Chinese President Xi Jinping has emphasized the unity of knowing and doing on a number of occasions,saying that knowing is the premise,while doing is putting knowledge to work,and these two ideas have a mutually reinf...Chinese President Xi Jinping has emphasized the unity of knowing and doing on a number of occasions,saying that knowing is the premise,while doing is putting knowledge to work,and these two ideas have a mutually reinforcing relationship.UNITY of knowing and doing is a key concept in ancient Chinese philosophy.Discussions on knowing and doing can be traced back to the pre-Qin period(before 221 B.C.).展开更多
针对网联车队列系统易受到干扰和拒绝服务(Denial of service, DoS)攻击问题,提出一种外部干扰和随机DoS攻击作用下的网联车安全H∞队列控制方法.首先,采用马尔科夫随机过程,将网联车随机DoS攻击特性建模为一个随机通信拓扑切换模型,据...针对网联车队列系统易受到干扰和拒绝服务(Denial of service, DoS)攻击问题,提出一种外部干扰和随机DoS攻击作用下的网联车安全H∞队列控制方法.首先,采用马尔科夫随机过程,将网联车随机DoS攻击特性建模为一个随机通信拓扑切换模型,据此设计网联车安全队列控制协议.然后,采用线性矩阵不等式(Linear matrix inequality, LMI)技术计算安全队列控制器参数,并应用Lyapunov-Krasovskii稳定性理论,建立在外部扰动和随机DoS攻击下队列系统稳定性充分条件.在此基础上,分析得到该队列闭环系统的弦稳定性充分条件.最后,通过7辆车组成的队列系统对比仿真实验,验证该方法的优越性.展开更多
多传感器网络化线性离散系统的每个传感器基于自己的观测数据可进行局部状态估计。当局部估值被传输给融合中心时,可能遭受DoS(Denial of service)攻击。为了补偿DoS攻击引起的数据丢失,采用丢失数据的预报器进行补偿。应用线性无偏最...多传感器网络化线性离散系统的每个传感器基于自己的观测数据可进行局部状态估计。当局部估值被传输给融合中心时,可能遭受DoS(Denial of service)攻击。为了补偿DoS攻击引起的数据丢失,采用丢失数据的预报器进行补偿。应用线性无偏最小方差矩阵加权融合算法获得分布式融合状态滤波器。所提出的分布式融合滤波器改善了局部估计的精度,且比协方差交叉融合算法具有更高的估计精度。仿真例子验证了算法的有效性。展开更多
传统电力系统容易受到网络干扰和攻击,系统中某一部分受到攻击可能会导致整个电力系统瘫痪。由于现代电力系统的广域性和灵活性会导致出现更多的网络攻击点,因此针对新领域研究更多的防御策略变得至关重要。基于此,利用连续时域模型对...传统电力系统容易受到网络干扰和攻击,系统中某一部分受到攻击可能会导致整个电力系统瘫痪。由于现代电力系统的广域性和灵活性会导致出现更多的网络攻击点,因此针对新领域研究更多的防御策略变得至关重要。基于此,利用连续时域模型对各种攻击策略进行建模,并分析电力系统防御拒绝服务(Denial of Service,DoS)攻击的机制。展开更多
The increasing prevalence of Internet of Things(IoT)devices has introduced a new phase of connectivity in recent years and,concurrently,has opened the floodgates for growing cyber threats.Among the myriad of potential...The increasing prevalence of Internet of Things(IoT)devices has introduced a new phase of connectivity in recent years and,concurrently,has opened the floodgates for growing cyber threats.Among the myriad of potential attacks,Denial of Service(DoS)attacks and Distributed Denial of Service(DDoS)attacks remain a dominant concern due to their capability to render services inoperable by overwhelming systems with an influx of traffic.As IoT devices often lack the inherent security measures found in more mature computing platforms,the need for robust DoS/DDoS detection systems tailored to IoT is paramount for the sustainable development of every domain that IoT serves.In this study,we investigate the effectiveness of three machine learning(ML)algorithms:extreme gradient boosting(XGB),multilayer perceptron(MLP)and random forest(RF),for the detection of IoTtargeted DoS/DDoS attacks and three feature engineering methods that have not been used in the existing stateof-the-art,and then employed the best performing algorithm to design a prototype of a novel real-time system towards detection of such DoS/DDoS attacks.The CICIoT2023 dataset was derived from the latest real-world IoT traffic,incorporates both benign and malicious network traffic patterns and after data preprocessing and feature engineering,the data was fed into our models for both training and validation,where findings suggest that while all threemodels exhibit commendable accuracy in detectingDoS/DDoS attacks,the use of particle swarmoptimization(PSO)for feature selection has made great improvements in the performance(accuracy,precsion recall and F1-score of 99.93%for XGB)of the ML models and their execution time(491.023 sceonds for XGB)compared to recursive feature elimination(RFE)and randomforest feature importance(RFI)methods.The proposed real-time system for DoS/DDoS attack detection entails the implementation of an platform capable of effectively processing and analyzing network traffic in real-time.This involvesemploying the best-performing ML algorithmfor detection and the integration of warning mechanisms.We believe this approach will significantly enhance the field of security research and continue to refine it based on future insights and developments.展开更多
