A data-driven method for network anomaly attack detection in public transport system

With the popularity of the Internet, more and more businesses and transactions rely on the network to complete, and at the same time, anomaly attacks against various network applications are becoming more and more frequent. How to detect and identify various network anomalies has become an unavoidable technical issue. The network attack methods emerge in an endless stream, and the attack methods are constantly updated, making the traditional security mechanisms such as firewalls difficult to detect for many attacks. As an effective defense technology, intrusion detection technology makes up for the shortcomings of traditional security technology and has been concerned by researchers at home and abroad. With the continuous expansion of network scale, the continuous growth of network traffic and the continuous development of hacker technologies, higher requirements are placed on the performance of network anomaly detection. This thesis designs an anomaly attack detection based on self-organizing mapping algorithm to improve the accuracy of anomaly detection technology. Through the unsupervised machine learning method, the daily data of the transportation system is clustered and a training model is established. Finally, abnormal attack detection is performed according to the clustering model. Master of Science (Electronics)