Research of Recognition System of Web Intrusion Detection Based on Storm
| Title | Research of Recognition System of Web Intrusion Detection Based on Storm |
| Publication Type | Conference Paper |
| Year of Publication | 2016 |
| Authors | Bo, Li, Jinzhen, Wang, Ping, Zhao, Zhongjiang, Yan, Mao, Yang |
| Conference Name | Proceedings of the Fifth International Conference on Network, Communication and Computing |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-4793-8 |
| Keywords | Big Data, composability, cosine similarity, cyber physical systems, False Data Detection, Human Behavior, pubcrawl, Resiliency, Strom, TF-IDF, Web Intrusion Detection System |
| Abstract | Based on Storm, a distributed, reliable, fault-tolerant real-time data stream processing system, we propose a recognition system of web intrusion detection. The system is based on machine learning, feature selection algorithm by TF-IDF(Term Frequency-Inverse Document Frequency) and the optimised cosine similarity algorithm, at low false positive rate and a higher detection rate of attacks and malicious behavior in real-time to protect the security of user data. From comparative analysis of experiments we find that the system for intrusion recognition rate and false positive rate has improved to some extent, it can be better to complete the intrusion detection work. |
| URL | http://doi.acm.org/10.1145/3033288.3033319 |
| DOI | 10.1145/3033288.3033319 |
| Citation Key | bo_research_2016 |