The RPL(IPv6 Routing Protocol for Low-Power and Lossy Networks)protocol is essential for efficient communi-cation within the Internet of Things(IoT)ecosystem.Despite its significance,RPL’s susceptibility to attacks r...The RPL(IPv6 Routing Protocol for Low-Power and Lossy Networks)protocol is essential for efficient communi-cation within the Internet of Things(IoT)ecosystem.Despite its significance,RPL’s susceptibility to attacks remains a concern.This paper presents a comprehensive simulation-based analysis of the RPL protocol’s vulnerability to the decreased rank attack in both static andmobilenetwork environments.We employ the Random Direction Mobility Model(RDM)for mobile scenarios within the Cooja simulator.Our systematic evaluation focuses on critical performance metrics,including Packet Delivery Ratio(PDR),Average End to End Delay(AE2ED),throughput,Expected Transmission Count(ETX),and Average Power Consumption(APC).Our findings illuminate the disruptive impact of this attack on the routing hierarchy,resulting in decreased PDR and throughput,increased AE2ED,ETX,and APC.These results underscore the urgent need for robust security measures to protect RPL-based IoT networks.Furthermore,our study emphasizes the exacerbated impact of the attack in mobile scenarios,highlighting the evolving security requirements of IoT networks.展开更多
To date, few studies have investigated the impact of organizational factors such as organizational status or the rank of firefighters on the development of posttraumatic stress disorder (PTSD) following a terrorist at...To date, few studies have investigated the impact of organizational factors such as organizational status or the rank of firefighters on the development of posttraumatic stress disorder (PTSD) following a terrorist attack. To fill this gap in the scientific literature, this field study aimed to investigate the consequences of terrorist attacks on firefighters’ psychological health in terms of PTSD. Data were collected in France following two terrorist attacks. PTSD was assessed with the PCL-S (DSM-IV) 3 to 6 months after the events. Confirmatory factor analyses (CFAs) with existing PTSD models were inconclusive, leading us to find a two-factor model via an exploratory factor analysis (EFA). A cluster analysis showed different symptom profiles that were influenced by the exposure level. Elements for a structural model explaining PTSD symptoms are proposed and suggest a central role of the exposure level. Firefighters I/II represented an at-risk sub-population, suggesting that PTSD was mainly experienced among those who performed tasks not common to their occupation.展开更多
P2P网络中的节点很可能从另外的节点那里收到质量很差的服务和信息,名誉评价是解决该问题的常见方法.基于评分反馈的P2P名誉计算机制存在下述缺点:无法区分恶意评价和诚实节点给出错误评价间的差别;需要对评分可信度进行二次评价,使名...P2P网络中的节点很可能从另外的节点那里收到质量很差的服务和信息,名誉评价是解决该问题的常见方法.基于评分反馈的P2P名誉计算机制存在下述缺点:无法区分恶意评价和诚实节点给出错误评价间的差别;需要对评分可信度进行二次评价,使名誉计算速度减慢;用数字来表示节点名誉的方式不够自然.实际上,名誉评价的用途是确定节点可信度的相对顺序.因此,提出了一种基于排名反馈的P2P名誉评价机制RbRf(reputation based ranking feedback).针对RbRf和其上的恶意攻击进行了数学建模和理论分析,结果表明,RbRf中非恶意错误的影响随排名反馈的数量指数而衰减;一般恶意攻击对RbRf的影响随排名反馈数量的多项式而减小;对于有意设计的共谋攻击,由于必须给RbRf引入正确信息而导致了恶意攻击被有效中和.因此,RbRf不仅由于不再反馈打分信息而不存在评分反馈引起的名誉评价问题(如不需要对反馈信息的可信度进行二次评价),而且具有更好的抵抗恶意攻击的能力.仿真实验验证了理论分析的结果.展开更多
