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 kinematic study of the integral shaped filament:what roles do filaments play in forming young stellar clusters?

Young protoclusters （embedded stellar clusters） are responsible for the vast majority of star formation currently occurring in the Galaxy. Recent observations suggest a scenario in which filamen-tary structures in the interstellar medium represent the first step towards precluster clumps and even- tually star formation. Whether filaments continuously fuel the star formation process when the cluster accretes material is still an open question. In this paper, we present a case study of the famous ＇integral shaped filament＇ （ISF） in the Orion A molecular cloud and we seek to study the kinematics which is truly originated from the ISF. We firstly define the central ridge of the ISF with NHa, ^12CO, ^13CO and N2H^＋. Undulations are present in all the ridges. Moreover, a large scale offset is apparent in the ridges as derived by different tracers, which may be explained by the slingshot mechanism proposed by Stutz ＆ Gould. We fit the velocity field of the ISF and find the derived velocity gradient is about 0.7 km s^- 1 pc^- 1 which may come from an overall contraction. We propose a method to check the accretion flow along the ISF by using the velocity deviations of different molecular tracers, which is better than the common method of using the velocity distribution of one tracer alone. Using the velocity deviations, we also find that OMC-1 to 5 are located close to the local extrema of the fluctuations, which may demonstrate that gas flows toward each clump along the ISE