Artificial Neural Networks for Misuse Detection

By James Cannady. 1998

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

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).”
An intelligent intrusion detection system (IDS) for anomaly and misuse detection in computer networks (2005, background): “The use of comprehensive rules is critical in the application of expert systems for intrusion detection (Cannady, 1998).”
Modeling intrusion detection system using hybrid intelligent systems (2007, methodology): “A neural network for misuse detection is implemented in two ways (Cannady, 1998).”
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).”
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].”
The use of computational intelligence in intrusion detection systems: A review (2010, methodology): “[40] applied MLFF-BP on 10,000 network packets collected from a simulated network environment for misuse detection purpose.”
Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches (2020, methodology): “Almost 30 years ago, the NN was already used for misuse detection in cyber-physical systems [118].”
Decision tree based light weight intrusion detection using a wrapper approach (2012, methodology): “There are two approaches for implementing neural network for misuse detection (Cannady, 1998).”
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection (2016, methodology): “Unlike the previous study by Cannady [24], which classified each packet-level data separately, this system used time windows to perform the detection and classified a group of packets.”
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 comprehensive survey on machine learning for networking: evolution, applications and research opportunities (2018, methodology): “Hence, it comes as no surprise to see a fair amount of literature [20, 84, 90, 252, 322, 344, 354, 402, 421] that rely onML for misuse-detection.”
A survey of intrusion detection techniques in Cloud (2013, unknown): “J. Cannady [37] proposed a three layer neural network for misuse detection in network.”
Next Generation Intrusion Detection: Autonomous Reinforcement Learning of Network Attacks (2000, methodology): “Cannady (1998) demonstrated the use of multi-level perceptron/SOM hybrid neural networks for misuse detection in the identification of computer attacks and Bonifacio (1998) demonstrated the use of a neural network in an integrated detection system.”