Open AccessPerformance analysis of flow-based attacks detection on CSE-CIC-IDS2018 dataset using deep learning
Author(s) -
Rawaa Ismael Farhan,
Abeer Tariq Maolood,
Nidaa Flaih Hassan
Publication year - 2020
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v20.i3.pp1413-1418
Subject(s) - intrusion detection system , computer science , deep learning , artificial intelligence , anomaly detection , field (mathematics) , internet of things , machine learning , network security , ideal (ethics) , the internet , computer security , world wide web , philosophy , mathematics , epistemology , pure mathematics
The emergence of the Internet of Things (IOT) as a result of the development of the communications system has made the study of cyber security more important. Day after day, attacks evolve and new attacks are emerged. Hence, network anomaly-based intrusion detection system is become very important, which plays an important role in protecting the network through early detection of attacks. Because of the development in machine learning and the emergence of deep learning field, and its ability to extract high-level features with high accuracy, made these systems involved to be worked with real network traffic CSE-CIC-IDS2018 with a wide range of intrusions and normal behavior is an ideal way for testing and evaluation . In this paper , we test and evaluate our deep model (DNN) which achieved good detection accuracy about 90% .