Misbehavior Detection Method by Time Series Change of Vehicle Position in Vehicle-to-Everything Communication

In recent years, research has been conducted on connected vehicles (CVs) that are equipped with communication devices and can be connected to networks. CVs share their own position information and surrounding information with other vehicles using Vehicle-to-Everything (V2X) communication. CVs can recognize obstacles on non-line-of-sight (NLoS), which cannot be recognized by autonomous vehicles, and reduce travel time to a destination by cooperative driving. Therefore, CVs are expected to provide safe and efficient transportation. On the other hand, problems of security of V2X communication by CVs have been discussed. Safe and efficient transportation by CVs is on the basis of the assumption that correct vehicle information is shared. If fake vehicle information is shared, it will affect the driving of CVs. In particular, vehicle position faking has been shown that it can induce traffic congestion and accidents, which is a serious problem. In this study, we define position faking by CV as misbehavior and propose a method to detect misbehavior on the basis of changes in vehicle position time series data composed of vehicle position information. We evaluated the proposed method using four different misbehavior models. F-measure of misbehavior models that CV sends random position information detected by the proposed method is higher than one by a related method. Therefore, the proposed method is suitable for detecting misbehavior in which the position information changes over time.

Incentive-Driven Approach for Misbehavior Avoidance in Vehicular Networks

For efficient and robust information exchange in the vehicular adhoc network,a secure and trusted incentive reward is needed to avoid and reduce the intensity of misbehaving nodes and congestion especially in the case where the periodic beacons exploit the channel.In addition,we cannot be sure that all vehicular nodes eagerly share their communication assets to the system for message dissemination without any rewards.Unfortunately,there may be some misbehaving nodes and due to their selfish and greedy approach,these nodes may not help others on the network.To deal with this challenge,trust-based misbehavior avoidance schemes are generally reflected as the capable resolution.In this paper,we employed a fair incentive mechanism for cooperation aware vehicular communication systems.In order to deploy a comprehensive credit based rewarding scheme,the proposed rewardbased scheme fully depends on secure and reliable cryptographic procedures.In order to achieve the security goals,we used the cryptographic scheme to generate a certified public key for the authenticity of every message exchange over the network.We evaluated the friction of misbehaving vehicles and the effect of rewarding schemes in context with honest messages dissemination over the network.

Detection and Fortification Analysis of WiMAX Network: With Misbehavior Node Attack

Wireless networks are playing an imperative role in our daily existence;in current scenario, the users want wireless connectivity for all location with all types of service. One of the major challenges for wireless network is security issue. First and foremost task is to detect the security attacks in the network and the second task is to prevent from an authorized attacks. In our view, a lot of researches are going on and somehow we have succeeded in the first case but the second task is very tough due to wireless channel. Our research is based on how to avoid network attack i.e. misbehavior node attack in the WiMAX system. In this paper we have proposed an algorithm for WiMAX network and our algorithm are able to prevent fixed as well as mobile misbehavior node attacks. As we know, misbehavior node misbehaves in the sense that the node does not esteem its MAC protocols and avariciously sends its packets without any restriction (Flooding of packets) because it doesn't follow any protocol. Our proposed work based on the standard time required for communication for valid user with some threshold time for valid delay and some unwanted delay with network conditions. Our approach can control continuous flooding of packets and continuously transmits Constant Bit Rate (CBR) packets by misbehavior node, which introduces noise in the network and upset the performance of the network. In the mean while the valid user communicate in a trouble-free approach.

Performance Analysis under MAC Layer Misbehavior Attack in Mobile Ad-Hoc Networks

This work presents a multi-criteria analysis of the MAC （media access control） layer misbehavior of the IEEE （Institute of Electrical and Electronics Engineers） 802.11 standard, whose principle is to cheat at the protocol to increase the transmission rate by greedy nodes at the expense of the other honest nodes. In fact, IEEE 802.11 forces nodes for access to the channel to wait for a back off interval, randomly selected from a specified range, before initiating a transmission. Greedy nodes may wait for smaller back-off intervals than honest nodes, and then obtaining an unfair assignment. In the first of our works a state of art on the research on IEEE 802.11 MAC layer misbehavior are presented. Then the impact of this misbehavior at the reception is given, and we will generalize this impact on a large scale. An analysis of the correlation between the throughput and the inter-packets time is given. Afterwards, we will define a new metric for measuring the performance and capability of the network.

