Vehicular Ad-hoc Networks (VANETs) technology has recently emerged, and gaining significant attention from the research because it is promising technologies related to Intelligent Transportation System (ITSs) and smar...Vehicular Ad-hoc Networks (VANETs) technology has recently emerged, and gaining significant attention from the research because it is promising technologies related to Intelligent Transportation System (ITSs) and smart cities. Wireless vehicular communication is employed to improve traffic safety and to reduce traffic congestion. Each vehicle in the ad-hoc network achieves as a smart mobile node categorized by high mobility and forming of dynamic networks. As a result of the movement of vehicles in a continuous way, VANETs are vulnerable to many security threats so it requisites capable and secure communication. Unfortunately, Ad hoc networks are liable to varied attacks like Block Hole attacks and Grey Hole attacks, Denial of service attacks, etc. Among the most known attacks are the Black Hole attacks while the malicious vehicle is able to intercept the data and drops it without forwarding it to the cars. The main goal of our simulation is to analyze the performance impact of black hole attack in real time vehicular traffic in the Greater Detroit Area using NS-2 and SUMO (Simulation of Urban). The simulation will be with AODV protocol.展开更多
In Wireless Body Area Networks(WBANs)with respect to health care,sensors are positioned inside the body of an individual to transfer sensed data to a central station periodically.The great challenges posed to healthca...In Wireless Body Area Networks(WBANs)with respect to health care,sensors are positioned inside the body of an individual to transfer sensed data to a central station periodically.The great challenges posed to healthcare WBANs are the black hole and sink hole attacks.Data from deployed sensor nodes are attracted by sink hole or black hole nodes while grabbing the shortest path.Identifying this issue is quite a challenging task as a small variation in medicine intake may result in a severe illness.This work proposes a hybrid detection framework for attacks by applying a Proportional Coinciding Score(PCS)and an MK-Means algorithm,which is a well-known machine learning technique used to raise attack detection accuracy and decrease computational difficulties while giving treatments for heartache and respiratory issues.First,the gathered training data feature count is reduced through data pre-processing in the PCS.Second,the pre-processed features are sent to the MK-Means algorithm for training the data and promoting classification.Third,certain attack detection measures given by the intrusion detection system,such as the number of data packages trans-received,are identified by the MK-Means algorithm.This study demonstrates that the MK-Means framework yields a high detection accuracy with a low packet loss rate,low communication overhead,and reduced end-to-end delay in the network and improves the accuracy of biomedical data.展开更多
Wireless technology is transforming the future of transportation through the development of the Internet of Vehicles(IoV).However,intricate security challenges are intertwinedwith technological progress:Vehicular ad h...Wireless technology is transforming the future of transportation through the development of the Internet of Vehicles(IoV).However,intricate security challenges are intertwinedwith technological progress:Vehicular ad hoc Networks(VANETs),a core component of IoV,face security issues,particularly the Black Hole Attack(BHA).This malicious attack disrupts the seamless flow of data and threatens the network’s overall reliability;also,BHA strategically disrupts communication pathways by dropping data packets from legitimate nodes altogether.Recognizing the importance of this challenge,we have introduced a new solution called ad hoc On-Demand Distance Vector-Reputation-based mechanism Local Outlier Factor(AODV-RL).The significance of AODVRL lies in its unique approach:it verifies and confirms the trustworthiness of network components,providing robust protection against BHA.An additional safety layer is established by implementing the Local Outlier Factor(LOF),which detects and addresses abnormal network behaviors.Rigorous testing of our solution has revealed its remarkable ability to enhance communication in VANETs.Specifically,Our experimental results achieve message delivery ratios of up to 94.25%andminimal packet loss ratios of just 0.297%.Based on our experimental results,the proposedmechanismsignificantly improves VANET communication reliability and security.These results promise a more secure and dependable future for IoV,capable of transforming transportation safety and efficiency.展开更多
传统移动Ad Hoc网络(mobile Ad Hoc network,MANETs)黑洞攻击解析模型存在网络拓扑结构固定、网络传输性能预测精确度低的问题。针对使用按需路由协议的MANETs网络,提出一种基于随机拓扑近似技术的黑洞攻击解析改进模型(improved black ...传统移动Ad Hoc网络(mobile Ad Hoc network,MANETs)黑洞攻击解析模型存在网络拓扑结构固定、网络传输性能预测精确度低的问题。针对使用按需路由协议的MANETs网络,提出一种基于随机拓扑近似技术的黑洞攻击解析改进模型(improved black hole attack analytical model,IBAAM)。IBAAM协议使用随机模型代替传统解析模型使用的n元2立方体模型,并将网络结构扩展至随机拓扑结构,使用最短跳距离概率描述表示网络拓扑结构的随机拓扑信息,再使用K均值聚类法实现跳距离文件配置以求解任意拓扑结构下的攻击概率问题,从而在不利用任何实际拓扑先验信息条件下有效预测MANETs网络平均丢包数目。IBAAM实验结果表明,在多种不同固定Ad Hoc拓扑结构下,IBAAM的网络丢包预测精确度在仿真结果的95%置信区间内,能够有效预测网络传输性能。展开更多
