| Title | Rule-based User Behavior Detection System for SaaS Application |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Park, Jee-Tae, Baek, Ui-Jun, Kim, Myung-Sup, Lee, Min-Seong, Shin, Chang-Yui |
| Conference Name | 2022 23rd Asia-Pacific Network Operations and Management Symposium (APNOMS) |
| Keywords | Behavioral sciences, Costs, Licenses, pubcrawl, Resiliency, rule based traffic analysis, SaaS, Scalability, security, signature based defense, user behavior detection |
| Abstract | SaaS is a cloud-based application service that allows users to use applications that work in a cloud environment. SaaS is a subscription type, and the service expenditure varies depending on the license, the number of users, and duration of use. For efficient network management, security and cost management, accurate detection of user behavior for SaaS applications is required. In this paper, we propose a rule-based traffic analysis method for the user behavior detection. We conduct comparative experiments with signature-based method by using the real SaaS application and demonstrate the validity of the proposed method. |
| DOI | 10.23919/APNOMS56106.2022.9919933 |
| Citation Key | park_rule-based_2022 |
Rule-based User Behavior Detection System for SaaS Application