The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
The rectified Gaussian distribution
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Identifying enterprise network vulnerabilities
International Journal of Network Management
Learning Program Behavior Profiles for Intrusion Detection
Proceedings of the Workshop on Intrusion Detection and Network Monitoring
A Computer Host-Based User Anomaly Detection System Using the Self-Organizing Map
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5 - Volume 5
A Neural Network Component for an Intrusion Detection System
SP '92 Proceedings of the 1992 IEEE Symposium on Security and Privacy
Unsupervised learning techniques for an intrusion detection system
Proceedings of the 2004 ACM symposium on Applied computing
Maximum and Minimum Likelihood Hebbian Learning for Exploratory Projection Pursuit
Data Mining and Knowledge Discovery
Configurable string matching hardware for speeding up intrusion detection
ACM SIGARCH Computer Architecture News - Special issue: Workshop on architectural support for security and anti-virus (WASSA)
Complexity Pursuit: Separating Interesting Components from Time Series
Neural Computation
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Traffic Data Preparation for a Hybrid Network IDS
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
IDS Based on Bio-inspired Models
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
A comparison of neural projection techniques applied to intrusion detection systems
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Intrusion detection at packet level by unsupervised architectures
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Neural visualization of network traffic data for intrusion detection
Applied Soft Computing
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
Testing CAB-IDS through mutations: on the identification of network scans
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
MOVICAB-IDS: visual analysis of network traffic data streams for intrusion detection
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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This research employs unsupervised pattern recognition to approach the thorny issue of detecting anomalous network behavior. It applies a connectionist model to identify user behavior patterns and successfully demonstrates that such models respond well to the demands and dynamic features of the problem. It illustrates the effectiveness of neural networks in the field of Intrusion Detection (ID) by exploiting their strong points: recognition, classification and generalization. Its main novelty lies in its connectionist architecture, which up until the present has never been applied to Intrusion Detection Systems (IDS) and network security. The IDS presented in this research is used to analyse network traffic in order to detect anomalous SNMP (Simple Network Management Protocol) traffic patterns. The results also show that the system is capable of detecting independent and compounded anomalous SNMP situations. It is therefore of great assistance to network administrators in deciding whether such anomalous situations represent real intrusions.