按需距离矢量路由(Ad Hoc on-demand distance vector,AODV)广泛应用于智能电网;然而传统的AODV容易遭受黑洞攻击。为了检测和避免黑洞攻击,提出基于黑洞攻击检测的AODV路由,记为E-AODV。与传统的AODV路由不同,EAODV路由修改了路由回复(...按需距离矢量路由(Ad Hoc on-demand distance vector,AODV)广泛应用于智能电网;然而传统的AODV容易遭受黑洞攻击。为了检测和避免黑洞攻击,提出基于黑洞攻击检测的AODV路由,记为E-AODV。与传统的AODV路由不同,EAODV路由修改了路由回复(route reply,RREP)系统。E-AODV路由通过比较多个回复的序列号,检测是否存在黑洞攻击。一旦发现黑洞攻击,电表就向目的节点回复一条否定回复消息,进而防止恶意电表不良行为的蔓延。实验数据表明,相比于AODV,E-AODV在数据包传递率、吞吐量方面具有较好的性能。展开更多
在移动Ad Hoc网络(Mobile Ad Hoc Networks,MANETs)中,节点之间通过协作交流实现信息传输.为阻止MANETs网络中恶意节点使用黑洞/灰洞协作攻击网络,基于动态源路由的路由机制,提出一种协同诱饵检测方法(Cooperative bait detection appro...在移动Ad Hoc网络(Mobile Ad Hoc Networks,MANETs)中,节点之间通过协作交流实现信息传输.为阻止MANETs网络中恶意节点使用黑洞/灰洞协作攻击网络,基于动态源路由的路由机制,提出一种协同诱饵检测方法(Cooperative bait detection approach,CBDA).CBDA算法中,源节点随机从邻居节点中选择协同节点,发送虚假RREQ分组诱骗恶意节点,使用反向追踪技术实现目标跟踪并检测出恶意节点,方法同时具有表驱和按需两种防御策略的优点.仿真结果证明,在恶意节点存在的MANETs网络环境下,使用CBDA的网络分组传递率和路由开销优于按需路由协议.展开更多
This paper presents a reliable and secure supervisory control and data acquisition (SCADA) system equipped with advanced communication technologies (ACT) to enhance the operation and cyber security of the communicatio...This paper presents a reliable and secure supervisory control and data acquisition (SCADA) system equipped with advanced communication technologies (ACT) to enhance the operation and cyber security of the communication network in residential microgrid. The proposed approach uses the mobile ad hoc networks (MANET) for collecting data of power consumption from smart meters of residential areas and electric vehicles (EVs), and also for connecting mobile system operators to the network. Moreover, by understanding the dynamic nature of MANET and their exposure to cyber-attacks, we propose an intrusion detection and prevention (IDP) technology with secure knowledge algorithm and anomaly detection for preventing the black hole attacks, and other unknown attacks that result into packet dropping. Test results obtained by using Network Simulator (NS-2) demonstrate the effectiveness of the proposed IDP technology in preventing the cyber-attacks in the proposed residential microgrid communication network.展开更多
文摘Vehicular Ad-hoc Networks (VANETs) technology has recently emerged, and gaining significant attention from the research because it is promising technologies related to Intelligent Transportation System (ITSs) and smart cities. Wireless vehicular communication is employed to improve traffic safety and to reduce traffic congestion. Each vehicle in the ad-hoc network achieves as a smart mobile node categorized by high mobility and forming of dynamic networks. As a result of the movement of vehicles in a continuous way, VANETs are vulnerable to many security threats so it requisites capable and secure communication. Unfortunately, Ad hoc networks are liable to varied attacks like Block Hole attacks and Grey Hole attacks, Denial of service attacks, etc. Among the most known attacks are the Black Hole attacks while the malicious vehicle is able to intercept the data and drops it without forwarding it to the cars. The main goal of our simulation is to analyze the performance impact of black hole attack in real time vehicular traffic in the Greater Detroit Area using NS-2 and SUMO (Simulation of Urban). The simulation will be with AODV protocol.
基金funded by Stefan cel Mare University of Suceava,Romania.
文摘In Wireless Body Area Networks(WBANs)with respect to health care,sensors are positioned inside the body of an individual to transfer sensed data to a central station periodically.The great challenges posed to healthcare WBANs are the black hole and sink hole attacks.Data from deployed sensor nodes are attracted by sink hole or black hole nodes while grabbing the shortest path.Identifying this issue is quite a challenging task as a small variation in medicine intake may result in a severe illness.This work proposes a hybrid detection framework for attacks by applying a Proportional Coinciding Score(PCS)and an MK-Means algorithm,which is a well-known machine learning technique used to raise attack detection accuracy and decrease computational difficulties while giving treatments for heartache and respiratory issues.First,the gathered training data feature count is reduced through data pre-processing in the PCS.Second,the pre-processed features are sent to the MK-Means algorithm for training the data and promoting classification.Third,certain attack detection measures given by the intrusion detection system,such as the number of data packages trans-received,are identified by the MK-Means algorithm.This study demonstrates that the MK-Means framework yields a high detection accuracy with a low packet loss rate,low communication overhead,and reduced end-to-end delay in the network and improves the accuracy of biomedical data.