学校管理教育措施的法律属性与适用辨析

修订后的预防未成年人犯罪法第31条增设的学校对有不良行为的未成年学生的管理教育措施,完善了罪错未成年人分级干预机制。对于管理教育措施与教育惩戒的关系,立法机关在立法过程中并未予以考虑。管理教育措施具有“以教代罚”的保护处分属性,而教育惩戒则具有在教育权力与职责边界内体现“以罚为教”的教育属性。对于有不良行为的未成年人,预防未成年人犯罪法规定学校可以“选择适用”教育惩戒或者管理教育措施,这一规定应当优先于教育部关于“应当适用”教育惩戒的规定。如果学校选择适用管理教育措施,则不应因同一行为再适用教育惩戒。管理教育措施具有协商性模式而非强制性模式的特点,这是管理教育措施适用所应把握的要义。

学生不当行为对教师职业幸福感的影响机制研究

为探讨学生不当行为对教师职业幸福感的影响机制,采用便利抽样的方法选取广东省高校266名教师样本进行调查,研究发现:(1)学生不当行为分别与教师组织自尊、职业幸福感呈负相关,组织自尊与职业幸福感呈正相关;(2)组织自尊在学生不当行为与教师职业幸福感间起部分中介作用;(3)教师个性控制点负向调节学生不当行为与教师职业幸福感之间的关系,以及组织自尊与职业幸福感之间的关系;(4)教师个性控制点负向调节学生不当行为通过组织自尊影响教师职业幸福感的后半路径:内控倾向越强,学生不当行为通过组织自尊对教师职业幸福感的间接效应越弱。

Organizational Misbehavior and Management Control in China＇s Public Hospitals： Doctors＇ Red Packets

This paper investigates Chinese doctors＇ informal payment, known as red packets, with reference to the debate on organizational misbehavior, fiddles and control. It aims to examine the internal and external factors that have contributed to the emergence of red packets in health services and the strategies of hospital management in dealing with informal payment. Analysis on the data collected from two hospitals shows that doctors＇ misbehavior is influenced by health services＇ funding mechanism, payment systems and corruption. More importantly, the study demonstrates the rationality of employee fiddles and management responses. Findings indicate that doctors are mainly responsible for this fiddling, unethical and illegal activity because of the financial gains acquired from patient bribery. However, doctor misbehavior remains under management＇s latent control as long as hospital income generation and reputation are not severely threatened. The study contributes to the analysis of informal payment in the field of organizational studies and employment relations, with a fresh perspective offered to extend our understanding of red packets in the context of healthcare marketization reform.

基于联邦学习的车联网虚假位置攻击检测研究

车联网恶意行为检测是车联网安全需要的重要组成部分.在车联网中,恶意车辆可以通过伪造虚假的位置信息实现虚假位置攻击.当前,针对车联网恶意位置攻击的解决办法是通过机器学习或深度学习的方式实现车辆恶意行为的检测.这种方式需要将数据进行收集,从而引发隐私问题.为解决上述问题,提出了一种基于联邦学习的车联网恶意位置攻击的检测方案.该方案不需要将用户数据进行收集,检测模型利用本地数据和模拟数据进行局部训练,这样即确保了车辆用户的隐私,同时减少了数据传输,节约了带宽.基于联邦学习的恶意位置攻击检测模型使用公开的VeReMi数据集进行训练和测试,并比较了以数据为中心的虚假位置攻击检测方案的性能.通过比较,基于联邦学习的恶意位置攻击检测与传统的以数据为中心的恶意位置攻击检测方案性能相近,但基于联邦学习的恶意位置攻击检测方案在数据传输和隐私保护和检测时延方面却更优.

作者和编辑对学术出版中作者学术不端行为的认知差异及防治措施

为调查作者及编辑对学术出版中作者学术不端行为的认知差异以及造成学术不端行为的原因,基于文献调研结果,从学术不端行为的主要责任者和审查者两个角度设计针对作者和编辑的学术不端行为调查问卷,通过问卷星发放问卷并获得调查数据。结果显示:(1)作者和编辑对剽窃、伪造、篡改等严重学术不端行为有统一且正确的认知,对不当署名、一稿多投、重复发表等行为存在认知差异,且对其危害性认识不足。(2)学术不规范行为的具体表现中,部分是学术不端行为,因存在一定的隐蔽性而被误判,体现出对相关规范制度了解不深刻;部分是学术不当行为,需要在出版中就学术规范性加强审核与引导。(3)造成学术不端行为最主要的个人因素是迫于科研成果产出、职称晋升、获取学位等的压力,最主要的制度因素是对学术不端、学术不当行为的具体表现界定不清晰,最主要的环境因素是学术研究中深受不良环境影响,功利主义、浮躁、夸大等现象普遍。最后基于调查结果对学术不端行为的防治提出建议,以期为防范治理学术不端行为提供参考。

