Machine Learning Enabled Novel Real-Time IoT Targeted DoS/DDoS Cyber Attack Detection System

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.

“Learning by Doing”教学模式的探索

"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。

Learning-by-doing教学模式在安全系统工程教学中的应用

安全系统工程课程是安全工程专业的专业基础课,其教学效果的好坏对后续课程的学习以及日后所从事的工作至关重要。因而该课程教学方法的运用选择值得深思。笔者分析了安全工程专业中安全系统工程课程的特点和教学中存在的问题,引用learning-by-doing的教学模式并在教学中加以应用,提出"通过授课得到答案——学会一个解,通过案例讨论得到方法——学会一个方法,通过实践模拟学会学习——学会找到个方法,通过总结学会融会贯通"的安全系统工程教学模式,收到了较好的教学效果。

“Flash动画设计”课程“Learning by doing”教学法探索

本文针对"Flash动画设计"的课程特点引入"Learningbydoing"教学理念,提出"简单实例掌握基本技能→案例讨论学会分析方法→模拟实践学会找到方法→总结经验融会贯通同时实现创新提高"的教学模式,在教学实践中取得了良好的效果。

基于Learning-by-doing的计算机系统结构课程改革

在计算机系统结构的课程教学中,引入由卡内基.梅隆大学提出的"Learning-by-doing"这一适用于工程教学的行之有效的先进教学理念是新教学模式的一种积极探索。文章围绕基于"Learning-by-doing"教学法的计算机系统结构课程改革实施的前期准备、遇到的问题,具体解决方案等环节进行了讨论和分析。

具有不完全技术外部性的随机Learning-by-Doing模型及解法

提出适用于随机Learning-by-Doing模型的"附加效用"值函数解法,并用此方法求解具有不完全技术外部性的随机learning-by-doing模型,得到了均衡时的经济增长路径、消费—资本比和值函数,讨论了技术外部性对私人资本回报率、消费倾向、均值经济增长率和个体福利的影响.

“嵌入式系统程序设计实习”教学改革——探索“Learning by Doing”教学模式

"Learning by Doing"是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。本文阐述了在"嵌入式系统程序设计实习"课程中实施"Learning by Doing"的具体方法以及一些经验得失。

基于Learning-by-doing的不确定经济增长与财政政策研究

本文研究基于learning-by-doing的随机增长模型,得到了均衡时的经济增长路径、债券回报率、资产组合份额和消费-资本比,分析财政政策对长期经济增长、资产组合选择、个体消费倾向和个体福利的影响,探讨最优的财政政策。

“干中学(Learning by Doing)”——浅议课堂教学方法改革

提出了“干中学”的新型课堂教学观.阐释了学生通过“干中学”能主动参与课堂教学,成为教学的真正主体.从传统课堂教学和新型课堂教学的比较,说明新型的师生关系是双向的、交互的,教师作为课堂教学的组织者和协调者,在教学中只能起“主导”作用.

一个多资本的Learning-by-doing模型

考虑一个多资本投入的一般Learning by doing模型 .该模型技术进步的增长率由经济发展过程内生地决定 .当技术对资本为递减规模回报时 ,该经济仅有唯一正的均衡点 .运用单调动力系统理论论证了此模型的稳定性问题 。

Learning by doing方法在移动平台应用开发课中的应用

"嵌入式移动平台应用开发"课程是电子信息科学与技术专业的专业课,以培养学生的嵌入式软件开发能力为目的。将Learning by doing教学模式应用到嵌入式移动平台应用开发课程中,通过改革授课方式、教学内容组织以及考核方式,使学生在做中理解所学的知识,融会贯通,实操能力和编程动手能力得到提高。通过实践,取得了良好的教学效果,培养了学生的创新精神和解决实际问题的能力。

