# Rogue Node Detection Based on a Fog Network Utilizing Parked Vehicles
## 2 Related Work  
The detection of rogue nodes can be roughly divided into two categories: the first category is the use of machine learning methods to identify rogue nodes, and the second one is the use of non-machine learning strategies for rogue node detection. [PERSON] [11] proposed an intrusion detection system based on a deep belief network, which used unsupervised pre-training to initialize parameters, thus improving the detection accuracy. [PERSON] [12] proposed an intrusion detection system based on VANETs, which used a self-encoder network and a recurrent neural network to achieve efficient intrusion detection. The methods proposed in [11; 12] have the problem that the generalization ability of the model is not strong due to the large difference between the training data set and the actual application. Literature [3] proposed a scheme based on SVM to defend against false information. The local trust module used the SVM-based classifier to detect false messages, and the vehicle trust module applied SVM to obtain the comprehensive trust value of the vehicle. Rogue nodes can be determined by combining the detection results of the two types of modules. However, this method has the problem of over-reliance on historical data, resulting in low recognition accuracy. Research [4] used an SVM-based module to analyze historical vehicle data to calculate the vehicle's trust value, which was then used to detect rogue nodes. However, this mechanism showed high network latency and storage overhead when the density of automobiles in the city was high. [PERSON] et al. [5] proposed an intrusion detection algorithm based on machine learning to monitor network traffic and detect abnormal network activities. However, due to the lack of samples, this algorithm had low universality for different network abnormal behavior detection. [PERSON] et al. [6] used KNN and RF to establish a generalized detection mechanism. Although these proposed mechanisms can detect rogue nodes in different scenarios, the detection speed is slow. Further, no response mechanism is established to deal with the detected anomalies. Nowadays, various non-machine learning-based detection strategies have been proposed. For instance, [PERSON], [PERSON] [13] proposed a secure intelligent traffic light control scheme based on fog computing. The system used roadside traffic lights as fog equipment, and its security was based on the hardness of the computational Diffie-Hellman puzzle. [PERSON] [14] proposed an Intrusion Detection System using statistical techniques to detect anomalies and identify rogue nodes. [PERSON] [15] proposed a fog computing-based Sybil attack detection for VANETs, which utilized onboard units of all the vehicles in the region to create a dynamic fog for rogue nodes detection. [PERSON] [16] proposed a VANETs architecture for secure communication, which included two parts: the software defined network and the fog computing. In the interim, fog computing was used to provide secure communication of information. The methods proposed in [13; 14; 15; 16] have the problem that their performance will generally decline as the number of rogue nodes increases. [PERSON] [7] presented a trust-based approach to classify moving vehicles. The authors used RSUs to calculate the trust score for each vehicle at first. Then, vehicles were classified according to their trust values. To resolve both the information security and the data privacy issues in fog computing, [PERSON] et al. [8] proposed a rogue node detection scheme based on symmetric keys. RSUs first encrypted the rogue node calculation results, and then shared the detection results with other RSUs in the network. The authors in [9] showed a method that can discover bad nodes based on a group of user equipment. Simulation results presented that the proposed method achieves high performance with minimum implementation overhead. In literature [10], the authors described the concept of guard nodes for judging illegal nodes. Moving vehicles on the road are employed to form a fog network.