Authentication of Vehicles and Road Side Units in Intelligent Transportation System

Security threats to smart and autonomous vehicles cause potential consequences such as traffic accidents,economically damaging traffic jams,hijacking,motivating to wrong routes,and financial losses for businesses and governments.Smart and autonomous vehicles are connected wirelessly,which are more attracted for attackers due to the open nature of wireless communication.One of the problems is the rogue attack,in which the attacker pretends to be a legitimate user or access point by utilizing fake identity.To figure out the problem of a rogue attack,we propose a reinforcement learning algorithm to identify rogue nodes by exploiting the channel state information of the communication link.We consider the communication link between vehicle-to-vehicle,and vehicle-to-infrastructure.We evaluate the performance of our proposed technique by measuring the rogue attack probability,false alarm rate(FAR),mis-detection rate(MDR),and utility function of a receiver based on the test threshold values of reinforcement learning algorithm.The results show that the FAR and MDR are decreased significantly by selecting an appropriate threshold value in order to improve the receiver’s utility.

Modified aquila optimizer for forecasting oil production

Oil production estimation plays a critical role in economic plans for local governments and organizations.Therefore,many studies applied different Artificial Intelligence(AI)based meth-ods to estimate oil production in different countries.The Adaptive Neuro-Fuzzy Inference System(ANFIS)is a well-known model that has been successfully employed in various applica-tions,including time-series forecasting.However,the ANFIS model faces critical shortcomings in its parameters during the configuration process.From this point,this paper works to solve the drawbacks of the ANFIS by optimizing ANFIS parameters using a modified Aquila Optimizer(AO)with the Opposition-Based Learning(OBL)technique.The main idea of the developed model,AOOBL-ANFIS,is to enhance the search process of the AO and use the AOOBL to boost the performance of the ANFIS.The proposed model is evaluated using real-world oil produc-tion datasets collected from different oilfields using several performance metrics,including Root Mean Square Error(RMSE),Mean Absolute Error(MAE),coefficient of determination(R2),Standard Deviation(Std),and computational time.Moreover,the AOOBL-ANFIS model is compared to several modified ANFIS models include Particle Swarm Optimization(PSO)-ANFIS,Grey Wolf Optimizer(GWO)-ANFIS,Sine Cosine Algorithm(SCA)-ANFIS,Slime Mold Algorithm(SMA)-ANFIS,and Genetic Algorithm(GA)-ANFIS,respectively.Additionally,it is compared to well-known time series forecasting methods,namely,Autoregressive Integrated Moving Average(ARIMA),Long Short-Term Memory(LSTM),Seasonal Autoregressive Integrated Moving Average(SARIMA),and Neural Network(NN).The outcomes verified the high performance of the AOOBL-ANFIS,which outperformed the classic ANFIS model and the compared models.