Proceedings Article | 14 December 2021
KEYWORDS: Machine learning, Data modeling, Floods, Tumor growth modeling, Statistical modeling, Binary data, Performance modeling, Internet, Visualization, Neural networks
Over the past few decades, ’as all kinds of network attacks have bad effects on people's daily life, cybersecurity has become an issue that is commonly concerned by society. In traditional ways, it belongs to human’s responsibility that estimates whether the traffic requests received by the network are attacks. This method is not only inefficient but also has bad timeliness. Later, with the popularization of machine learning, people attempt to train some models by constructing features to detect network attacks. However, the labor cost of feature construction is very high. In recent years, with the development of deep learning, this method has become a more popular choice because of its relatively low cost and outstanding effect on prediction. According to the above analysis, this paper firstly uses machine learning-based methods including Random Forest, Gaussian NB, XGBOOST, Decision-Tree, Logistic Regression, KNN to train the effective model. Besides, we also trained a Multi-Layer Perceptron (MLP) model to compare the differences between machine learning and deep learning in predicting network traffic attacks on the CIC-ID2017 dataset. The results show that the XGBOOST, Random Forests, KNN, Decision Tree, Logistic Regression, and Gaussian Naive Bayesian respectively achieve accuracy with 0.999, 0.998, 0.991, 0.979, 0.568, and 0.509, while MLP obtains accuracy 0.643 with loss NaN. This fact suggests that the MLP method is unsuitable for the web attack detection task compared with the machine learning based approaches.
