چكيده به لاتين
Accurate timing is one of the key features of the Global Positioning System (GPS), which is employed in many critical infrastructures. Any imprecise time measurement in GPS-based structures, such as smart power grids, and Phasor Mmeasurement Units (PMUs), can lead to disastrous results. The vulnerability of the stationary GPS receivers to the Time Synchronization Attacks (TSAs) jeopardizes the GPS timing precision and trust level. The receiver's position remains constant during the attack; hence, attack detection and mitigation are challenging.
Data and signal authentication can be used as an effective approach to mitigate such security threats. This research proposes a Navigation Message Authentication (NMA) method based on Timed Efficient Stream Loss-tolerant Authentication (TESLA), which exploits one-way encryption chains. The contributions of this research are two techniques for implementing TESLA using reserved bits for GPS L1 data. The TESLA algorithm is applied to the extracted navigation message of the GPS L1C/A. Due to the flexibility of the GPS L1C message structure, the protocol is also implemented on Civil Navigation signals (CNAV-2) of GPS L1C. In comparison with Elliptic Curve Digital Signature Algorithm (ECDSA), the Authentication Rate (AR) increases to two minutes for each authentication, and performance in terms of the Authentication Error Rate (AER) is optimized by employing the TESLA method. TESLA can defend against intermediate and complex spoofing attacks; however, a simple replay attack can easily spoof this method. Therefore, the concurrent exploitation of TESLA and other countermeasures are suggested in this research field.
In the past few years, studies suggested the adoption of estimators to follow the authentic trend of the clock offset information under attack conditions. However, the estimators would lose track of the authentic signal without proper knowledge of the signal characteristics. Therefore, a Multi-Layer Perceptron Neural Network (MLP NN) is proposed to follow the trend of the data. The main difference between the proposed method and typical estimators is the reliance of the network on the training information consisting of signal features. The proposed MLP NN performance has been evaluated through two real-world datasets and two well-known types of TSA. The root mean square error results exhibit an improvement of at least six times compared to other conventional and state-of-art methods.
Various countermeasures have been suggested to mitigate TSA effects. However, they are mainly software-based and are exploited to protect software implemented software-defined radios (SDRs). In this research, two hardware protection approaches are contributed for hardware-based SDRs based on MLP NN with sigmoid activation function. The most challenging part of MPL NN implementation is the activation function approximation. Therefore, two lightweight architectures are proposed for sigmoid function implementation. Linear approximation and look-up table (LA-LUT) and Piece-wise Linear Approximation (PLA) are exploited for this task. The synthesis results demonstrate that the PLA approach has a slightly higher resource utilization in comparison to LA-LUT, while this method is more accurate. The Mean Squared Error (MSE) of the PLA approach is equal to 0.019, which is 57% better than the LA-LUT approach with an MSE of 0.033. Furthermore, the designs are evaluated by two conventional types of TSA. According to the results, both methods are lightweight, and they only consume less than 0.3% of slice registers, 5% of slice LUTs, and 8% of DSP48E1Ss. Furthermore, they are real-time, and can mitigate the attack consequences; however, the PLA architecture has a better performance compared to LA-LUT.