The Double Take column looks at a single topic from an African and Chinese perspective.This month we discuss whether highly educated people like college graduates should be doing manual labour work.Before I delve into...The Double Take column looks at a single topic from an African and Chinese perspective.This month we discuss whether highly educated people like college graduates should be doing manual labour work.Before I delve into the topic,I want to clarify that I do not consider manual labour jobs to be inferior or less demanding compared to white collar jobs.In fact,currently there are many services,such as Japanese-style moving,an all-inclusive moving service during which you don’t need to do anything,or repair services dedicated to single women.展开更多
"Learning by Doing"是由美国卡内基·梅隆大学率先提出的一种旨在强化工程学科的学生全面实践能力和工程素养的教学模式。其目的就是让学生在"做"的过程中,深刻掌握相关的技术和技能,获得远超过课堂教学的教..."Learning by Doing"是由美国卡内基·梅隆大学率先提出的一种旨在强化工程学科的学生全面实践能力和工程素养的教学模式。其目的就是让学生在"做"的过程中,深刻掌握相关的技术和技能,获得远超过课堂教学的教学效果。本文首先介绍了"LearningbyDoing"的概念及作用,然后详细讨论了在"WindowsCE嵌入式系统"课程中实施"LearningbyDoing"的具体做法以及经验得失。展开更多
文摘Chinese President Xi Jinping has emphasized the unity of knowing and doing on a number of occasions,saying that knowing is the premise,while doing is putting knowledge to work,and these two ideas have a mutually reinforcing relationship.UNITY of knowing and doing is a key concept in ancient Chinese philosophy.Discussions on knowing and doing can be traced back to the pre-Qin period(before 221 B.C.).
文摘多传感器网络化线性离散系统的每个传感器基于自己的观测数据可进行局部状态估计。当局部估值被传输给融合中心时,可能遭受DoS(Denial of service)攻击。为了补偿DoS攻击引起的数据丢失,采用丢失数据的预报器进行补偿。应用线性无偏最小方差矩阵加权融合算法获得分布式融合状态滤波器。所提出的分布式融合滤波器改善了局部估计的精度,且比协方差交叉融合算法具有更高的估计精度。仿真例子验证了算法的有效性。
文摘传统电力系统容易受到网络干扰和攻击,系统中某一部分受到攻击可能会导致整个电力系统瘫痪。由于现代电力系统的广域性和灵活性会导致出现更多的网络攻击点,因此针对新领域研究更多的防御策略变得至关重要。基于此,利用连续时域模型对各种攻击策略进行建模,并分析电力系统防御拒绝服务(Denial of Service,DoS)攻击的机制。
文摘The increasing prevalence of Internet of Things(IoT)devices has introduced a new phase of connectivity in recent years and,concurrently,has opened the floodgates for growing cyber threats.Among the myriad of potential attacks,Denial of Service(DoS)attacks and Distributed Denial of Service(DDoS)attacks remain a dominant concern due to their capability to render services inoperable by overwhelming systems with an influx of traffic.As IoT devices often lack the inherent security measures found in more mature computing platforms,the need for robust DoS/DDoS detection systems tailored to IoT is paramount for the sustainable development of every domain that IoT serves.In this study,we investigate the effectiveness of three machine learning(ML)algorithms:extreme gradient boosting(XGB),multilayer perceptron(MLP)and random forest(RF),for the detection of IoTtargeted DoS/DDoS attacks and three feature engineering methods that have not been used in the existing stateof-the-art,and then employed the best performing algorithm to design a prototype of a novel real-time system towards detection of such DoS/DDoS attacks.The CICIoT2023 dataset was derived from the latest real-world IoT traffic,incorporates both benign and malicious network traffic patterns and after data preprocessing and feature engineering,the data was fed into our models for both training and validation,where findings suggest that while all threemodels exhibit commendable accuracy in detectingDoS/DDoS attacks,the use of particle swarmoptimization(PSO)for feature selection has made great improvements in the performance(accuracy,precsion recall and F1-score of 99.93%for XGB)of the ML models and their execution time(491.023 sceonds for XGB)compared to recursive feature elimination(RFE)and randomforest feature importance(RFI)methods.The proposed real-time system for DoS/DDoS attack detection entails the implementation of an platform capable of effectively processing and analyzing network traffic in real-time.This involvesemploying the best-performing ML algorithmfor detection and the integration of warning mechanisms.We believe this approach will significantly enhance the field of security research and continue to refine it based on future insights and developments.
文摘The Double Take column looks at a single topic from an African and Chinese perspective.This month we discuss whether highly educated people like college graduates should be doing manual labour work.Before I delve into the topic,I want to clarify that I do not consider manual labour jobs to be inferior or less demanding compared to white collar jobs.In fact,currently there are many services,such as Japanese-style moving,an all-inclusive moving service during which you don’t need to do anything,or repair services dedicated to single women.
文摘"Learning by Doing"是由美国卡内基·梅隆大学率先提出的一种旨在强化工程学科的学生全面实践能力和工程素养的教学模式。其目的就是让学生在"做"的过程中,深刻掌握相关的技术和技能,获得远超过课堂教学的教学效果。本文首先介绍了"LearningbyDoing"的概念及作用,然后详细讨论了在"WindowsCE嵌入式系统"课程中实施"LearningbyDoing"的具体做法以及经验得失。
