An Effective Method to Safeguard Cyber Security by Preventing Malicious Data

• Main Authors: Guohua Wang, Shangda Xie, Xun Zhang, Jinggeng Gao, Feng Wei, Bo Zhao, Chunying Wang, Shichao Lv
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: D-S evidence theory; algebra statistics; cross-site scripting attacks; geometric trend matching; cyber attacks
• Online Access: https://ieeexplore.ieee.org/document/8890693/

In order to ensure the information security of a network, network security devices have been widely deployed in the information and communication network. Based on the monitoring data collected by network security equipment, network security administrators analyze whether there is an attacking incident or abnormal situation in the current network environment. However, if the original monitoring data collected by network security equipment is tampered with by the attacker, then network security administrators will make a wrong judgment and delay the best time to take protective actions. Therefore, the reliability and authenticity of data generated by network security devices are of the utmost importance. In this paper, we first define a revised Euclidean distance formula for the D-S evidence theory and propose a dual test method for the authenticity of data based on algebra statistics and geometric trends, detecting any malicious tampering on these monitoring data. Utilizing a web application firewall (WAF) device, we carry out a series of experiments to verify the proposed method.