A survey of deep learning-based network anomaly detection

By Donghwoon Kwon, Hyunjoo Kim, Jinoh Kim, S. Suh, Ikkyun Kim, Kuinam J. Kim. Cluster Computing, 2017

738 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/5b59992ca6b77aaec066a0d3142336d2cb1028f1

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In Artificial Intelligence

In Computer Networks and Communications

Citations in the corpus

Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series (2018, background): “As such, researchers have moved beyond specification or signature-based techniques and begun to exploit both supervised and unsupervised machine learning techniques to develop more intelligent and adaptive methods from big data to identify anomalies or intrusions [2].”
Machine Learning and Deep Learning Methods for Cybersecurity (2018, background): “The Restricted Boltzmann Machine (RBM) and DBN are interrelated because composing and stacking a number of RBMs enables many hidden layers to train data efﬁciently through activations of one RBM for further training stages [55].”
A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications (2020, background): “the images in the previous example) to the final outputs without the need to extract features in advance [87].”
Deep Learning for Anomaly Detection: A Survey (2019, background): “A broad review of deep anomaly detection (DAD) techniques for cyber-intrusion detection is presented by Kwon et al. [2017].”
A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges (2019, neutral): “anomaly-based IDS [195]–[198].”
A Comprehensive Survey on Graph Anomaly Detection With Deep Learning (2021, methodology): “Other surveys are dedicated to particular applications of graph anomaly detection, such as computer network intrusion detection and anomaly detection in online social networks, e.g., [3], [6], [19], and [50], [51], [52], [53], [54].”
A Survey on Anomaly Detection for Technical Systems using LSTM Networks (2021, methodology): “An architectural analysis of deep-learning-based anomaly detection approaches with a focus on Boltzmann machines, Autoencoders (AE) and RNN is given by [6].”
Progress in Outlier Detection Techniques: A Survey (2019, background): “Recently, more attention has been given to deep learning in many areas including several studies related to outlier detection problems [35], [36] [30], [207]–[209].”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, background): “Modern cyber security systems benefit increasingly from deep learning [403], since it can enable the system to (i) automatically learn signatures and patterns from experience and generalize to future intrusions (supervised learning); or (ii) identify patterns that are clearly differed from regular b”
Machine Learning for Anomaly Detection: A Systematic Review (2021, neutral): “On the other hand, Kwon et al. [28] mainly focused on deep learning techniques, such as restricted Boltzmann machine based deep belief networks, deep recurrent neural networks, as well as machine learning methods appropriate to network anomaly detection.”
A Unifying Review of Deep and Shallow Anomaly Detection (2020, background): “A cluster of anomalies, such as similar or related network attacks in cybersecurity, forms a collective anomaly, for instance [18], [208], [209].”
A Survey on Distributed Machine Learning (2019, background): “They are often used for the following tasks [85]:”
Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research (2022, methodology): “3 [15] 2017 N/A N/A N/A √ N/A N/A √ √ ≈ N/A √”