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Based on immunocomputing, this paper describes an approach to intrusion detection. The approach includes both low-level signal processing (feature extraction) and high-level (intelligent) pattern recognition. The key model is the formal immune network (FIN) including apoptosis (programmed cell death) and immunization, both controlled by cytokines (messenger proteins). Such FIN can be formed from the network traffic signals using discrete tree transforms, singular value decomposition, and the proposed index of inseparability as a measure of quality of FIN. Recent results suggest that the approach outperforms (by training time and accuracy) state-of-the-art approaches of computational intelligence.