Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Towards an effective cooperation of the user and the computer for classification
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive Intrusion Detection: A Data Mining Approach
Artificial Intelligence Review - Issues on the application of data mining
NATE: Network Analysis of Anomalous Traffic Events, a low-cost approach
Proceedings of the 2001 workshop on New security paradigms
Identifying enterprise network vulnerabilities
International Journal of Network Management
IEEE Transactions on Visualization and Computer Graphics
Benchmarking Anomaly-Based Detection Systems
DSN '00 Proceedings of the 2000 International Conference on Dependable Systems and Networks (formerly FTCS-30 and DCCA-8)
Maximum and Minimum Likelihood Hebbian Learning for Exploratory Projection Pursuit
Data Mining and Knowledge Discovery
Detecting Flaws and Intruders with Visual Data Analysis
IEEE Computer Graphics and Applications
Scatter (and other) plots for visualizing user profiling data and network traffic
Proceedings of the 2004 ACM workshop on Visualization and data mining for computer security
Intrusion detection using hierarchical neural networks
Pattern Recognition Letters
Complexity Pursuit: Separating Interesting Components from Time Series
Neural Computation
Hierarchical Visualization of Network Intrusion Detection Data
IEEE Computer Graphics and Applications
Focusing on Context in Network Traffic Analysis
IEEE Computer Graphics and Applications
A clustering-based method for unsupervised intrusion detections
Pattern Recognition Letters
An Anomaly Intrusion Detection System Based on Vector Quantization
IEICE - Transactions on Information and Systems
Factor-analysis based anomaly detection and clustering
Decision Support Systems
A hierarchical SOM-based intrusion detection system
Engineering Applications of Artificial Intelligence
Intrusion detection using a fuzzy genetics-based learning algorithm
Journal of Network and Computer Applications - Special issue: Network and information security: A computational intelligence approach
Data mining approaches for intrusion detection
SSYM'98 Proceedings of the 7th conference on USENIX Security Symposium - Volume 7
Visual Discovery in Computer Network Defense
IEEE Computer Graphics and Applications
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
Computational Statistics & Data Analysis
Detecting compounded anomalous SNMP situations using cooperative unsupervised pattern recognition
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Learning intrusion detection: supervised or unsupervised?
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Training genetic programming on half a million patterns: an example from anomaly detection
IEEE Transactions on Evolutionary Computation
Hierarchical Kohonenen net for anomaly detection in network security
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Min-max hyperellipsoidal clustering for anomaly detection in network security
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An Automatically Tuning Intrusion Detection System
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
ViSOM - a novel method for multivariate data projection and structure visualization
IEEE Transactions on Neural Networks
Testing ensembles for intrusion detection: On the identification of mutated network scans
CISIS'11 Proceedings of the 4th international conference on Computational intelligence in security for information systems
RT-MOVICAB-IDS: Addressing real-time intrusion detection
Future Generation Computer Systems
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A crucial aspect in network monitoring for security purposes is the visual inspection of the traffic pattern, mainly aimed to provide the network manager with a synthetic and intuitive representation of the current situation. Towards that end, neural projection techniques can map high-dimensional data into a low-dimensional space adaptively, for the user-friendly visualization of monitored network traffic. This work proposes two projection methods, namely, cooperative maximum likelihood Hebbian learning and auto-associative back-propagation networks, for the visual inspection of network traffic. This set of methods may be seen as a complementary tool in network security as it allows the visual inspection and comprehension of the traffic data internal structure. The proposed methods have been evaluated in two complementary and practical network-security scenarios: the on-line processing of network traffic at packet level, and the off-line processing of connection records, e.g. for post-mortem analysis or batch investigation. The empirical verification of the projection methods involved two experimental domains derived from the standard corpora for evaluation of computer network intrusion detection: the MIT Lincoln Laboratory DARPA dataset.