Progressive Transfer Learning-based Deep Q Network for DDOS Defence in WSN

In The Wireless Multimedia Sensor Network(WNSMs)have achieved popularity among diverse communities as a result of technological breakthroughs in sensor and current gadgets.By utilising portable technologies,it achieves solid and significant results in wireless communication,media transfer,and digital transmission.Sensor nodes have been used in agriculture and industry to detect characteristics such as temperature,moisture content,and other environmental conditions in recent decades.WNSMs have also made apps easier to use by giving devices self-governing access to send and process data connected with appro-priate audio and video information.Many video sensor network studies focus on lowering power consumption and increasing transmission capacity,but the main demand is data reliability.Because of the obstacles in the sensor nodes,WMSN is subjected to a variety of attacks,including Denial of Service(DoS)attacks.Deep Convolutional Neural Network is designed with the stateaction relationship mapping which is used to identify the DDOS Attackers present in the Wireless Sensor Networks for Smart Agriculture.The Proposed work it performs the data collection about the traffic conditions and identifies the deviation between the network conditions such as packet loss due to network congestion and the presence of attackers in the network.It reduces the attacker detection delay and improves the detection accuracy.In order to protect the network against DoS assaults,an improved machine learning technique must be offered.An efficient Deep Neural Network approach is provided for detecting DoS in WMSN.The required parameters are selected using an adaptive particle swarm optimization technique.The ratio of packet transmission,energy consumption,latency,network length,and throughput will be used to evaluate the approach’s efficiency.

基于联邦学习的无线通信网络DoS攻击检测方法

无线通信网络受到DoS攻击,会使得网络的负载增加,导致延迟增加。而在无线通信网络中,数据通常分散在多个节点上,这会造成数据泄露和被攻击。为此,提出一种基于联邦学习的无线通信网络DoS攻击检测方法。对初始无线通信网络数据进行预处理和归一化,并采用随机森林算法进行降维处理,去除冗余特征,获得最佳网络数据特征集。将特征集输入到以深度卷积神经网络为通用模型的联邦学习训练模型中,独立训练本地模型并进行模型修正,传输至中心服务器进行聚合,收敛后完成训练。利用训练得到的联邦学习模型检测无线通信网络DoS攻击速率,再与接收者接收的容量最大值进行比较,判断是否有DoS攻击。实验结果表明,所提方法在处理大量数据时具有较高的稳定性和可靠性,能够在短时间内准确地检测出DoS攻击。

Under "Integration of Doing, Learning and Teaching", Research on the Project-Based Teaching Innovation of "Landscape Planning and Design"

Based on the research on the project course theory of "integration of theory and practice" in higher vocational education and the analysis of practical teaching in colleges and universities at home and abroad, combined with literature research, case analysis, system theory and other research methods, the project-based teaching goal, model, content and means of "integration of doing, learning and teaching" in higher vocational education is explored, and the project-based teaching model of "Landscape Planning and Design" is discussed combined with the application of information-based teaching methods. So as to provide references for carrying out the project-based teaching in similar courses in higher vocational colleges and really achieve docking the actual post requirements with the course to provide the basis for achieving the purpose of cultivating skilled talents in higher vocational education.

Learning by Doing Effect in North South Trade under the Global Value Chains： An Empirical Analysis of Various Industries in the U.S.

This paper studies the division of labor and economic development under global value chains in North South trade by mainly investigating the changes of production hours and cost per unit along with more and more output and increasing trade value in several industries in the U.S., because the U. S. is at the leading position in the division of labor by global value chains. The empirical evidence reveals that more international outsourcing, there will be more detailed division of labor, and the industry unit production time and production cost will show more declining trend year by year. This is consistent with that the global value chains and the outsourcing play more and more important roles in the international division of labor and economic growth in both developed and developing countries, and helps explain the integration of workforce across countries in the global value chains.

