Intrusion detection method by use of improved self-organizing feature neural network clustering algorithm
A clustering algorithm and neural network technology, applied in the field of machine learning and intrusion detection, intrusion detection, can solve the problem of neuron position deviation and reduce the possible effect of human control
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[0020] Such as figure 1 As shown, the present invention provides a kind of intrusion detection method that adopts improved self-organizing feature neural network clustering algorithm to comprise: by data cleaning, two-level clustering algorithm (Canopy) and improved self-organizing feature mapping neural network clustering method (SOFM) and abnormal intrusion detection, as follows:
[0021] 1.1 Data cleaning
[0022] Such as figure 2 As shown, the data to be detected comes from log files in the cloud storage system environment. For unstructured log files, data structure initialization processing is required here to make the data to be detected meet the input format. The algorithm description is shown in Algorithm 1.
[0023] Algorithm 1. Data cleaning algorithm description
[0024] Input: the log file log_file.txt under the cloud storage system, the regular expression reg for extracting feature attributes
[0025] Output: structured training data dataSet
[0026] 1. Tra...
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