ADON/CLOUDCOM 2018: International Workshop on Anomaly Detection on the Cloud and the Internet of Things Hilton Cyprus Nicosia, Cyprus, December 10, 2018 |
Conference website | https://steliosot0.wixsite.com/adon |
Submission link | https://easychair.org/conferences/?conf=adoncloudcom2018 |
Abstract registration deadline | September 21, 2018 |
Submission deadline | September 21, 2018 |
Notification for paper acceptance | September 28, 2018 |
Scope:
Anomalies are detected in systems as a result of malicious behavior of users or as unscheduled changes in the operation of a system. With the advent of cloud, similar behavior is now detected in virtualized environments such as the environment of a cloud provider (now affecting the operation of the system in scale and of a much large number of users) with certain economic and operational impact. Although cloud systems are considered to be more efficient, for example in terms of reliability, security etc. compared to legacy systems operating within the premises of a company, they are exposed to a much larger number of users and the internet. At the same time, due to its scalability and affordability, the cloud is considered to be the ideal environment for deploying IoT applications. This exposes the cloud to even more risks as IoT is operating in the periphery of the cloud and is generally less protected than the cloud itself. In particular, the advent of the cloud and Internet of Things (IoT) open-up new possibilities in the design and development of methodologies ensuring reliable security protection and, in the case this fails, of methodologies for detecting and for dealing with the cause and point of system failure.
Coverage:
Due to the size and complexity of modern systems, anomalies can be detected in many aspects of system operation and relate mainly to:
- Anomaly detection for malicious behavior detectionwhich is typically expressed as (a) Fraud detection in which case, authorized of unauthorized users operate the system for the purpose of unfair or unlawful gain and (b) Intrusion detection in which case, unauthorized users are attempting to disrupt normal system operation.
- Anomaly detection on large scale system failureswhich is due to heavy (CPU, network and memory) workloads or faulty/misconfigured resources. A special case of system failure is encountered when parts of the system fails to operate as scheduled due to power failure or material fatigue (e.g. disk failure).
- Anomaly detection on IoT systems is due to unexpected behaviorof connected devices which can bedetected by monitoring the operation of these devices on the network, or by the analysis of real time data streams of misconfigured devices, or by monitoring and analyzing network traffic.
Anomaly detection has been studied extensively in recent years and new methods are now becoming available on the cloud. Depending on application, anomalies can be detected either in real time i.e. typically by the analysis of stream data acquired by the application and operation of the system or, in batch (i.e. by analyzing system log data). Methods and systems for stream processing for example Storm, Spark, Flink, big data analysis techniques (as log data eventually become big) combined with Machine Learning techniques (for adapting anomaly detection to the peculiarities of the data and of the operation environment) are of particular importance to the design of anomaly detection methods. Combined with methods of system security analysis in virtualized environments (such as the cloud), the new era of methods for anomaly detection will soon arise.
The purpose of this Workshop in to bring together experts from the fields of distributed computing systems including security, cloud and Internet of Things as well as experts on algorithms for signal processing, log analysis, pattern recognition and statistical learning models, working in all aspects of anomaly detection such as those referred to above.
Topics of interest:
Methods and Tool for anomaly detection in virtualized (including containerized) environments and the IoT with particular emphasis to:
- Malicious behavior detection (fraud detection, intrusion detection)
- Security breach detection in IoT
- System failure detection
- Stream processing for anomaly detection (real time and batch processing techniques)
- Stream query processing for anomaly detection
- Service oriented architectures for anomaly detection
- Machine learning platforms and techniques for anomaly detection
- Use cases and performances of anomaly detection methods
- Application of machine learning methods for anomaly detection
- Pattern-based and time-series system data analytics for anomaly detection
- Log data analysis and data mining for anomaly detection
- Text mining and regular expressions processing for anomaly detection
- Black-box anomaly detection techniques for detection of erroneous events
Submission Guidelines
All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome:
Full papers describing original content: Submitted papers must not substantially overlap papers that have been published or that are simultaneously submitted to a journal or a conference with proceedings. Authors must submit their papers by the deadline indicated above, using the Easychair submission system. Only PDF files will be accepted. Manuscripts need to be prepared according to the IEEE CS format. Only PDF files will be accepted. All regular paper submissions should be written in English with a maximum paper length of 6 pages, two-column (same as the main conference). All submitted papers will be reviewed by at least three experts.
Important dates
- Paper submission: 26 September 2018 (final, extended)
- Notification of acceptance: 1 October 2018
- Camera-ready version: 08 October 2018 (please observe main CloudCom 2018 website)
- Workshop day: December 10, 2018.
Committees
Program Committee
- Euripides G.M. Petrakis, Technical University of Crete, Greece
- Stelios Sotiriadis, Birkbeck, University of London, UK
Organizing committee
- Cristiana Amza, University of Toronto, Canada
- Nik Bessis, Edge Hill University, UK
- Rajkumar Buyya, University of Melbourne, Australia
- Valentin Cristea, Technical University of Bucharest, Romania
- Antonios Deligiannakis, Technical University of Crete, Greece
- Evangelos Milios, Dalhousie University, Canada
- Florin Pop, Technical University of Bucharest, Romania
- Alessandro Provetti, Birkbeck, University of London, UK
- Marcelo Trovati, Edge Hill, UK
Contact
All questions about submissions should be emailed to Euripides G.M. Petrakis (petrakis@intelligence.tuc.gr) or Stelios Sotiriadis (stelios@dcs.bbk.ac.uk)