection is one of the prominent research areas in multiple domains such as cyber security, healthcare, image processing, agriculture, financial services and data mining applications. Because of its importance researchers from both academia and industry are evaluating possibility of developing computationally efficient approaches by applying machine learning, artificial intelligence and deep learning principles and techniques.
Although, numerous approaches are explored to build highly efficient and effective anomaly detection systems, but they are confronted with many difficulties when dealing with complex data, such as failing to capture intricate feature interactions or extract good feature representations. Machine Learning and Deep learning techniques have shown very promising performance in tackling different types of complex data in a broad range of tasks/problems, but its development in the area of anomaly detection is relatively slow due to some unique characteristics of anomalies, e.g., rarity, heterogeneity, boundless nature, and prohibitively high cost of collecting large-scale anomaly data. This special issue aims to promote the development of Machine Learning and Deep Learning techniques specially designed for anomaly detection in Cloud, IoT environments.
Recommended Topics
This special session will feature the most recent advances in Machine Learning and Deep Learning techniques for anomaly detection. Research contributions are invited from both academicians, and industrial practitioners working in the areas of data mining, machine learning and deep learning, and solicits original and high-quality research on the following topics. (but not limited to)
- DDoS attacks detection in cloud environment
- Anomaly Detection in IoT environment
- Feature representation of massive datasets
- Regression modelling for anomaly detection
- Deep anomaly detection in different types of data
- Representation learning for anomaly detection
- End-to-end anomaly classification
- Deep anomaly detection theories/foundation
- Artificial Intelligence tools & Applications
- Explainable AI systems
- Evolutionary Computing
- Pattern recognition
- Heuristic Planning Strategies and Tools
Paper Submission Process
Please submit your paper (in word format) at vangipuramradhakrishna@ieee.org, vrkrishna@acm.org, radhakrishna_v@vnrvjiet.in with Special Session on "Deep Learning and Machine Learning Techniques for Anomaly Detection in Cloud and IoT Environments" mentioned in the subject line.