Intrusion Detection with Neural Networks

By J. Ryan, M. Lin, Risto Miikkulainen. 1997

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Citations in the corpus

Defeating Image Obfuscation with Deep Learning (2016, background): “For example, Cannady et al. [5] and Ryan et al. [41] used neural networks in intrusion detection systems (IDS).”
A new intrusion detection system using support vector machines and hierarchical clustering (2007, background): “A few other groups [7, 31] advocate the use of neural networks in intrusion detection.”
A hybrid machine learning approach to network anomaly detection (2007, methodology): “Also, Ryan et al. [36] uses artiﬁcial neural networks with supervised learning, based on the back-propagation neural network, called Neural Network Intrusion Detector, was trained in the identiﬁcation task and tests experimentally on a system of 10 users.”
Feature-Guided Black-Box Safety Testing of Deep Neural Networks (2017, background): “Deep neural networks (DNNs or networks, for simplicity) have been developed for a variety of tasks, including malware detection [11], abnormal network activity detection [31], and self-driving cars [5,6,32].”
Intrusion detection using an ensemble of intelligent paradigms (2005, background): “Neural networks have been extensively used to identify both misuse and anomalous patterns (Debar and Dorizzi, 1992; Debar et al., 1992; Ryan et al., 1997; Cannady, 1998; Mukkamala et al., 2001).”
The use of computational intelligence in intrusion detection systems: A review (2010, background): “formation, such as command sets, CPU usage, login host addresses, to distinguish between normal and abnormal behavior, while [238] considered the patterns of commands and their frequency.”
Deep Learning With Edge Computing: A Review (2019, background): “Using deep learning for network functions, such as for intrusion detection [49], [50] and wireless scheduling [51], has been proposed.”
Statistical fraud detection: A review (2002, methodology): “One of the most elaborate money laundering detection systems is the US Financial Crimes Enforcement Network AI system (FAIS), described in Senator et al (1995) and Goldberg and Senator (1998).”
Modeling intrusion detection system using hybrid intelligent systems (2007, background): “Neural network intrusion detector (NNID) (Ryan et al., 1998) identiﬁes intrusions based on the distribution of commands used by the user.”
Host-based intrusion detection using dynamic and static behavioral models (2003, background): “While novelty detection problems appear to be similar to 2-class classi/cation problems, with the two classes corresponding to normal and abnormal patterns respectively, a major difference is that novelty detection methods typically use only training examples from the class corresponding to normal…”
Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streams (2017, background): “Ryan et al. (1998) train a standard neural network with one hidden layer to predict the probabilities that each of a set of ten users created a distribution of Unix commands for a given day.”
Anomalous Instance Detection in Deep Learning: A Survey (2020, background): “In [81], a neural network based intrusion detector is trained to identify intruders.”
Identifying important features for intrusion detection using support vector machines and neural networks (2003, background): “Since most of the intrusions can be uncovered by examining patterns of user activities, many intrusion detection systems have been built by utilizing the recognized attack and misuse patterns to develop learning machines [1,2,3,4,5,6,7,8,9].”
Effective detection of sophisticated online banking fraud on extremely imbalanced data (2012, background): “Anomaly detection tries to establish a historical normal profile for each user and then uses a sufficiently large deviation from the profile to indicate possible intrusions [30, 55].”