A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection

By Lalu Banoth, M. Teja, M. Saicharan, N. Chandra. 2017

1,789 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d7d9107de19eba8228bc599f53f013245760caee

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Citations in the corpus

A Survey of Network-based Intrusion Detection Data Sets (2019, background): “mal user behavior based on representative training data and tag deviations from the learned behavior as malicious [17].”
When Do Neural Nets Outperform Boosted Trees on Tabular Data? (2023, background): “They are the oldest and among the most ubiquitous dataset types in machine learning in practice [7, 55], due to their numerous applications across medicine [36, 58], finance [5, 13], online advertising [28, 45, 50], and many other areas [9, 10, 59].”
Variational LSTM Enhanced Anomaly Detection for Industrial Big Data (2021, background): “mentation, including the dimension reduction and feature extraction [2], is usually used in the potential low-dimensional space.”
A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends (2018, result): “Similar to existing research applying machine learning to conduct accurate cyber situation awareness [24], [41], [49], [56], [154], [158], the use of deep learning technologies for cyber security analysis and intrusion detection is highly relevant, as the majority of attacks use families of intrusiv”
Machine Learning for Anomaly Detection: A Systematic Review (2021, background): “Similarly, Patcha and Park [23] presented an extensive study of anomaly detection and intrusion detection techniques, and Buczak and Guven [24] surveyed machine learning and data mining methods for cyber intrusion detection.”
CICIDS-2017 Dataset Feature Analysis With Information Gain for Anomaly Detection (2020, methodology): “Bayesian classiﬁcation is based on the Bayes theorem [43].”
Network Intrusion Detection for IoT Security Based on Learning Techniques (2019, background): “present different learning IDSs in their surveys [15], [25], [26] where ML algorithms such as decision trees (DT), support vector machines (SVM), naive bayes (NB), artificial neural networks (ANN), k-means clustering, fuzzy logic, genetic algorithms, stacked auto encoder (SAE), were”
HAST-IDS: Learning Hierarchical Spatial-Temporal Features Using Deep Neural Networks to Improve Intrusion Detection (2018, background): “Some researchers have noted that the attack types considered in KDD99 are now obsolete [27].”
A Survey on Machine Learning Techniques for Cyber Security in the Last Decade (2020, methodology): “Machine learning techniques evolved to purpose new ways of intrusion detection due to the increase of bandwidth [73, 204].”
An Overview on Application of Machine Learning Techniques in Optical Networks (2018, background): “Some applications of ML in different networking areas have already matched these expectations in areas such as intrusion detection [2], traffic classification [3], cognitive radios [4].”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, background): “Cyber security systems guard mobile devices and users through firewalls, anti-virus software, and Intrusion Detection Systems (IDS) [401].”
Learning Universal Adversarial Perturbations with Generative Models (2017, background): “Machine Learning models are increasingly relied upon for safety and business critical tasks such as in medicine [23], [29], [39], robotics and automotive [27], [31], [38], security [2], [17], [36] and ﬁnancial [13], [18], [34] applications.”
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks (2019, methodology): “ML and data mining assisted cyber intrusion detection were surveyed in [35], including the complexity comparison of each algorithm and a set of recommendations concerning the best methods applied to different cyber intrusion detection problems.”
Machine Learning and Deep Learning Methods for Cybersecurity (2018, background): “Buczakand Guven [9] focus on machine-learning methods and their applications to intrusion detection.”
Deep Neural Networks and Tabular Data: A Survey (2021, background): “…the recommendation of investment strategies, and portfolio management) [14], click-through rate (CTR) prediction [15], user recommendation systems [16], [17], customer churn prediction [18], cybersecurity [19], fraud detection [20], psychology [21], anomaly detection [22], [23], [24], and so forth.”
Lucid: A Practical, Lightweight Deep Learning Solution for DDoS Attack Detection (2020, background): “The 2016 Buczak and Guven survey [41] cites the use of Support Vector Machine”
Smart Cities: A Survey on Data Management, Security, and Enabling Technologies (2017, background): “Buczak and Guven [156] focus on surveying machine learning use in support for intrusion detection.”