Assessment of Network Security Alerts Based on Expert Experience

Abstract

The network architecture of the public security video private network is relatively complex, and the types of devices it carries are different from those of general information networks. It mainly consists of video surveillance IoT devices, as well as numerous streaming media servers, video scheduling servers, structured computing power, big data computing power, storage devices, and network devices. These devices have strong regional dispersion. In such a network environment, when conducting alarm analysis and assessment for network intrusions, malicious code, and abnormal logs, the collected security logs have strong characteristics of multi-source and heterogeneity. Traditional log processing methods are prone to alarm fatigue, high false positive rates, and poor adaptability to complex traffic. To improve the accuracy and efficiency of alarm analysis and assessment, this paper proposes a model that integrates expert experience and supervised learning, called FEESLM, aiming to enhance the ability to judge network security alarms. The FEESLM model consists of three major components: the expert experience modeling component, the supervised learning modeling component, and the assessment result decision component. The expert experience modeling component summarizes the expert’s assessment experience to form rules, statistics, and association models. The supervised learning modeling component uses the XGBoost algorithm to learn from the data processed by expert experience, constructing a assessment model. The assessment result decision component uses the maximum entropy model to calculate the most credible results from the outputs of expert and supervised learning modeling as the final assessment result. Experimental results show that the FEESLM model outperforms single expert experience modeling and supervised learning modeling in terms of accuracy, recall rate, and F1-score, effectively improving the accuracy of network security alarm assessment.

Keywords

• Network security
• alarm assessment
• expert experience
• supervised learning
• FEESLM model