This paper presents a computationally efficient real-time trajectory planning framework for typical unmanned combat aerial vehicle （UCAV） performing autonomous air-to-surface （A/S） attack. It combines the benefits...This paper presents a computationally efficient real-time trajectory planning framework for typical unmanned combat aerial vehicle （UCAV） performing autonomous air-to-surface （A/S） attack. It combines the benefits of inverse dynamics optimization method and receding horizon optimal control technique. Firstly, the ground attack trajectory planning problem is mathematically formulated as a receding horizon optimal control problem （RHC-OCP）. In particular, an approximate elliptic launch acceptable region （LAR） model is proposed to model the critical weapon delivery constraints. Secondly, a planning algorithm based on inverse dynamics optimization, which has high computational efficiency and good convergence properties, is developed to solve the RHCOCP in real-time. Thirdly, in order to improve robustness and adaptivity in a dynamic and uncer- tain environment, a two-degree-of-freedom （2-DOF） receding horizon control architecture is introduced and a regular real-time update strategy is proposed as well, and the real-time feedback can be achieved and the not-converged situations can be handled. Finally, numerical simulations demon- strate the efficiency of this framework, and the results also show that the presented technique is well suited for real-time implementation in dynamic and uncertain environment.展开更多
This paper provides a calculating method which can be used in calculation of the kill probability attack area for every AAM. At first, attack area of AAM and kill probability of every characteristic point are obtained...This paper provides a calculating method which can be used in calculation of the kill probability attack area for every AAM. At first, attack area of AAM and kill probability of every characteristic point are obtained by combining trajectory calculation with kill probability calculation. Then, coordinates of a fire point relative to standard kill probability value in terms of standardization method are found. At last, equivalent kill probability curve equations are formulated by means of curve fitting method.展开更多
The use of machine learning algorithms to identify characteristics in Distributed Denial of Service (DDoS) attacks has emerged as a powerful approach in cybersecurity. DDoS attacks, which aim to overwhelm a network or...The use of machine learning algorithms to identify characteristics in Distributed Denial of Service (DDoS) attacks has emerged as a powerful approach in cybersecurity. DDoS attacks, which aim to overwhelm a network or service with a flood of malicious traffic, pose significant threats to online systems. Traditional methods of detection and mitigation often struggle to keep pace with the evolving nature of these attacks. Machine learning, with its ability to analyze vast amounts of data and recognize patterns, offers a robust solution to this challenge. The aim of the paper is to demonstrate the application of ensemble ML algorithms, namely the K-Means and the KNN, for a dual clustering mechanism when used with PySpark to collect 99% accurate data. The algorithms, when used together, identify distinctive features of DDoS attacks that prove a very accurate reflection of reality, so they are a good combination for this aim. Impressively, having preprocessed the data, both algorithms with the PySpark foundation enabled the achievement of 99% accuracy when tuned on the features of a DDoS big dataset. The semi-supervised dataset tabulates traffic anomalies in terms of packet size distribution in correlation to Flow Duration. By training the K-Means Clustering and then applying the KNN to the dataset, the algorithms learn to evaluate the character of activity to a greater degree by displaying density with ease. The study evaluates the effectiveness of the K-Means Clustering with the KNN as ensemble algorithms that adapt very well in detecting complex patterns. Ultimately, cross-reaching environmental results indicate that ML-based approaches significantly improve detection rates compared to traditional methods. Furthermore, ensemble learning methods, which combine two plus multiple models to improve prediction accuracy, show greatness in handling the complexity and variability of big data sets especially when implemented by PySpark. The findings suggest that the enhancement of accuracy derives from newer software that’s designed to reflect reality. However, challenges remain in the deployment of these systems, including the need for large, high-quality datasets and the potential for adversarial attacks that attempt to deceive the ML models. Future research should continue to improve the robustness and efficiency of combining algorithms, as well as integrate them with existing security frameworks to provide comprehensive protection against DDoS attacks and other areas. The dataset was originally created by the University of New Brunswick to analyze DDoS data. The dataset itself was based on logs of the