Abstract:

Intrusion detection method based on machine learning is applied in imbalanced intrusion datasets, mostly focused on enhancing the overall detection rate and reduce the overall failure rate, but the detection rates of minority classes are low, a good classification performance of the minority classes in practical application is also important. Therefore, an intrusion detection method for the SMOTE based on the maximum dissimilarity coefficient density algorithm with DBN (Deep Belief Network) and GBDT (Gradient Boosting Decision Tree) is proposed. Its core idea: in the data preprocessing stage, the SMOTE algorithm based on the maximum dissimilarity coefficient density is applied for data oversampling, and Deep Belief Network is used for feature extraction. In this way, improving the number of minority samples, and increasing the number of samples while reducing the number of sample dimensions, then training GBDT classifier on the balanced datasets, and the experimental verification is carried out by using the NSLKDD datasets. Experimental results show that ,while the proposed method maintains a high overall detection rate, the effect of minority detection is improved significantly, which improves the detection ability of intrusion detection for minority attack.

Key words: intrusion detection, maximum dissimilarity coefficient, density, SMOTE algorithm, DBN, GBDT