Abstract:

In order to avoid the drawbacks caused by network node attacks， a complex network node attack detection algorithm based on incremental learning is proposed. Firstly，this method utilizes threshold self-learning to denoise the signals contained in the detected complex network；Secondiy， uses a combination of support vector machine and network node attack feature extraction principles to achieve accurate extraction of complex network node attack features； Finally， the extracted node attack features are input into an improved hybrid neural network and incremental learning is carried out to achieve the classification of node attack features and achieve precise detection of complex network node attacks. Through experimental verification， the proposed method can accurately detect node attacks in various complex networks smoothly and efficiently.

Key words: incremental learning, complex network, attack detection algorithm, network denoising, feature extraction