Machine Learning Approaches to Detect DoS and Their Effect on WSNs Lifetime

Energy and security remain the main two challenges in Wireless Sensor Networks(WSNs).Therefore,protecting these WSN networks from Denial of Service(DoS)and Distributed DoS(DDoS)is one of the WSN networks security tasks.Traditional packet deep scan systems that rely on open field inspection in transport layer security packets and the open field encryption trend are making machine learning-based systems the only viable choice for these types of attacks.This paper contributes to the evaluation of the use machine learning algorithms in WSN nodes traffic and their effect on WSN network life time.We examined the performance metrics of different machine learning classification categories such asK-Nearest Neighbour(KNN),Logistic Regression(LR),Support Vector Machine(SVM),Gboost,Decision Tree(DT),Na飗e Bayes,Long Short Term Memory(LSTM),and Multi-Layer Perceptron(MLP)on aWSN-dataset in different sizes.The test results proved that the statistical and logical classification categories performed the best on numeric statistical datasets,and the Gboost algorithm showed the best performance compared to different algorithms on average of all performance metrics.The performance metrics used in these validations were accuracy,F1-score,False Positive Ratio(FPR),False Negative Ratio(FNR),and the training execution time.Moreover,the test results showed the Gboost algorithm got 99.6%,98.8%,0.4%0.13%in accuracy,F1-score,FPR,and FNR,respectively.At training execution time,it obtained 1.41 s for the average of all training time execution datasets.In addition,this paper demonstrated that for the numeric statistical data type,the best results are in the size of the dataset ranging from3000 to 6000 records and the percentage between categories is not less than 50%for each category with the other categories.Furthermore,this paper investigated the effect of Gboost on the WSN lifetime,which resulted in a 32%reduction compared to other Gboost-free scenarios.

DoS攻击下多智能体系统无模型自适应迭代学习跟踪控制

针对周期性拒绝服务(DoS)攻击下多智能体系统有限时间趋同跟踪控制问题,本文提出了一种无模型自适应迭代学习控制(MFAILC)算法.假设多智能体系统具有固定拓扑结构,并且仅有部分智能体可获取到期望轨迹信息.在多智能体系统数据传输过程中,需要经由对数量化器进行量化处理.首先,使用伪偏导数将智能体系统动态线性化,处理过程中考虑符合伯努利分布的周期性DoS攻击现象,在此基础上设计了MFAILC控制算法,其次,采用压缩映射方法给出了一个在期望意义下保证跟踪误差收敛的充分条件,并在理论上证明了所提算法的收敛性.所提算法只需利用系统的输入输出数据就可完成趋同跟踪任务.最后,仿真结果验证了所提算法的有效性.

“Can-do”模式下基础日语教学多维化的探索

2010年日本国际交流基金会发布的《JF日语教育标准2010》是用以促进国际日语教育、学习和评估的参考标准,其主要目的是培养日语学习者的跨文化交际能力。这与我国2020年发布的《普通高等学校本科外国语言文学类专业教学指南》中提出的日语专业应该培养学生灵活运用策略完成跨文化交流任务的目标不谋而合。在新文科建设以及新国标颁布的背景下,高校的日语教育面临着诸多新的挑战,该文旨在通过结合TPR教学法,在基础日语教学中创新应用JF标准中的“Can-do”模式以及导入、完善电子学习档案等,以促进学习者跨文化交际能力的提升。与此同时,还将从打造师生学习共同体、创新教学内容和探索多维化教学等去构建有利于学习者主体能动性发挥的基础日语教学模式。

光伏组件技术进步的“干中学”与“研中学”效应研究

光伏产业是我国具有全球竞争优势的战略性新兴产业,推动其高质量发展是实现“3060”双碳目标的重要手段之一。采用双因素学习曲线模型,参照索罗余值法测度从2015—2020年光伏组件技术进步的“干中学”效应和“研中学”效应。其中,由“干中学”效应导致的光伏组件成本下降约为0.85元/瓦,贡献率为42.40%;由“研中学”效应导致的成本下降约为1.07元/瓦,贡献率为57.60%。进一步对光伏组件“干中学”效应和“研中学”效应的驱动因素进行研究。“干中学”效应方面,从供给侧和需求侧两个维度采用文献分析法讨论了驱动光伏组件产量增加的因素;“研中学”效应方面,采用知识生产函数分析研发资本投入和人力投入对光伏组件技术进步速度的影响。结果表明,光伏组件研发资本投入存在边际报酬递减的现象,而研发人力投入的边际报酬是递增的。在此基础上,分析研究结果的政策启示。