university’s servers, which found various DoS attacks throughout the publicly available period to totally generate 80 attributes with a 6.40GB size. In this dataset, the label and binary column become a very important portion of the final classification. In the last column, this means the normal traffic would be differentiated by the attack traffic. Further analysis is then ripe for investigation. Finally, malicious traffic alert software, as an example, should be trained on packet influx to Flow Duration dependence, which creates a mathematical scope for averages to enact. In achieving such high accuracy, the project acts as an illustration (referenced in the form of excerpts from my Google Colab account) of many attempts to tune. Cybersecurity advocates for more work on the character of brute-force attack traffic and normal traffic features overall since most of our investments as humans are digitally based in work, recreational, and social environments.展开更多
网络控制系统通过通信网络连接传感器、控制器和执行器,实现远程监控和智能控制,具有突破地域限制的优势,但其开放性和网络依赖性也引入了诸多问题。首先,系统分析了网络控制系统中存在的信号量化误差、数据包丢失、网络时延、带宽占用...网络控制系统通过通信网络连接传感器、控制器和执行器,实现远程监控和智能控制,具有突破地域限制的优势,但其开放性和网络依赖性也引入了诸多问题。首先,系统分析了网络控制系统中存在的信号量化误差、数据包丢失、网络时延、带宽占用和网络安全威胁等问题;其次,在回顾网络控制系统研究成果的基础上,提出了新的控制策略,包括新型量化控制、随机丢包控制、时变时延的自触发控制、变采样周期智能调度控制、动态事件触发控制、DoS(denial of service)攻击的网络控制等;再次,归纳了相关的控制理论方法,包括随机系统法、预测控制法、时延估算与补偿、模糊反馈法、神经网络预测法;最后,提出了网络控制系统研究在未来面临的挑战。展开更多
Underground mine fire always exists since the mining activity was practiced.It poses a severe safety hazard to the mine workers and may also cause a tremendous economic loss to the mines.Methods for controlling and ex...Underground mine fire always exists since the mining activity was practiced.It poses a severe safety hazard to the mine workers and may also cause a tremendous economic loss to the mines.Methods for controlling and extinguishing fires in underground mine have long been studied and there have been significant improvements.In order to know clearly about the firefighting technology used,this paper summarizes most of the underground mine firefighting methods used in the United States the past 150 years.This paper describes not only the accepted firefighting theories,but also the technologies,both direct and indirect attacking,in accordance to regulations or codes,with special attention is given to the indirect attack method and its related technologies.Further research needed is also briefly discussed at the end of this paper.展开更多
Three kinds of vulnerabilities that may exist in some of current virtualization-based security monitoring systems were proposed： page mapping problem,lack of overall protection,and inherent limitations. Aiming at the...Three kinds of vulnerabilities that may exist in some of current virtualization-based security monitoring systems were proposed： page mapping problem,lack of overall protection,and inherent limitations. Aiming at these vulnerabilities,relative attack methods were presented in detail. Our experiments show that the attack methods,such as page mapping attack,data attack,and non-behavior detection attack,can attack simulated or original security monitors successfully. Defenders,who need to effectively strengthen their security monitors,can get an inspiration from these attack methods and find some appropriate solutions.展开更多
随着化工行业朝着智能化方向发展,化工反应系统频繁遭受网络攻击,产生了严重的后果。现有工控安全仿真研究大多单独在网络领域进行,缺少针对化工反应系统的工控安全仿真技术。因此,针对该问题,以化工反应系统中常见的连续搅拌式反应釜(c...随着化工行业朝着智能化方向发展,化工反应系统频繁遭受网络攻击,产生了严重的后果。现有工控安全仿真研究大多单独在网络领域进行,缺少针对化工反应系统的工控安全仿真技术。因此,针对该问题,以化工反应系统中常见的连续搅拌式反应釜(continuous stirred tank reactor,CSTR)为研究对象,提出一种针对CSTR的工控安全虚实融合仿真技术。首先建立CSTR控制系统模型,提出了基于攻击类型分析、攻击仿真方法、响应分析方法与攻击监测和控制方法的工控安全虚实融合仿真技术框架,利用CSTR工控安全仿真平台,实现了针对CSTR系统的攻击模拟和响应分析,并验证了提出的针对网络攻击的监测和控制方法,为推动网络安全在化工领域内的研究提供了借鉴。展开更多
基金supported by the National Defense Foundation of China(No.403060103)
文摘This paper presents a computationally efficient real-time trajectory planning framework for typical unmanned combat aerial vehicle （UCAV） performing autonomous air-to-surface （A/S） attack. It combines the benefits of inverse dynamics optimization method and receding horizon optimal control technique. Firstly, the ground attack trajectory planning problem is mathematically formulated as a receding horizon optimal control problem （RHC-OCP）. In particular, an approximate elliptic launch acceptable region （LAR） model is proposed to model the critical weapon delivery constraints. Secondly, a planning algorithm based on inverse dynamics optimization, which has high computational efficiency and good convergence properties, is developed to solve the RHCOCP in real-time. Thirdly, in order to improve robustness and adaptivity in a dynamic and uncer- tain environment, a two-degree-of-freedom （2-DOF） receding horizon control architecture is introduced and a regular real-time update strategy is proposed as well, and the real-time feedback can be achieved and the not-converged situations can be handled. Finally, numerical simulations demon- strate the efficiency of this framework, and the results also show that the presented technique is well suited for real-time implementation in dynamic and uncertain environment.
