Network Anomaly Detection: Methods, Systems and Tools

By Monowar Hussain Bhuyan, Dhruba Kumar Bhattacharyya, Jugal Kumar Kalita. IEEE Communications Surveys and Tutorials, 2014

1,294 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/e467ff1ea3a0c535a0cef87f7e0daa414d524d9d

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Citations in the corpus

User Profile Preserving Social Network Embedding (2017, background): “These approaches have been shown to be effective in a variety of network analytic tasks, ranging from node classification [Sen et al., 2008], anomaly detection [Bhuyan et al., 2014], community detection [Yang et al., 2013], to link prediction [Lü and Zhou, 2011].”
Performance analysis of NSL-KDD dataset using ANN (2015, positive): “Bhuyan et al [12] discussed different methods of anomaly detection on his survey paper that are statistical, classification based, clustering and outlier based, soft computing, knowledge based and combination learners.”
Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey (2023, methodology): “Most outlier detection-based NIDS uses statistical measures such as K-Nearest-Neighbour (KNN), One-Class SVM (OC-SVM), and the Markov chain model [86].”
Homophily, Structure, and Content Augmented Network Representation Learning (2016, background): “Existing studies on network representation learning (NRL) [1, 2] have shown that NRL can effectively capture network structure to facilitate subsequent analytic tasks, such as node classification [3], anomaly detection [4], and link prediction [5].”
Survey of intrusion detection systems: techniques, datasets and challenges (2019, background): “On the other hand, NIDSs have limited ability to inspect all data in a high bandwidth network because of the volume of data passing through modern high-speed communication networks (Bhuyan et al., 2014).”
A Survey of Network-based Intrusion Detection Data Sets (2019, background): “[34] present a comprehensive review of network anomaly detection.”
Malware traffic classification using convolutional neural network for representation learning (2017, background): “In real application of traffic classification, the most common requirement is only the malware traffic identification, which is also the main goal of NIDS.”
Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey (2019, methodology): “Reference [9] presents a comprehensive study where the ML techniques represent more than 30% of the solutions found in this review.”
Machine Learning for Anomaly Detection: A Systematic Review (2021, methodology): “Several methods are used to conduct statistical anomaly detection [11].”
A Survey on Advanced Persistent Threats: Techniques, Solutions, Challenges, and Research Opportunities (2019, background): “Bhuyan et al. [49] have provided an overview of the extensive research done in network intrusion detection systems, speciﬁcally in network anomaly based detection approaches.”
Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT (2017, background): “The ﬁrst monitor and analyze the internals of a computer system while the second deal with attacks on the communication interfaces [2].”
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection (2016, methodology): “[2]; instead it concentrates only on ML and DM techniques.”
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018, neutral): “detection by distinguishing normal behaviour from abnormal behaviour by feature similarity calculations [181], [182].”