为了在高速网络环境下对大容量网络流量进行准确和快速的分类,以检测分布式拒绝服务(Distributed Denial of Service,DDoS)攻击,本文提出一种基于并行积累排序算法和主动学习的DDoS攻击检测算法.该技术采用并行积累排序算法对流量特征...为了在高速网络环境下对大容量网络流量进行准确和快速的分类,以检测分布式拒绝服务(Distributed Denial of Service,DDoS)攻击,本文提出一种基于并行积累排序算法和主动学习的DDoS攻击检测算法.该技术采用并行积累排序算法对流量特征进行积累排序来选择最佳特征子集,通过专家模块以无监督的方式选择适当的实例来训练用于检测DDoS攻击流量的支持向量机(SVM)二值分类器,从而实现从数据集中选择小批量训练样本来产生高精度的网络流量分类.实验结果表明,与现有方法相比,本文算法在分类准确率和执行速度方面均优于现有方法.展开更多
文摘The RPL(IPv6 Routing Protocol for Low-Power and Lossy Networks)protocol is essential for efficient communi-cation within the Internet of Things(IoT)ecosystem.Despite its significance,RPL’s susceptibility to attacks remains a concern.This paper presents a comprehensive simulation-based analysis of the RPL protocol’s vulnerability to the decreased rank attack in both static andmobilenetwork environments.We employ the Random Direction Mobility Model(RDM)for mobile scenarios within the Cooja simulator.Our systematic evaluation focuses on critical performance metrics,including Packet Delivery Ratio(PDR),Average End to End Delay(AE2ED),throughput,Expected Transmission Count(ETX),and Average Power Consumption(APC).Our findings illuminate the disruptive impact of this attack on the routing hierarchy,resulting in decreased PDR and throughput,increased AE2ED,ETX,and APC.These results underscore the urgent need for robust security measures to protect RPL-based IoT networks.Furthermore,our study emphasizes the exacerbated impact of the attack in mobile scenarios,highlighting the evolving security requirements of IoT networks.
文摘To date, few studies have investigated the impact of organizational factors such as organizational status or the rank of firefighters on the development of posttraumatic stress disorder (PTSD) following a terrorist attack. To fill this gap in the scientific literature, this field study aimed to investigate the consequences of terrorist attacks on firefighters’ psychological health in terms of PTSD. Data were collected in France following two terrorist attacks. PTSD was assessed with the PCL-S (DSM-IV) 3 to 6 months after the events. Confirmatory factor analyses (CFAs) with existing PTSD models were inconclusive, leading us to find a two-factor model via an exploratory factor analysis (EFA). A cluster analysis showed different symptom profiles that were influenced by the exposure level. Elements for a structural model explaining PTSD symptoms are proposed and suggest a central role of the exposure level. Firefighters I/II represented an at-risk sub-population, suggesting that PTSD was mainly experienced among those who performed tasks not common to their occupation.
文摘P2P网络中的节点很可能从另外的节点那里收到质量很差的服务和信息,名誉评价是解决该问题的常见方法.基于评分反馈的P2P名誉计算机制存在下述缺点:无法区分恶意评价和诚实节点给出错误评价间的差别;需要对评分可信度进行二次评价,使名誉计算速度减慢;用数字来表示节点名誉的方式不够自然.实际上,名誉评价的用途是确定节点可信度的相对顺序.因此,提出了一种基于排名反馈的P2P名誉评价机制RbRf(reputation based ranking feedback).针对RbRf和其上的恶意攻击进行了数学建模和理论分析,结果表明,RbRf中非恶意错误的影响随排名反馈的数量指数而衰减;一般恶意攻击对RbRf的影响随排名反馈数量的多项式而减小;对于有意设计的共谋攻击,由于必须给RbRf引入正确信息而导致了恶意攻击被有效中和.因此,RbRf不仅由于不再反馈打分信息而不存在评分反馈引起的名誉评价问题(如不需要对反馈信息的可信度进行二次评价),而且具有更好的抵抗恶意攻击的能力.仿真实验验证了理论分析的结果.
文摘为了在高速网络环境下对大容量网络流量进行准确和快速的分类,以检测分布式拒绝服务(Distributed Denial of Service,DDoS)攻击,本文提出一种基于并行积累排序算法和主动学习的DDoS攻击检测算法.该技术采用并行积累排序算法对流量特征进行积累排序来选择最佳特征子集,通过专家模块以无监督的方式选择适当的实例来训练用于检测DDoS攻击流量的支持向量机(SVM)二值分类器,从而实现从数据集中选择小批量训练样本来产生高精度的网络流量分类.实验结果表明,与现有方法相比,本文算法在分类准确率和执行速度方面均优于现有方法.