文摘Wireless technology is transforming the future of transportation through the development of the Internet of Vehicles(IoV).However,intricate security challenges are intertwinedwith technological progress:Vehicular ad hoc Networks(VANETs),a core component of IoV,face security issues,particularly the Black Hole Attack(BHA).This malicious attack disrupts the seamless flow of data and threatens the network’s overall reliability;also,BHA strategically disrupts communication pathways by dropping data packets from legitimate nodes altogether.Recognizing the importance of this challenge,we have introduced a new solution called ad hoc On-Demand Distance Vector-Reputation-based mechanism Local Outlier Factor(AODV-RL).The significance of AODVRL lies in its unique approach:it verifies and confirms the trustworthiness of network components,providing robust protection against BHA.An additional safety layer is established by implementing the Local Outlier Factor(LOF),which detects and addresses abnormal network behaviors.Rigorous testing of our solution has revealed its remarkable ability to enhance communication in VANETs.Specifically,Our experimental results achieve message delivery ratios of up to 94.25%andminimal packet loss ratios of just 0.297%.Based on our experimental results,the proposedmechanismsignificantly improves VANET communication reliability and security.These results promise a more secure and dependable future for IoV,capable of transforming transportation safety and efficiency.
文摘传统移动Ad Hoc网络(mobile Ad Hoc network,MANETs)黑洞攻击解析模型存在网络拓扑结构固定、网络传输性能预测精确度低的问题。针对使用按需路由协议的MANETs网络,提出一种基于随机拓扑近似技术的黑洞攻击解析改进模型(improved black hole attack analytical model,IBAAM)。IBAAM协议使用随机模型代替传统解析模型使用的n元2立方体模型,并将网络结构扩展至随机拓扑结构,使用最短跳距离概率描述表示网络拓扑结构的随机拓扑信息,再使用K均值聚类法实现跳距离文件配置以求解任意拓扑结构下的攻击概率问题,从而在不利用任何实际拓扑先验信息条件下有效预测MANETs网络平均丢包数目。IBAAM实验结果表明,在多种不同固定Ad Hoc拓扑结构下,IBAAM的网络丢包预测精确度在仿真结果的95%置信区间内,能够有效预测网络传输性能。
文摘为了解决分布式拒绝服务(Distributed Denial of Service,DDoS)攻击使云计算的最终用户无法访问云服务的问题,该文提出一种基于投票极限学习机(Voting Extreme Learning Machine,V-ELM)和黑洞优化的云计算DDoS攻击检测算法.该算法采用V-ELM作为分类器进行系统设计,使用多个极端学习机器同时检测攻击.使用数据包分析器捕获网络流量生成供分类器使用的样本,然后使用黑洞优化训练V-ELM中的所有ELM,在攻击检测过程中将样本应用于每个ELM并计算输出,最后在多数投票的基础上合并得到最终输出.实验结果表明:该文提出的算法在网络安全实验知识发现与数据挖掘(Network Security Lab Knowledge Discovery and Data Mining,NSL KDD)数据集和KDD分布式拒绝服务(KDD Distributed Denial of Service,KDD DDoS)数据集上的准确性、灵敏度和特异性均优于所对比的方法.
文摘在移动Ad Hoc网络(Mobile Ad Hoc Networks,MANETs)中,节点之间通过协作交流实现信息传输.为阻止MANETs网络中恶意节点使用黑洞/灰洞协作攻击网络,基于动态源路由的路由机制,提出一种协同诱饵检测方法(Cooperative bait detection approach,CBDA).CBDA算法中,源节点随机从邻居节点中选择协同节点,发送虚假RREQ分组诱骗恶意节点,使用反向追踪技术实现目标跟踪并检测出恶意节点,方法同时具有表驱和按需两种防御策略的优点.仿真结果证明,在恶意节点存在的MANETs网络环境下,使用CBDA的网络分组传递率和路由开销优于按需路由协议.
文摘This paper presents a reliable and secure supervisory control and data acquisition (SCADA) system equipped with advanced communication technologies (ACT) to enhance the operation and cyber security of the communication network in residential microgrid. The proposed approach uses the mobile ad hoc networks (MANET) for collecting data of power consumption from smart meters of residential areas and electric vehicles (EVs), and also for connecting mobile system operators to the network. Moreover, by understanding the dynamic nature of MANET and their exposure to cyber-attacks, we propose an intrusion detection and prevention (IDP) technology with secure knowledge algorithm and anomaly detection for preventing the black hole attacks, and other unknown attacks that result into packet dropping. Test results obtained by using Network Simulator (NS-2) demonstrate the effectiveness of the proposed IDP technology in preventing the cyber-attacks in the proposed residential microgrid communication network.
