Classification Method of Encrypted Traffic Based on Deep Neural Network

With the widespread use of network traffic encryption technology, the traditional traffic classification method has gradually become invalid, which increases the difficulty of network management and poses a serious threat to network security. This paper analyzes the traffic encrypted and transmitted by VPN and explores its classification method. By extracting the timing characteristics of the encrypted traffic, the classification model of the deep neural network was used to classify the traffic of seven different categories in the encrypted traffic, and compared with the commonly used naive Bayesian classification algorithm. At the same time, the batch size that affects the training of deep neural network models was studied. Experiments show that the classification ability of encrypted traffic classification model based on deep neural network is much better than the naive Bayesian method. During training, the batch size has different effects on the deep neural network model. When the batch size is 40, the deep neural network model has the best classification ability.

A certificateless Multi-receiver Encryption scheme based on SM2 signature algorithm

The Multi-receiver Encryption(MRE)scheme can meet the secure data transmission requirements in multicast and broadcast scenarios.To meet compliance,critical information infrastructure in China should be protected with Chinese national commercial cryptographic algorithms.Designing an MRE scheme based on Elliptic Curve Cryptography(ECC)is one of the current design methods with better flexibility and performance.However,the research on MRE schemes based on SM2 elliptic curve public-key cryptography is still in a blank state.This paper proposes a Certificateless SM2-based Multireceiver Encryption(CL-SM2-MRE)scheme.We prove the security of the CL-SM2-MRE scheme under the Random Oracle Model(ROM)and analyze the performance.

A V2I communication-based pipeline model for adaptive urban traffic light scheduling

Adaptive traffic light scheduling based on realtime traffic information processing has proven effective for urban traffic congestion management. However, fine-grained information regarding individual vehicles is difficult to acquire through traditional data collection techniques and its accuracy cannot be guaranteed because of congestion and harsh environments. In this study, we first build a pipeline model based on vehicle-to-infrastructure communication, which is a salient technique in vehicular adhoc networks. This model enables the acquisition of fine-grained and accurate traffic information in real time via message exchange between vehicles and roadside units. We then propose an intelligent traffic light scheduling method (ITLM) based on a “demand assignment” principle by considering the types and turning intentions of vehicles. In the context of this principle, a signal phase with more vehicles will be assigned a longer green time. Furthermore, a green-way traffic light scheduling method (GTLM) is investigated for special vehicles (e.g., ambulances and fire engines) in emergency scenarios. Signal states will be adjusted or maintained by the traffic light control system to keep special vehicles moving along smoothly. Comparative experiments demonstrate that the ITLM reduces average wait time by 34%-78% and average stop frequency by 12%-34% in the context of traffic management. The GTLM reduces travel time by 22%^44% and 30%-55% under two types of traffic conditions and achieves optimal performance in congested scenarios.

An improved multi-attribute decision-making based network selection algorithm for heterogeneous vehicular network

1 Introduction With sharply rising quantity of urban vehicles over the past few decades,traffic jams and safety have gradually become outstanding problems.In recent years,Vehicular Ad hoc Network(VANET)is appeared and utilized to solve traffic related issues.It is a type of self-organized and open-structured network,and provides Vehicle-to-Everything(V2X)communications.Almost all kinds of applications in VANET rely on efficient data transmission and interaction[1-3].

A mobile edge computing-based applications execution framework for Internet of Vehicles

Mobile edge computing(MEC)is a promising technology for the Internet of Vehicles,especially in terms of application offloading and resource allocation.Most existing offloading schemes are sub-optimal,since these offloading strategies consider an application as a whole.In comparison,in this paper we propose an application-centric framework and build a finer-grained offloading scheme based on application partitioning.In our framework,each application is modelled as a directed acyclic graph,where each node represents a subtask and each edge represents the data flow dependency between a pair of subtasks.Both vehicles and MEC server within the communication range can be used as candidate offloading nodes.Then,the offloading involves assigning these computing nodes to subtasks.In addition,the proposed offloading scheme deal with the delay constraint of each subtask.The experimental evaluation show that,compared to existing non-partitioning offloading schemes,this proposed one effectively improves the performance of the application in terms of execution time and throughput.