面向车联网环境的异常行为检测机制研究综述

以智能网联汽车为核心的下一代智能交通系统逐渐深入城市居民生活,但也暴露出如远程恶意控制车辆、泄露车主个人信息等安全威胁。相较于车端设备与车联网服务平台层面的安全问题,文章重点关注车联网通信层面面临的安全问题。基于此,文章对近年来车联网环境中异常行为检测机制的相关研究进行了梳理。首先,分析了异常行为定义,并总结了常见威胁模型;然后,从基于消息内容的异常检测、基于消息处理行为的异常检测和结合传感器的异常检测3个方面,讨论了异常行为检测机制的分类;最后,总结了当前车联网通信异常行为检测机制中尚未解决的技术问题和未来研究趋势。

Decentralized content sharing in mobile ad-hoc networks:A survey

The evolution of smart mobile devices has significantly impacted the way we generate and share contents and introduced a huge volume of Internet traffic.To address this issue and take advantage of the short-range communication capabilities of smart mobile devices,the decentralized content sharing approach has emerged as a suitable and promising alternative.Decentralized content sharing uses a peer-to-peer network among colocated smart mobile device users to fulfil content requests.Several articles have been published to date to address its different aspects including group management,interest extraction,message forwarding,participation incentive,and content replication.This survey paper summarizes and critically analyzes recent advancements in decentralized content sharing and highlights potential research issues that need further consideration.

Generalized Jaccard Similarity Based Recurrent DNN for Virtualizing Social Network Communities

In social data analytics,Virtual Community(VC)detection is a primary challenge in discovering user relationships and enhancing social recommenda-tions.VC formation is used for personal interaction between communities.But the usual methods didn’t find the Suspicious Behaviour(SB)needed to make a VC.The Generalized Jaccard Suspicious Behavior Similarity-based Recurrent Deep Neural Network Classification and Ranking(GJSBS-RDNNCR)Model addresses these issues.The GJSBS-RDNNCR model comprises four layers for VC formation in Social Networks(SN).In the GJSBS-RDNNCR model,the SN is given as an input at the input layer.After that,the User’s Behaviors(UB)are extracted in the first Hidden Layer(HL),and the Generalized Jaccard Similarity coefficient calculates the similarity value at the second HL based on the SB.In the third HL,the similarity values are examined,and SB tendency is classified using the Activation Function(AF)in the Output Layer(OL).Finally,the ranking process is performed with classified users in SN and their SB.Results analysis is performed with metrics such as Classification Accuracy(CA),Time Complexity(TC),and False Positive Rate(FPR).The experimental setup consid-ers 250 tweet users from the dataset to identify the SBs of users.

基于QCA的同属乘客干预意向研究

近年来由乘客不良行为所致的公共交通事故频发,而同属乘客的干预成为化解事故的重要因素。文章从事件属性、个体特征和外部环境三个方面提取出同属乘客干预意向产生的五个关键要素,在此基础上运用定性比较分析(QCA)对同属乘客干预意向产生的组态条件进行分析。结果显示,前因变量组成的5个条件组合构成了同属乘客干预意向产生的充分条件;个人规范、社会支持是同属乘客干预意向产生的必要条件。研究结果为发挥社会公众对乘客不良行为的干预作用、化解突发公共交通事件提供了有益指导。

建设统一大市场背景下的产品服务系统差异化与企业伦理决策——基于财产保险行业的实证研究

文章基于行业竞争场景,在企业层面检验了产品服务系统(PSS)差异化与企业违规行为之间的关系,样本取自中国财产保险行业2009—2019年间的面板数据,共涉及56家在中国内地提供财产保险服务的公司。研究发现,PSS差异化与企业违规行为之间呈现U型关系,竞争地位对以上主效应具有中介作用。从全国统一大市场视角看,市场分割作为中国本土管理情境中的制度变量,对PSS差异化和企业违规行为之间的关系存在正向调节作用,具体地说,在PSS差异化程度较低时,市场分割程度的增加会削弱PSS差异化对企业违规行为的负向影响;在PSS差异化程度较高时,市场分割程度的增加会增强PSS差异化对企业违规行为的正向影响。该研究聚焦于中国统一大市场建设进程中的突出现象,批判吸收了西方学术界现有的伦理决策模型,研究结论为企业违规行为的发生机理和边界条件提供了更加符合中国管理情境的解释,也为行业市场上企业违规行为的干预和治理策略提供了理论参考。

