Design of anomaly-based intrusion detection and prevention system for smart city web application using rule-growth sequential pattern mining

Nowadays, with the increasing use of internet, many private sectors and governments started to conduct their web applications as a part in smart city development. They were aimed to provide reliable services to the community by implementing online services with web-based approach. To ensure the sustainability of the online application on the smart city environment, the system design should pay attention on information security aspect, with three main key principles as confidentiality, integrity, and availability. This paper proposes a sequential pattern analysis on web usage, a novel intrusion detection and prevention system design, which uses Rule-Growth sequential rule-patterns algorithm to detect intrusions on user behaviors. By comparing the predefined user behavior baseline patterns to malicious patterns, the proposed model can identify the potential intrusions.