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...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.展开更多
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 ne...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.展开更多
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 informat...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 </span><span style="font-family:Verdana;">CVs is on the basis of the assumption that correct vehicle information is </span><span style="font-family:Verdana;">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. </span><span style="font-family:Verdana;">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 </span><span style="font-family:Verdana;">is suitable for detecting misbehavior in which the position information</span><span style="font-family:Verdana;"> changes over time.展开更多
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 comm...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.展开更多
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....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.展开更多
基金This research was financially supported in part by Researchers Supporting Project(TURSP-2020/121),Taif University,Saudi Arabia.
文摘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.
文摘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.
文摘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 </span><span style="font-family:Verdana;">CVs is on the basis of the assumption that correct vehicle information is </span><span style="font-family:Verdana;">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. </span><span style="font-family:Verdana;">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 </span><span style="font-family:Verdana;">is suitable for detecting misbehavior in which the position information</span><span style="font-family:Verdana;"> changes over time.
文摘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.
文摘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.
