| Title | Clustering Based Incident Handling For Anomaly Detection in Cloud Infrastructures |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Raj, C., Khular, L., Raj, G. |
| Conference Name | 2020 10th International Conference on Cloud Computing, Data Science Engineering (Confluence) |
| Keywords | Anomaly, anomaly detection, anomaly detection mechanisms, cloud, cloud computing, cloud infrastructures, Cloud Security, Clustering algorithms, clustering based algorithm, data mining, DBSCAN, decision based approach, f-score, incident handling model, isolation forest, Isolation Forest algorithm, Kmeans, local outlier factor(LOF), Monitoring, nonclustering based algorithm, NSL-KDD, pattern clustering, Prediction algorithms, Protocols, pubcrawl, Resiliency, run time metrics, Scalability, security of data, Servers, work factor metrics |
| Abstract | Incident Handling for Cloud Infrastructures focuses on how the clustering based and non-clustering based algorithms can be implemented. Our research focuses in identifying anomalies and suspicious activities that might happen inside a Cloud Infrastructure over available datasets. A brief study has been conducted, where a network statistics dataset the NSL-KDD, has been chosen as the model to be worked upon, such that it can mirror the Cloud Infrastructure and its components. An important aspect of cloud security is to implement anomaly detection mechanisms, in order to monitor the incidents that inhibit the development and the efficiency of the cloud. Several methods have been discovered which help in achieving our present goal, some of these are highlighted as the following; by applying algorithm such as the Local Outlier Factor to cancel the noise created by irrelevant data points, by applying the DBSCAN algorithm which can detect less denser areas in order to identify their cause of clustering, the K-Means algorithm to generate positive and negative clusters to identify the anomalous clusters and by applying the Isolation Forest algorithm in order to implement decision based approach to detect anomalies. The best algorithm would help in finding and fixing the anomalies efficiently and would help us in developing an Incident Handling model for the Cloud. |
| DOI | 10.1109/Confluence47617.2020.9058314 |
| Citation Key | raj_clustering_2020 |
Clustering Based Incident Handling For Anomaly Detection in Cloud Infrastructures