With the increasing prevalence of social networks, more and more social network data are published for many applications, such as social network analysis and data mining. However, this brings privacy problems. For exa...With the increasing prevalence of social networks, more and more social network data are published for many applications, such as social network analysis and data mining. However, this brings privacy problems. For example, adversaries can get sensitive information of some individuals easily with little background knowledge. How to publish social network data for analysis purpose while preserving the privacy of individuals has raised many concerns. Many algorithms have been proposed to address this issue. In this paper, we discuss this privacy problem from two aspects： attack models and countermeasures. We analyse privacy conceres, model the background knowledge that adversary may utilize and review the recently developed attack models. We then survey the state-of-the-art privacy preserving methods in two categories： anonymization methods and differential privacy methods. We also provide research directions in this area.展开更多
The cyberspace has simultaneously presented opportunities and challenges alike for personal data security and privacy, as well as the process of research and learning. Moreover, information such as academic data, rese...The cyberspace has simultaneously presented opportunities and challenges alike for personal data security and privacy, as well as the process of research and learning. Moreover, information such as academic data, research data, personal data, proprietary knowledge, complex equipment designs and blueprints for yet to be patented products has all become extremely susceptible to Cybersecurity attacks. This research will investigate factors that affect that may have an influence on perceived ease of use of Cybersecurity, the influence of perceived ease of use on the attitude towards using Cybersecurity, the influence of attitude towards using Cybersecurity on the actual use of Cybersecurity and the influences of job positions on perceived ease of use of Cybersecurity and on the attitude towards using Cybersecurity and on the actual use of Cybersecurity. A model was constructed to investigate eight hypotheses that are related to the investigation. An online questionnaire was constructed to collect data and results showed that hypotheses 1 to 7 influence were significant. However, hypothesis 8 turned out to be insignificant and no influence was found between job positions and the actual use of Cybersecurity.展开更多
针对受扰移动机器人系统自触发模型预测控制(self-triggered model predictive control,STMPC)在虚假数据注入(false data injection,FDI)攻击下的安全控制问题,提出了一种基于输入重构的弹性STMPC方法。结合自触发机制非周期采样特性和...针对受扰移动机器人系统自触发模型预测控制(self-triggered model predictive control,STMPC)在虚假数据注入(false data injection,FDI)攻击下的安全控制问题,提出了一种基于输入重构的弹性STMPC方法。结合自触发机制非周期采样特性和FDI攻击模型,设计了一种基于关键数据的输入重构机制,以减弱FDI攻击对被控系统的影响。根据状态误差的最优控制问题,设计了重构参数的确定方法,以保证系统在应用重构控制输入后的控制性能。详细分析了所提出弹性STMPC算法的稳定性以及算法可行性。通过仿真和实验验证了所提出算法的有效性。展开更多
随着电力配变网络基础设施规模的不断扩大,各类安全二次设备、边缘终端节点和业务系统产生的信息通信流量数据在格式、协议、语义特征层面存在显著差异。主要存在现有缓解框架缺乏多源异构网络异常流量检测数据归一化处理算法,网络攻击...随着电力配变网络基础设施规模的不断扩大,各类安全二次设备、边缘终端节点和业务系统产生的信息通信流量数据在格式、协议、语义特征层面存在显著差异。主要存在现有缓解框架缺乏多源异构网络异常流量检测数据归一化处理算法,网络攻击行为分析依赖人工特征提取的规则引擎,以及难以确定有效的网络攻击缓解措施等痛点。针对以上痛点,提出了一种基于归一化处理和TrafficLLM的网络攻击缓解框架(Network Attack Mitigation Framework Based on Normalized Processing and TrafficLLM,NAMF-NPTLLM)。该框架涵盖数据解析、归一化处理、模型微调和生成攻击缓解方案4个核心阶段。首先,在特征选择阶段,通过构建集成学习模型,融合多类基学习器的特征评估结果,精准提取对分类结果影响较大的关键特征。其次,将选取的关键特征通过归一化处理,生成统一的自然语言token序列形式表达,为该网络攻击缓解框架的流量异常分析TrafficLLM模型提供标准化输入。然后,对TrafficLLM模型进行微调,使该模型能够理解提示词模板指令并学习攻击行为的流量模式。最后,通过微调后的大模型进行推理,生成攻击缓解指令,使得该框架能够根据攻击行为特征动态调整网络攻击缓解策略。通过在CIC-DDoS2019数据集上进行实验验证,与传统方法相比,该框架将网络攻击行为分类的准确率达到99.80%,提高了1.3%。实验结果表明,该框架对于缓解海量多源异构电力网络终端流量攻击,具有更好的准确性和有效性。展开更多
作为支撑电网安全稳定运行的主要手段,电力数据采集与监控(supervisory control and data acquisition,SCADA)系统的网络安全问题备受关注。鉴于此,该文提出一种计及残差污染的虚假数据注入攻击(false data injection attack,FDIA)新模...作为支撑电网安全稳定运行的主要手段,电力数据采集与监控(supervisory control and data acquisition,SCADA)系统的网络安全问题备受关注。鉴于此,该文提出一种计及残差污染的虚假数据注入攻击(false data injection attack,FDIA)新模式。该模式利用电力系统状态估计中的残差污染现象,诱导不良数据辨识环节剔除正常量测而保留篡改量测,从而精准误导状态估计的结果;针对加权/标准化残差搜索法辨识原理的不同,该文提出两种攻击模型;考虑到攻击者掌握不完整网络信息的现实情况,挖掘不完全信息下的攻击建模机理,设计基于机理驱动与图论搜索的攻击方案寻优算法。算例表明,攻击者仅需掌握局部拓扑结构和线路参数,就能在几十ms内构造攻击向量,并以很小的攻击代价误导直流/交流状态估计结果,破坏电网安全稳定经济运行。展开更多
文摘With the increasing prevalence of social networks, more and more social network data are published for many applications, such as social network analysis and data mining. However, this brings privacy problems. For example, adversaries can get sensitive information of some individuals easily with little background knowledge. How to publish social network data for analysis purpose while preserving the privacy of individuals has raised many concerns. Many algorithms have been proposed to address this issue. In this paper, we discuss this privacy problem from two aspects： attack models and countermeasures. We analyse privacy conceres, model the background knowledge that adversary may utilize and review the recently developed attack models. We then survey the state-of-the-art privacy preserving methods in two categories： anonymization methods and differential privacy methods. We also provide research directions in this area.
