Browse wiki

From Navigators

Jump to: navigation, search
Abstract Current machine learning approaches for ne Current machine learning approaches for network-based intrusion detection do not cope with new network traffic behavior, which requires periodic computationally and time-consuming model updates. This paper proposes a novel stream learning intrusion detection model that maintains system accuracy, even in the presence of unknown traffic behavior. It also facilitates the process of updating the model, gradually incorporating new knowledge into the machine learning model. Our experiments were performed using a recent realistic dataset of network behaviors and they have shown that the proposed technique detects potentially unreliable classifications. Moreover, the proposed model can incorporate the new network traffic behavior from model updates to improve the system accuracy while maintaining its reliability. ccuracy while maintaining its reliability.
Author Eduardo Viegas + , Altair Santin + , Vinicius Vielmo Cogo + , Vilmar Abreu +
Booktitle Proceedings of the 34th International Conference on Advanced Information Networking and Applications (AINA)  +
Document Document for Publication-Viegas2020facing.pdf +
Key Viegas2020facing  +
Month apr  +
NumPubDate 2,020.04  +
ResearchLine Fault and Intrusion Tolerance in Open Distributed Systems (FIT) +
Title Facing the Unknown: a Stream Learning Intrusion Detection System for Reliable Model Updates  +
Type inproceedings  +
Year 2020  +
Has improper value forThis property is a special property in this wiki. Url  +
Categories Publication  +
Modification dateThis property is a special property in this wiki. 28 February 2020 18:55:25  +
show properties that link here 


Enter the name of the page to start browsing from.
Personal tools
Navigators toolbox