Streaming Botnet traffic analysis using bio-inspired active learning

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Khanchi:2018:NOMS,

• author = "Sara Khanchi and Nur Zincir-Heywood and Malcolm Heywood",
• booktitle = "NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium",
• title = "Streaming Botnet traffic analysis using bio-inspired active learning",
• year = "2018",
• abstract = "Non-stationary network traffic, together with stealth occurrences of malicious behaviours, make analysing network traffic challenging. In this research, a machine learning framework is used to incrementally learn the network behavior and adapt to the changes in the traffic. This framework works under two main constraints: 1) label budget, 2) class imbalance; which makes it suitable for real-world network scenarios. Evaluations are performed on a public dataset with multiple Botnet scenarios under 0.5percent and 5percent label budgets; only around 2.2percent of traffic is Botnet. Our results demonstrate the significance of the proposed Stream Genetic Programming solution and a general robustness to factors such as long latencies between instances of the same Botnet.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/NOMS.2018.8406293",
• ISSN = "2374-9709",
• month = apr,
• notes = "Also known as \cite{8406293}",

}

Genetic Programming entries for Sara Khanchi Nur Zincir-Heywood Malcolm Heywood

Citations