TOWARDS AN IMMUNE-INSPIRED TEMPORAL ANOMALY DETECTION ALGORITHM BASED ON TUNABLE ACTIVATION THRESHOLDS

• Type: Article in International Conference Proceedings Book
• Date: 2009

Authors

Mario Antunes (Author) Other; Manuel Correia (Author) FCUP; Jorge Carneiro (Author) Other

Conference proceedings International

Title: ARTIFICIAL NEURAL NETWORK APPROACH FOR OBESITY-HYPERTENSION CLASSIFICATION

Pages: 357-362

BIOSTEC 2009 - Second International Joint Conference on Biomedical Engineering Systems and Technologies

Vila Nova de Gaia, Portugal, 14 a 17 de Janeiro de 2009

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 4 Citations

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

Other information

Authenticus ID: P-003-PA2

Abstract (EN): The detection of anomalies in computer environments, like network intrusion detection, computer virus or spam classification, is usually based on some form of pattern search on a database of "signatures " for known anomalies. Although very successful and widely deployed, these approaches are only able to cope with anomalous events that have already been seen. To cope with these weaknesses, the "behaviour" based systems has been deployed. Although conceptually more appealing, they have still an impractical high rate of false alarms. The vertebrate Immune System is an emergent and appealing metaphor for new ideas on anomaly detection, being already adopted some algorithms and theoretical theories in particular fields, such as network intrusion detection. In this paper we present a temporal anomaly detection architecture based on the Grossman's Tunable Activation Threshold (TAT) hypothesis. The basic idea is that the repertoire of immune cells is constantly tuned according to the cells temporal interactions with the environment and yet retains responsiveness to an open-ended set of abnormal events. We describe some preliminary work on the development of an anomaly detection algorithm derived from TAT and present the results obtained thus far using some synthetic data-sets.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6