Explosive Cyber Security Threats During COVID-19 Pandemic and a Novel Tree-Based Broad Learning System to Overcome

Abstract

The rapid spread of the COVID-19 has not only affected personal health and economy, but also revolutionized people's lifestyles. As more people turn to work and socialize online, the development of unmanned technologies based on the Internet of Vehicles (IoV), such as unmanned delivery, unmanned vehicles, unmanned transportation, etc., will become an inevitable trend. However, all kinds of intelligent terminals for unmanned equipment require a large amount of data interaction with devices such as cloud servers, mobile terminals, and roadside terminals, which poses cyber security risks. Furthermore, the outbreak of COVID-19 has prompted people to put forward higher demands for the security of network communications. Unfortunately, the current intrusion detection methods based on machine learning still have weaknesses such as low accuracy and low efficiency when faced with unbalanced data distribution. To solve the above problems, we propose a novel Tree-based BLS (TBLS) intrusion detection method according to the idea of ensemble learning and decision tree (CART and J48). The performance of TBLS was tested on the NSL-KDD dataset and the UNSW-NB15 dataset respectively, which contain a variety of malicious traffic types for attacks on the IoV. The results show that our proposed method can achieve higher accuracy rate and lower false alarm rate, compared with the existing 16 solutions.