Abstract

/" "p"Computer network traffic is analyzed via mutual information techniques, implemented using linear and nonlinear canonical correlation analyses, with the specific objective of detecting UDP flooding attacks. NS simulation of HTTP, FTP, and CBR traffic shows that flooding attacks are accompanied by a change of mutual information, either at the link being flooded or at another upstream or downstream link. This observation appears to be topology independent, as the technique is demonstrated on the so-called parking-lot topology, random 50-node topology, and 100-node transit-stub topology. This technique is also employed to detect UDP flooding with low false alarm rate on a backbone link. These results indicate that a change in mutual information provides a useful detection criterion when no other signature of the attack is available.

Abstract

Autonomous vehicle platoon is a promising paradigm towards traffic congestion problems in the intelligent transportation system. However, under certain circumstances, the advantage of the platoon cannot be fully developed. In this paper, we focus on the highway Electronic Toll Collection (ETC) charging problem. We try to let the opportunistic platoon pass the ETC as a whole. There are three main issues in this scenario. Firstly, the opportunistic platoon is temporarily composed; vehicles do not trust each other. Secondly, single vehicle may try to escape from the ETC charging by following the platoon. Finally, platoon members may collude with each other and try to underreport the number of vehicles in the platoon so as to evade payment. To solve these challenges, we propose a blockchain-based efficient highway toll paradigm for the opportunistic platoon. The driving history, credential information of every registered vehicle, is recorded and verified from the blockchain. A roadside unit (RSU) is adopted to distinguish the single vehicle from the platoon and in charge of lane allocation. Additionally, an aggregate signature is introduced to accelerate the authentication procedure in the RSU. We analyse the potential security threats in this scenario. The experimental result indicates that our scheme is efficient and practical. Document type: Article

Abstract

Various kinds of smart sensors are being developed and released with the growth of sensor technology and Internet of Things (IoT) technology. IoT smart service can provide convenience in our daily lives with these smart sensors. However, the increasing number of mobile sensors and amount of data may increase latency. It may cause network congestion on a particular network. Therefore, we propose a Smart Edge Broker (SEB) to intelligently transmit data traffic generated by a smart city in this paper. SEB can prevent the traffic congestion from being transmitted or bypassed to a location where traffic is not necessary. In addition, SEB can prevent overload in a specific area between services through a location-based transfer. Plus, SEB is suitable to operate it as a fog computing model by placing it at the edge of a smart city network. We conducted a latency measurement experiment and load measurement experiment to evaluate the effectiveness of the proposed Smart Edge Broker. As a result, we found that the latency was reduced by 72%, and the CPU usage was reduced by 63% compared to when the Smart Edge Broker was not used. Document type: Article

Abstract

In the fields of advanced driver assistance systems (ADAS) and Internet of Vehicles (IoV), predicting the vehicle state is essential, including the ego vehicle’s position, velocity, and acceleration. In ADAS, an early position prediction helps to avoid traffic accidents. In IoV, the vehicle state prediction is essential for the required calculation of the expected reliable communication time between two vehicles. Many approaches have emerged to perform this vehicle state prediction. However, such approaches consider limited information of the ego vehicle and its surroundings, and they may not be very effective in practice because the real situation is highly complex and complicated. Moreover, some of the approaches often lead to a delayed prediction time due to collecting and calculating the substantial history information. By assuming that the driver is a robot driver, which eliminates distinct driving behaviors of different persons when facing the same situation, this paper creates a decision tree as a new quick and reliable method adapted to all road segments, and it proposes a new method to perform the vehicle state prediction based on this decision tree. Document type: Article