Raul Ceretta Nunes

Bio: Raul Ceretta Nunes is an academic researcher. The author has contributed to research in topics: Recurrence quantification analysis & Cluster analysis. The author has co-authored 1 publications.

Combining Recurrence Quantification Analysis and Adaptive Clustering to Detect DDoS Attacks.

Abstract: The high number of Distributed Denial of Service (DDoS) attacks executed against a lot of nations has demanded innovative solutions to guarantee reliability and availability of internet services in the cyberspace. In this sense, different methods have been used to analyze network traffic for denial of service attacks, such as statistical analysis, data mining, machine learning and others. However, few of them explore hidden recurrence patterns in nonlinear network traffic and none of them explores it together with the Adaptive Clustering. This work proposes a new method, called DDoSbyRQA, which uses the Recurrence Quantification Analysis (RQA) based on the extraction of network traffic dynamic features and the combination with an Adaptive Clustering Algorithm (A-Kmeans) to detect DDoS attacks. The experiments were made by using the CAIDA and UCLA databases and it has demonstrated the ability of the method to increase the accuracy of DDoS detection and to real-time applicability. Keywords— DDoS, RQA, Adaptive Clustering, A-Kmeans.

Monitoring Runtime Metrics of Fog Manufacturing via a Qualitative and Quantitative (QQ) Control Chart

Abstract: Fog manufacturing combines Fog and Cloud computing in a manufacturing network to provide efficient data analytics and support real-time decision-making. Detecting anomalies, including imbalanced computational workloads and cyber-attacks, is critical to ensure reliable and responsive computation services. However, such anomalies often concur with dynamic offloading events where computation tasks are migrated from well-occupied Fog nodes to less-occupied ones to reduce the overall computation time latency and improve the throughput. Such concurrences jointly affect the system behaviors, which makes anomaly detection inaccurate. We propose a qualitative and quantitative (QQ) control chart to monitor system anomalies through identifying the changes of monitored runtime metric relationship (quantitative variables) under the presence of dynamic offloading (qualitative variable) using a risk-adjusted monitoring framework. Both the simulation and Fog manufacturing case studies show the advantage of the proposed method compared with the existing literature under the dynamic offloading influence.