Deep Transfer Learning Techniques in Intrusion Detection System-Internet of Vehicles: A State-of-the-Art Review

The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles(IoV)technology.The functional advantages of IoV include online communication services,accident prevention,cost reduction,and enhanced traffic regularity.Despite these benefits,IoV technology is susceptible to cyber-attacks,which can exploit vulnerabilities in the vehicle network,leading to perturbations,disturbances,non-recognition of traffic signs,accidents,and vehicle immobilization.This paper reviews the state-of-the-art achievements and developments in applying Deep Transfer Learning(DTL)models for Intrusion Detection Systems in the Internet of Vehicles(IDS-IoV)based on anomaly detection.IDS-IoV leverages anomaly detection through machine learning and DTL techniques to mitigate the risks posed by cyber-attacks.These systems can autonomously create specific models based on network data to differentiate between regular traffic and cyber-attacks.Among these techniques,transfer learning models are particularly promising due to their efficacy with tagged data,reduced training time,lower memory usage,and decreased computational complexity.We evaluate DTL models against criteria including the ability to transfer knowledge,detection rate,accurate analysis of complex data,and stability.This review highlights the significant progress made in the field,showcasing how DTL models enhance the performance and reliability of IDS-IoV systems.By examining recent advancements,we provide insights into how DTL can effectively address cyber-attack challenges in IoV environments,ensuring safer and more efficient transportation networks.

A Framework for ranking ubiquitous computing services by AHP analysis

With the increasing advances in technology,ubiquitous computing services have been able to satisfy users by providing high quality services.Such services can be found in different areas such as healthcare,social networks,urban transportation,and multimedia.Nowadays,there are a wide variety of services with similar functionalities in each of the abovementioned areas.Ranking these services based on quality of service(QoS)criteria can help the users to choose the most appropriate services that meet their preferences.The aim of this research is to create a comprehensive framework based on QoS criteria in ubiquitous environment to rank services.At first by extending the previous research,this paper gathers and classifies the QoS criteria into four classes of architecture,usability,ubiquity,and security.This classification organizes QoS criteria in a hierarchical structure.Afterward,Analytic Hierarchy Process(AHP)is used to propose a customized service ranking framework according to the values of each criterion.To utilize the opinions of experts of the subject,a questionnaire is designed to evaluate the proposed structure of QoS criteria and to determine the weight of each criterion.Finally,comparing the proposed framework with previous approaches indicates that this framework includes a complete set of criteria,which leads to a deep perception of QoS in a ubiquitous computing environment.Moreover,the computed inconsistency ratio indicates that the performed decision-making process has a reasonable consistency.

Trasfugen:Traffic assignment of urban network by an approximation fuzzy genetic algorithm

This paper proposes the Trasfugen method for traffic assignment aimed at solving the user equilibrium problem.To this end,the method makes use of a genetic algorithm.A fuzzy system is proposed for controlling the mutation and crossover rates of the genetic algorithm,and the corrective strategy is exploited for handling the equilibrium problem constraints.In the model,an approximation algorithm is proposed for obtaining the paths between the origin–destination pairs in the demand matrix.Unlike the traditional deterministic algorithm that has exponential time complexity,this approximation algorithm has polynomial time complexity and is executed much faster.Afterward,the Trasfugen method is applied to the urban network of Tehran metropolitan and the efficiency is investigated.Upon comparing the results obtained from the proposed model with those obtained from the conventional traffic assignment method,namely,the Frank–Wolfe method;it is shown that the proposed algorithm,while acting worse during the initial iterations,achieves better results in the subsequent iterations.Moreover,it prevents the occurrence of local optimal points as well as early/premature convergence,thus producing better results than the Frank–Wolfe algorithm.