The Global Navigation Satellite System (GNSS) is capable of accurate positioning because it can provide high-precision data. These data are transmitted to the receiver in the form of navigation messages, called civil navigation messages (CNAV). As it is transmitted in an open, transparent environment without data integrity protection mechanisms and secure data transmission measures, the CNAV is suspected to spoofing attacks. In 2023, the OPSGROUP has received approximately 50 reports of GPS spoofing activity. A spoofed plane's navigation system will show it as being in a different place - a security risk if a jet is guided to fly into a hostile country's airspace. To prevent the forging of GNSS positioning data by spoofing attacks targeting CNAV, we propose a certificateless aggregation authentication for CNAV by using the elliptic curve discrete logarithm problem and the combination of the GNAV structural characteristics, called TrustCNAV. Security proof and performance analysis indicate that this authentication scheme can resist spoofing attacks and ensure data security of CNAV, also it avoids pairing operations with high computational complexity, thus meeting security requirements without causing too much time and communication consumption.

Abstract Aiming at the low rate and strong concealment of low‐rate Denial of Service (LDoS) attacks, the calculation of traffic Hurst index is combined with traffic classification, and a machine learning LDoS attack detection method based on search sorting is proposed. The method first calculates the segmentation Hurst exponent of each flow, and constructs a traffic similarity matrix as a statistical feature. Then, using the improved model XGBoost of the Gradient Boosting Decision Tree (GBDT), the traffic is classified and predicted. The network angle distinguishes between normal traffic and abnormal Origin‐Destination (OD) flows containing LDoS attacks, thereby achieving the purpose of detecting LDoS attacks. The method in this study was validated using the US public network dataset Abilene. The experimental results show that the global LDoS attack traffic detection method based on the Hurst index and GBDT algorithm achieves better detection results under different attack rates.