Abstract
With the development of information technology, computer networks have become a part of people’s lives and work. However, computer viruses and malicious network attacks make network security face huge challenges, and more accurate detection of attacks has become the focus of attention to current computer fields. This paper proposes an intrusion detection model, which is mainly based on the XGBoost (Extreme Gradient Boosting), and uses the WOA (Whale Optimization Algorithm) to find the best parameters for it. The collected network data are first preprocessed by the PCA (Principal Component Analysis) dimensionality reduction method, and then, the preprocessed data are imported into the WOA-XGBoost algorithm so that the overall model has better intrusion detection capabilities for data after training. The experimental results are applied to the well-known KDD CUP 99 data in the computer network field, and compared with the accuracy of the results obtained by parameter adjustment in the traditional way, it shows that the intrusion detection model under this method has better accuracy.
Highlights
A computer network occupies an important position in today’s social life
Haghnegandar and Wang proposed a power system intrusion detection model based on the Whale Optimization Algorithm (WOA) algorithm and an artificial neural network (ANN). e model uses the WOA algorithm to adjust the weight vector of a neural network to minimize the mean square error. e model has been tested by the Mississippi State University Electric Power Attack Database and compared with other commonly used classifiers to prove that the proposed model has good superiority [6]
In order to prove the effectiveness of the combination of the Principal Component Analysis (PCA) dimensionality reduction method proposed in this paper with the WOA-XGBoost model, the experimental environment is Anaconda 3, which runs on Intel(R) i71165G7 @2.8 GHz, 16 GB RAM, and 64-bit Windows operating system
Summary
A computer network occupies an important position in today’s social life. Life is full of various Internet-based services, such as online chat, online banking, and online games, which are functions we use every day. Haghnegandar and Wang proposed a power system intrusion detection model based on the WOA algorithm and an artificial neural network (ANN). The GA (Genetic Algorithm) method requires more initial parameters to be set, and the search speed is relatively slow, and it takes time to obtain an accurate solution It is relatively long, and PSO (Particle Swarm Optimization) is easy to fall into a local optimal solution due to the simple way of updating the position of particles. Erefore, this paper proposes an intrusion detection algorithm based on the WOA optimized XGBoost model, which uses the powerful optimization capabilities of the WOA algorithm to optimize key parameters for XGBoost, effectively improving the prediction accuracy of the XGBoost model, so as to more accurately detect intrusions or attacks in the network environment behavior Combining the XGBoost model with the metaheuristic algorithm, WOA can effectively overcome these limitations [9]. erefore, this paper proposes an intrusion detection algorithm based on the WOA optimized XGBoost model, which uses the powerful optimization capabilities of the WOA algorithm to optimize key parameters for XGBoost, effectively improving the prediction accuracy of the XGBoost model, so as to more accurately detect intrusions or attacks in the network environment behavior
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