国外关于课堂纪律问题的研究述评

维持良好的课堂纪律,促进学生的自我管理已经成为学校德育工作的重要内容。目前该领域的研究主要集中在两个方面:一是对学生不良行为的描述和解释;二是对教师旨在维持纪律,改变学生不良行为所采取的各种预防性措施和反应性措施以及在此基础上形成的各种综合干预模式的有效性进行评估。

小学生课堂问题行为与心理素质的关系:一项观察研究

为科学、客观的了解小学生课堂问题行为的现状并探讨与心理素质的关系,采用Martin等人提出的TPOT观察范式对重庆市潼南区某小学4-6年级117名学生的课堂问题行为进行录像、编码,并施测小学生心理素质问卷简化版。结果表明:(1)T-POT观察范式在本研究中具有适用性;(2)任务偏移是小学生最常见的课堂问题行为;(3)小学生课堂问题行为的表现存在显著的性别、年级差异;(4)在控制了年级、性别、班级环境变量后,心理素质能够显著负向预测课堂问题行为。

其他顾客不当行为引发服务失败的补救效果研究

已有服务失败和补救的研究更多集中于服务系统和服务员工引发的失败与补救,较少关注由其他顾客不当行为引发的服务失败与补救。本文提出其他顾客补救的概念,并考察其他顾客不当行为引发服务失败情境下,其他顾客补救、服务商补救以及共同补救对顾客满意及行为意向的影响。基于餐饮业采用实验法进行数据收集,研究结果表明:与其他顾客不补救相比,其他顾客补救使顾客有更高的满意度和重购意愿以及更低的负面口碑传播意向。与其他顾客不补救相比,其他顾客补救情境下,员工道歉会更显著地提高顾客满意度和重购意愿。其他顾客和员工进行补救后,赔偿的效果不明显;其他顾客无补救情境下,赔偿可以显著提高顾客的满意度和重购意向。本研究为服务企业在其他顾客不当行为引发服务失败后实施补救策略提供了理论指导。

基于人工免疫的MANET不端行为检测模型

针对层次ad hoc网络的结构特点和安全需求,提出了一种基于人工免疫的不端行为检测系统模型。该模型将不端行为检测数据集分为簇内节点数据集和簇首节点数据集,对其分别进行二进制编码和数值编码。使用分层的抗原(不端行为)动态检测算法,通过簇内节点检测器行为匹配和簇首节点协作验证判断节点的不端行为。仿真实验表明,当网络中不端节点比率在0～40%范围内动态变化时,该模型检测率高于87.6%,误报率低于1.01%,说明模型的整体性能优于DSR-Probe算法。

服务破坏行为:概念、测量与相关变量

服务破坏行为是指服务行业组织成员在向顾客提供服务的过程中,故意做出的对服务造成负面影响的行为,也是以往研究中关于工作场所中的破坏行为在服务行业中的体现;服务破坏行为的表现主要有四种类型:习惯且私下的服务破坏,习惯且公开的服务破坏,偶发且私下的服务破坏,偶发且公开的服务破坏;服务破坏行为的测量方法主要有:访谈法,问卷调查法和情景投射法;从个人、群体、组织与环境四个水平分析服务破坏行为的前因变量,从对个体、服务绩效及组织绩效三个方面探讨服务破坏行为的结果变量;该领域未来的研究应着重于进一步改进测量的方法,分析相关变量之间的作用机制,寻求控制服务破坏行为的有效对策,拓展视野,进一步扩大研究中被试的类型及行业领域。

基于簇的分布式认知无线电网络安全体系结构

针对认知无线电网络中出现的模仿主用户攻击和自私行为攻击问题,提出一种基于簇的分布式认知无线电网络安全体系结构。这种安全体系结构通过采用数据加密和认证等安全技术解决无线网络中原有的安全问题,通过在主用户基站与认知用户间使用Hash匹配技术可解决模仿主用户攻击问题,通过簇头向目的节点发送转发节点的可用频谱信息可解决自私行为攻击问题。由分析可知该安全体系结构是安全、高效和可行的。