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NetFlow data is a popular network log format used by many network analysts and researchers.
Isolation forest
Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou · 2008
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Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
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An empirical comparison of botnet detection methods
S. Garcia, M. Grill, J. Stiborek, and A. Zunino · 2014
Earlier work this paper cites.
UNSW-NB15: A comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)
Nour Moustafa and Jill Slay · 2015
Earlier work this paper cites.
Deeplog: Anomaly detection and diagnosis from system logs through deep learning
Min Du, Feifei Li, Guineng Zheng, and Vivek Srikumar · 2017
Earlier work this paper cites.
Learning behavioral fingerprints from Netflows using Timed Automata
Gaetano Pellegrino, Qin Lin, Christian Hammerschmidt, and Sicco Verwer · 2017
Earlier work this paper cites.
Big data analytics for network anomaly detection from netflow data
Duygu Sinanc Terzi, Ramazan Terzi, and Seref Sagiroglu · 2017
Earlier work this paper cites.
Flexfringe: A passive automaton learning package
Sicco Verwer and Christian A. Hammerschmidt · 2017
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Cisco IOS NetFlow - Cisco
Cisco Systems · 2018
Earlier work this paper cites.
UGR‘16: A new dataset for the evaluation of cyclostationarity-based network IDSs
Gabriel Maciá-Fernández, José Camacho, Roberto Magán-Carrión, Pedro García-Teodoro, and Roberto Therón · 2018
Cited alongside, same era.
Semi-supervised multivariate statistical network monitoring for learning security threats
Jose Camacho, Gabriel Macia-Fernandez, Noemi Marta Fuentes-Garcia, and Edoardo Saccenti · 2019
Cited alongside, same era.
Towards intelligible robust anomaly detection by learning interpretable behavioural models
Gudmund Grov, Wei Chen, Marc Sabate, and David Aspinall · 2019
Cited alongside, same era.
GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection
Quoc Phong Nguyen, Kar Wai Lim, Dinil Mon Divakaran, Kian Hsiang Low, and Mun Choon Chan · 2019
Cited alongside, same era.
MalAlert: Detecting malware in large-scale network traffic using statistical features
Michal Piskozub, Riccardo Spolaor, and Ivan Martinovic · 2019
Cited alongside, same era.
Towards a Reliable Comparison and Evaluation of Network Intrusion Detection Systems Based on Machine Learning Approaches
Roberto Magán-Carrión, Daniel Urda, Ignacio Díaz-Cano, and Bernabé Dorronsoro · 2020
Later among the works it cites.
Threat matrix for Kubernetes
Microsoft · 2020
Later among the works it cites.
Performance Evaluation of Botnet Detection using Deep Learning Techniques
Beny Nugraha, Anshitha Nambiar, and Thomas Bauschert · 2020
Later among the works it cites.
Intrusion detection system using voting-based neural network
Mohammad Hashem Haghighat and Jun Li · 2021
Later among the works it cites.
Efficient modelling of ics communication for anomaly detection using probabilistic automata
Petr Matoušek, Vojtěch Havlena, and Lukáš Holík · 2021
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Unsupervised anomaly detectors to detect intrusions in the current threat landscape
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A new malware detection system using a high performance-elm method
Shahab Shamshirband and Anthony T. Chronopoulos · 2019
Cited alongside, same era.
Expansion of Cyber Attack Data From Unbalanced Datasets Using Generative Techniques
Ibrahim Yilmaz and Rahat Masum · 2019
Cited alongside, same era.
Deep learning-based classification model for botnet attack detection
Abdulghani Ali Ahmed, Waheb A. Jabbar, Ali Safaa Sadiq, and Hiran Patel · 2020
Cited alongside, same era.
Efficient Distributed Preprocessing Model for Machine Learning-Based Anomaly Detection over Large-Scale Cybersecurity Datasets
Xavier Larriva-Novo, Mario Vega-Barbas, Víctor A. Villagrá, Diego Rivera, Manuel Álvarez-Campana, and Julio Berrocal · 2020
Cited alongside, same era.
https://assuremoss.eu/en/
AssureMOSS
Cited in the paper.
https://github.com/Thijsvanede/DeepLog
DeepLog - PyTorch implementation of Deeplog: Anomaly detection and diagnosis from system logs through deep learning
Cited in the paper.
https://github.com/tudelft-cda-lab/ENCODE
ENCODE: Encoding NetFlows for Network Anomaly Detection
Cited in the paper.
Tommaso Zoppi, Andrea Ceccarelli, Tommaso Capecchi, and Andrea Bondavalli · 2021
Later among the works it cites.
Meta-learning to improve unsupervised intrusion detection in cyber-physical systems
Tommaso Zoppi, Mohamad Gharib, Muhammad Atif, and Andrea Bondavalli · 2021
Later among the works it cites.
AssureMOSS Kubernetes Run-time Monitoring Dataset
Clinton Cao and Agathe Blaise · 2022
Closest in time.
Learning state machines to monitor and detect anomalies on a kubernetes cluster
Clinton Cao, Agathe Blaise, Sicco Verwer, and Filippo Rebecchi · 2022
Closest in time.