All Outputs (9)

Degrees of tenant isolation for cloud-hosted software services: a cross-case analysis. (2018)
Journal Article
OCHEI, L.C., BASS, J.M. and PETROVSKI, A. 2018. Degrees of tenant isolation for cloud-hosted software services: a cross-case analysis. Journal of cloud computing [online], 7, article ID 22. Available from: https://doi.org/10.1186/s13677-018-0121-8

A challenge, when implementing multi-tenancy in a cloud-hosted software service, is how to ensure that the performance and resource consumption of one tenant does not adversely affect other tenants. Software designers and architects must achieve an o... Read More about Degrees of tenant isolation for cloud-hosted software services: a cross-case analysis..

Ranking of geostatistical models and uncertainty quantification using signal detection principle (SDP). (2018)
Journal Article
ANI, M., OLUYEMI, G., PETROVSKI, A. and REZAEI-GOMARI, S. 2019. Ranking of geostatistical models and uncertainty quantification using signal detection principle (SDP). Journal of petroleum science and engineering [online], 174, pages 833-843. Available from: https://doi.org/10.1016/j.petrol.2018.11.024

The selection of an optimal model from a set of multiple realizations for dynamic reservoir modelling and production forecasts has been a persistent issue for reservoir modelers and decision makers. Current evidence has shown that many presumably goo... Read More about Ranking of geostatistical models and uncertainty quantification using signal detection principle (SDP)..

Overlap-based undersampling for improving imbalanced data classification. (2018)
Presentation / Conference Contribution
VUTTIPITTAYAMONGKOL, P., ELYAN, E., PETROVSKI, A. and JAYNE, C. 2018. Overlap-based undersampling for improving imbalanced data classification. In Yin, H., Camacho, D., Novais, P. and Tallón-Ballesteros, A. (eds.) Intelligent data engineering and automated learning: proceedings of the 19th International intelligent data engineering and automated learning conference (IDEAL 2018), 21-23 November 2018, Madrid, Spain. Lecture notes in computer science, 11341. Cham: Springer [online], pages 689-697. Available from: https://doi.org/10.1007/978-3-030-03493-1_72

Classification of imbalanced data remains an important field in machine learning. Several methods have been proposed to address the class imbalance problem including data resampling, adaptive learning and cost adjusting algorithms. Data resampling me... Read More about Overlap-based undersampling for improving imbalanced data classification..

A framework for achieving the required degree of multitenancy isolation for deploying components of a cloud-hosted service. (2018)
Journal Article
OCHEI, L.C., PETROVSKI, A. and BASS, J.M. 2018. A framework for achieving the required degree of multitenancy isolation for deploying components of a cloud-hosted service. International journal of cloud computing [online], 7(3-4), pages 248-281. Available from: https://doi.org/10.1504/IJCC.2018.095396

Multitenancy allows multiple tenants to access a single instance of a cloud offering. While several approaches exist for implementing multitenancy, little attention has been paid to implementing the required degree of isolation since there are varyin... Read More about A framework for achieving the required degree of multitenancy isolation for deploying components of a cloud-hosted service..

Botnet detection in the Internet of Things using deep learning approaches. (2018)
Presentation / Conference Contribution
MCDERMOTT, C.D., MAJDANI, F. and PETROVSKI, A.V. 2018. Botnet detection in the Internet of Things using deep learning approaches. In Proceedings of the 2018 International joint conference on neural networks (IJCNN 2018), 8-13 July 2018, Rio de Janeiro, Brazil. Piscataway, NJ: IEEE [online], article number 8489489. Available from: https://doi.org/10.1109/IJCNN.2018.8489489

The recent growth of the Internet of Things (IoT) has resulted in a rise in IoT based DDoS attacks. This paper presents a solution to the detection of botnet activity within consumer IoT devices and networks. A novel application of Deep Learning is u... Read More about Botnet detection in the Internet of Things using deep learning approaches..

Fuzzy data analysis methodology for the assessment of value of information in the oil and gas industry. (2018)
Presentation / Conference Contribution
VILELA, M., OLUYEMI, G. and PETROVSKI, A. 2018. Fuzzy data analysis methodology for the assessment of value of information in the oil and gas industry. In Proceedings of the 2018 IEEE international conference on fuzzy systems (FUZZ-IEEE 2018), 8-13 July 2018, Rio de Janeiro, Brazil. Piscataway, NJ: IEEE [online], article ID 8491628. Available from: https://doi.org/10.1109/FUZZ-IEEE.2018.8491628

To manage uncertainty in reservoir development projects, the Value of Information is one of the main factors on which the decision is based to determine whether it is necessary to acquire additional data. However, subsurface data is not always precis... Read More about Fuzzy data analysis methodology for the assessment of value of information in the oil and gas industry..

Generic application of deep learning framework for real-time engineering data analysis. (2018)
Presentation / Conference Contribution
MAJDANI, F., PETROVSKI, A. and PETROVSKI, S. 2018. Generic application of deep learning framework for real-time engineering data analysis. In Proceedings of the 2018 International joint conference on neural networks (IJCNN 2018), 8-13 July 2018, Rio de Janeiro, Brazil. Piscataway, NJ: IEEE [online], article number 8489356. Available from: https://doi.org/10.1109/IJCNN.2018.8489356

The need for computer-assisted real-time anomaly detection in engineering data used for condition monitoring is apparent in various applications, including the oil and gas, automotive industries and many other engineering domains. To reduce the relia... Read More about Generic application of deep learning framework for real-time engineering data analysis..

Evolutionary computation for optimal component deployment with multitenancy isolation in cloud-hosted applications. (2018)
Presentation / Conference Contribution
OCHEI, L.C., PETROVSKI, A. and BASS, J.M. 2018. Evolutionary computation for optimal component deployment with multitenancy isolation in cloud-hosted applications. In Proceedings of the 2018 IEEE international symposium on innovations in intelligent systems and applications (INISTA 2018), 3-5 July 2018, Thessaloniki, Greece. New York: IEEE [online], article ID 8466315. Available from: https://doi.org/10.1109/INISTA.2018.8466315

A multitenant cloud-application that is designed to use several components needs to implement the required degree of isolation between the components when the workload changes. The highest degree of isolation results in high resource consumption and... Read More about Evolutionary computation for optimal component deployment with multitenancy isolation in cloud-hosted applications..

Towards situational awareness of botnet activity in the Internet of Things (2018)
Presentation / Conference Contribution
MCDERMOTT, C.D., PETROVSKI, A.V. and MAJDANI, F. 2018. Towards situational awareness of botnet activity in the Internet of Things. In Proceedings of the 2018 International conference on cyber situational awareness, data analytics and assessment (Cyber SA 2018): cyber situation awareness as a tool for analysis and insight, 11-12 June 2018, Glasgow, UK. Piscataway: IEEE [online], article number 8551408. Available from: https://doi.org/10.1109/CyberSA.2018.8551408

An IoT botnet detection model is designed to detect anomalous attack traffic utilised by the mirai botnet malware. The model uses a novel application of Deep Bidirectional Long Short Term Memory based Recurrent Neural Network (BLSTMRNN), in conjuncti... Read More about Towards situational awareness of botnet activity in the Internet of Things.