scholarly journals State of the Art Literature Review on Network Anomaly Detection with Deep Learning

2018 ◽  
pp. 64-76 ◽  
Author(s):  
Tero Bodström ◽  
Timo Hämäläinen
Keyword(s):  
Deep Learning ◽  
Literature Review ◽  
Anomaly Detection ◽  
State Of The Art ◽  
Network Anomaly Detection

scholarly journals Multiresolution dendritic cell algorithm for network anomaly detection

2021 ◽  
Vol 7 ◽  
pp. e749
Author(s):  
David Limon-Cantu ◽  
Vicente Alarcon-Aquino
Keyword(s):  
Information Systems ◽  
Dendritic Cell ◽  
Anomaly Detection ◽  
State Of The Art ◽  
Denial Of Service ◽  
Series Data ◽  
Time Frequency ◽  
Computer Services ◽  
Dendritic Cell Algorithm ◽  
Network Anomaly Detection

Anomaly detection in computer networks is a complex task that requires the distinction of normality and anomaly. Network attack detection in information systems is a constant challenge in computer security research, as information systems provide essential services for enterprises and individuals. The consequences of these attacks could be the access, disclosure, or modification of information, as well as denial of computer services and resources. Intrusion Detection Systems (IDS) are developed as solutions to detect anomalous behavior, such as denial of service, and backdoors. The proposed model was inspired by the behavior of dendritic cells and their interactions with the human immune system, known as Dendritic Cell Algorithm (DCA), and combines the use of Multiresolution Analysis (MRA) Maximal Overlap Discrete Wavelet Transform (MODWT), as well as the segmented deterministic DCA approach (S-dDCA). The proposed approach is a binary classifier that aims to analyze a time-frequency representation of time-series data obtained from high-level network features, in order to classify data as normal or anomalous. The MODWT was used to extract the approximations of two input signal categories at different levels of decomposition, and are used as processing elements for the multi resolution DCA. The model was evaluated using the NSL-KDD, UNSW-NB15, CIC-IDS2017 and CSE-CIC-IDS2018 datasets, containing contemporary network traffic and attacks. The proposed MRA S-dDCA model achieved an accuracy of 97.37%, 99.97%, 99.56%, and 99.75% for the tested datasets, respectively. Comparisons with the DCA and state-of-the-art approaches for network anomaly detection are presented. The proposed approach was able to surpass state-of-the-art approaches with UNSW-NB15 and CSECIC-IDS2018 datasets, whereas the results obtained with the NSL-KDD and CIC-IDS2017 datasets are competitive with machine learning approaches.

Time Series in Sensor Data Using State-of-the-Art Deep Learning Approaches: A Systematic Literature Review

2021 ◽  
pp. 503-514
Author(s):  
Luis-Roberto Jácome-Galarza ◽  
Miguel-Andrés Realpe-Robalino ◽  
Jonathan Paillacho-Corredores ◽  
José-Leonardo Benavides-Maldonado
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
State Of The Art ◽  
Sensor Data ◽  
Learning Approaches

Network anomaly detection with deep learning

2018 ◽  
Author(s):  
Ugur Cekmez ◽  
Zeki Erdem ◽  
Ali Gokhan Yavuz ◽  
Ozgur Koray Sahingoz ◽  
Ali Buldu
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Network Anomaly Detection

scholarly journals An Empirical Evaluation of Deep Learning for Network Anomaly Detection

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 140806-140817 ◽  
Author(s):  
Ritesh K. Malaiya ◽  
Donghwoon Kwon ◽  
Sang C. Suh ◽  
Hyunjoo Kim ◽  
Ikkyun Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Empirical Evaluation ◽  
Network Anomaly Detection

A survey of deep learning-based network anomaly detection

2017 ◽  
Vol 22 (S1) ◽  
pp. 949-961 ◽  
Author(s):  
Donghwoon Kwon ◽  
Hyunjoo Kim ◽  
Jinoh Kim ◽  
Sang C. Suh ◽  
Ikkyun Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Network Anomaly Detection
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