文摘The cyberspace has simultaneously presented opportunities and challenges alike for personal data security and privacy, as well as the process of research and learning. Moreover, information such as academic data, research data, personal data, proprietary knowledge, complex equipment designs and blueprints for yet to be patented products has all become extremely susceptible to Cybersecurity attacks. This research will investigate factors that affect that may have an influence on perceived ease of use of Cybersecurity, the influence of perceived ease of use on the attitude towards using Cybersecurity, the influence of attitude towards using Cybersecurity on the actual use of Cybersecurity and the influences of job positions on perceived ease of use of Cybersecurity and on the attitude towards using Cybersecurity and on the actual use of Cybersecurity. A model was constructed to investigate eight hypotheses that are related to the investigation. An online questionnaire was constructed to collect data and results showed that hypotheses 1 to 7 influence were significant. However, hypothesis 8 turned out to be insignificant and no influence was found between job positions and the actual use of Cybersecurity.
基金浙江省“尖兵”“领雁”研发攻关计划(2024C01058)浙江省“十四五”第二批本科省级教学改革备案项目(JGBA2024014)+2 种基金2025年01月批次教育部产学合作协同育人项目(2501270945)2024年度浙江大学本科“AI赋能”示范课程建设项目(24)浙江大学第一批AI For Education系列实证教学研究项目(202402)。
文摘针对受扰移动机器人系统自触发模型预测控制(self-triggered model predictive control,STMPC)在虚假数据注入(false data injection,FDI)攻击下的安全控制问题,提出了一种基于输入重构的弹性STMPC方法。结合自触发机制非周期采样特性和FDI攻击模型,设计了一种基于关键数据的输入重构机制,以减弱FDI攻击对被控系统的影响。根据状态误差的最优控制问题,设计了重构参数的确定方法,以保证系统在应用重构控制输入后的控制性能。详细分析了所提出弹性STMPC算法的稳定性以及算法可行性。通过仿真和实验验证了所提出算法的有效性。
文摘随着电力配变网络基础设施规模的不断扩大,各类安全二次设备、边缘终端节点和业务系统产生的信息通信流量数据在格式、协议、语义特征层面存在显著差异。主要存在现有缓解框架缺乏多源异构网络异常流量检测数据归一化处理算法,网络攻击行为分析依赖人工特征提取的规则引擎,以及难以确定有效的网络攻击缓解措施等痛点。针对以上痛点,提出了一种基于归一化处理和TrafficLLM的网络攻击缓解框架(Network Attack Mitigation Framework Based on Normalized Processing and TrafficLLM,NAMF-NPTLLM)。该框架涵盖数据解析、归一化处理、模型微调和生成攻击缓解方案4个核心阶段。首先,在特征选择阶段,通过构建集成学习模型,融合多类基学习器的特征评估结果,精准提取对分类结果影响较大的关键特征。其次,将选取的关键特征通过归一化处理,生成统一的自然语言token序列形式表达,为该网络攻击缓解框架的流量异常分析TrafficLLM模型提供标准化输入。然后,对TrafficLLM模型进行微调,使该模型能够理解提示词模板指令并学习攻击行为的流量模式。最后,通过微调后的大模型进行推理,生成攻击缓解指令,使得该框架能够根据攻击行为特征动态调整网络攻击缓解策略。通过在CIC-DDoS2019数据集上进行实验验证,与传统方法相比,该框架将网络攻击行为分类的准确率达到99.80%,提高了1.3%。实验结果表明,该框架对于缓解海量多源异构电力网络终端流量攻击,具有更好的准确性和有效性。
文摘作为支撑电网安全稳定运行的主要手段,电力数据采集与监控(supervisory control and data acquisition,SCADA)系统的网络安全问题备受关注。鉴于此,该文提出一种计及残差污染的虚假数据注入攻击(false data injection attack,FDIA)新模式。该模式利用电力系统状态估计中的残差污染现象,诱导不良数据辨识环节剔除正常量测而保留篡改量测,从而精准误导状态估计的结果;针对加权/标准化残差搜索法辨识原理的不同,该文提出两种攻击模型;考虑到攻击者掌握不完整网络信息的现实情况,挖掘不完全信息下的攻击建模机理,设计基于机理驱动与图论搜索的攻击方案寻优算法。算例表明,攻击者仅需掌握局部拓扑结构和线路参数,就能在几十ms内构造攻击向量,并以很小的攻击代价误导直流/交流状态估计结果,破坏电网安全稳定经济运行。
