Intrusion Detection Systems with Deep Learning: A Systematic Mapping Study

Submitted by aekwall on Mon, 01/20/2020 - 11:56am

Title	Intrusion Detection Systems with Deep Learning: A Systematic Mapping Study
Publication Type	Conference Paper
Year of Publication	2019
Authors	Osken, Sinem, Yildirim, Ecem Nur, Karatas, Gozde, Cuhaci, Levent
Conference Name	2019 Scientific Meeting on Electrical-Electronics Biomedical Engineering and Computer Science (EBBT)
Date Published	apr
Keywords	Artificial neural networks, Classification algorithms, composability, Computational modeling, Databases, Deep Learning, deep learning algorithms, Intrusion detection, Intrusion Detection Systems, learning (artificial intelligence), neural nets, pubcrawl, Resiliency, security of data, systematic mapping method, systematic mapping study, Systematics
Abstract	In this study, a systematic mapping study was conducted to systematically evaluate publications on Intrusion Detection Systems with Deep Learning. 6088 papers have been examined by using systematic mapping method to evaluate the publications related to this paper, which have been used increasingly in the Intrusion Detection Systems. The goal of our study is to determine which deep learning algorithms were used mostly in the algortihms, which criteria were taken into account for selecting the preferred deep learning algorithm, and the most searched topics of intrusion detection with deep learning algorithm model. Scientific studies published in the last 10 years have been studied in the IEEE Explorer, ACM Digital Library, Science Direct, Scopus and Wiley databases.
DOI	10.1109/EBBT.2019.8742081
Citation Key	osken_intrusion_2019

Groups:

Science of Security VO