Intrusion Detection Immune Network Algorithm
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Solution Overview
Problem
Traditional intrusion detection algorithms face challenges with handling noisy data and blurred boundaries, leading to low detection accuracy and high false alarm rates, especially when dealing with complex and dynamic network environments.
Innovation Solution
An improved immune network algorithm that initializes an antibody group, calculates affinities between antigens and antibodies, identifies duality antigens, determines boundary and neighbor antibody sets, clones and mutates antibodies, and simplifies the network to enhance detection accuracy and reduce false alarms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional clustering algorithms are used for intrusion detection, then the algorithm is simple to implement, but the detection accuracy is low and the algorithm is easily stuck in local optimum when applied to large-scale intrusion detection
Solution Approach 1:
The patent segments the intrusion detection process into multiple stages: data preprocessing with PCA, affinity calculation between antigens and antibodies, boundary antibody identification, and iterative optimization. This segmentation allows each stage to address specific aspects of the problem, improving overall detection accuracy while maintaining implementation feasibility
Solution Approach 2:
The patent introduces dynamic optimization through iterative affinity calculations and adaptive boundary antibody identification. The algorithm dynamically adjusts antibody affinities and reclassifies boundary antibodies in each iteration, enabling the system to escape local optima and improve detection accuracy over time
2Adaptability or versatility
If traditional immune network algorithm is used, then the algorithm has certain adaptability, but the processing efficiency is low and the detection accuracy is low due to inability to extract effective features from massive data
Solution Approach 1:
The patent applies PCA dimensionality reduction as a preliminary action before the immune network algorithm processes the data. This preprocessing step extracts effective features from massive intrusion detection data, reducing computational complexity and improving processing efficiency while preserving the adaptability of the immune network algorithm
Solution Approach 2:
The patent changes key parameters of the immune network algorithm, including the affinity calculation method and the boundary antibody identification threshold. These parameter changes optimize the balance between adaptability and processing efficiency, enabling the algorithm to handle massive data effectively
3Adaptability or versatility
If traditional immune network algorithm is used, then the algorithm can process intrusion detection data, but the network structure becomes complex and the training efficiency is low when dealing with noisy data and blurred boundaries
Solution Approach 1:
The patent extracts and separately processes boundary antibodies from the main network structure. By identifying and handling boundary antibodies specifically through affinity comparisons with duality antigens, the algorithm simplifies the overall network structure while improving its ability to handle noisy data and blurred boundaries
4Ease of operation
If traditional immune network algorithm is used, then the algorithm initializes antibody groups, but the detection accuracy is low and false alarm rate is high when the training set contains information noise or blurred boundaries
Solution Approach 1:
The patent implements feedback mechanisms through iterative affinity calculations and boundary antibody reclassification. In each iteration, the algorithm recalculates affinities, identifies new boundary antibodies, and reassigns them based on updated information. This feedback loop continuously improves detection accuracy and reduces false alarm rates even when the training set contains noise or blurred boundaries
Data Source
AI summary
The present disclosure belongs to the technical field of intrusion detection, specifically provides an intrusion detection method based on an improved immune network algorithm, and an application thereof The method includes: S1, initializing an antibody group; S2, calculating affinities between antigens and antibodies; S3, searching for a pair of inhomogeneous antigens which have the highest affinities and referred to as a duality antigen; S4, determining a boundary antibody set C; S5, determining a neighbor antibody set; S6, cloning and mutating, according to the affinities, obsolete antibodies to update a subnetwork to which the antigens belong; S7, calculating an average affinity between the antibodies in the boundary antibody set C and the antigens in the duality antigen; and S8, inhibiting the network, and simplifying the network to output a result network subset. The solution has relatively high detection accuracy and relatively low false alarm rate.