文摘This paper provides a calculating method which can be used in calculation of the kill probability attack area for every AAM. At first, attack area of AAM and kill probability of every characteristic point are obtained by combining trajectory calculation with kill probability calculation. Then, coordinates of a fire point relative to standard kill probability value in terms of standardization method are found. At last, equivalent kill probability curve equations are formulated by means of curve fitting method.
文摘The use of machine learning algorithms to identify characteristics in Distributed Denial of Service (DDoS) attacks has emerged as a powerful approach in cybersecurity. DDoS attacks, which aim to overwhelm a network or service with a flood of malicious traffic, pose significant threats to online systems. Traditional methods of detection and mitigation often struggle to keep pace with the evolving nature of these attacks. Machine learning, with its ability to analyze vast amounts of data and recognize patterns, offers a robust solution to this challenge. The aim of the paper is to demonstrate the application of ensemble ML algorithms, namely the K-Means and the KNN, for a dual clustering mechanism when used with PySpark to collect 99% accurate data. The algorithms, when used together, identify distinctive features of DDoS attacks that prove a very accurate reflection of reality, so they are a good combination for this aim. Impressively, having preprocessed the data, both algorithms with the PySpark foundation enabled the achievement of 99% accuracy when tuned on the features of a DDoS big dataset. The semi-supervised dataset tabulates traffic anomalies in terms of packet size distribution in correlation to Flow Duration. By training the K-Means Clustering and then applying the KNN to the dataset, the algorithms learn to evaluate the character of activity to a greater degree by displaying density with ease. The study evaluates the effectiveness of the K-Means Clustering with the KNN as ensemble algorithms that adapt very well in detecting complex patterns. Ultimately, cross-reaching environmental results indicate that ML-based approaches significantly improve detection rates compared to traditional methods. Furthermore, ensemble learning methods, which combine two plus multiple models to improve prediction accuracy, show greatness in handling the complexity and variability of big data sets especially when implemented by PySpark. The findings suggest that the enhancement of accuracy derives from newer software that’s designed to reflect reality. However, challenges remain in the deployment of these systems, including the need for large, high-quality datasets and the potential for adversarial attacks that attempt to deceive the ML models. Future research should continue to improve the robustness and efficiency of combining algorithms, as well as integrate them with existing security frameworks to provide comprehensive protection against DDoS attacks and other areas. The dataset was originally created by the University of New Brunswick to analyze DDoS data. The dataset itself was based on logs of the university’s servers, which found various DoS attacks throughout the publicly available period to totally generate 80 attributes with a 6.40GB size. In this dataset, the label and binary column become a very important portion of the final classification. In the last column, this means the normal traffic would be differentiated by the attack traffic. Further analysis is then ripe for investigation. Finally, malicious traffic alert software, as an example, should be trained on packet influx to Flow Duration dependence, which creates a mathematical scope for averages to enact. In achieving such high accuracy, the project acts as an illustration (referenced in the form of excerpts from my Google Colab account) of many attempts to tune. Cybersecurity advocates for more work on the character of brute-force attack traffic and normal traffic features overall since most of our investments as humans are digitally based in work, recreational, and social environments.
文摘网络控制系统通过通信网络连接传感器、控制器和执行器,实现远程监控和智能控制,具有突破地域限制的优势,但其开放性和网络依赖性也引入了诸多问题。首先,系统分析了网络控制系统中存在的信号量化误差、数据包丢失、网络时延、带宽占用和网络安全威胁等问题;其次,在回顾网络控制系统研究成果的基础上,提出了新的控制策略,包括新型量化控制、随机丢包控制、时变时延的自触发控制、变采样周期智能调度控制、动态事件触发控制、DoS(denial of service)攻击的网络控制等;再次,归纳了相关的控制理论方法,包括随机系统法、预测控制法、时延估算与补偿、模糊反馈法、神经网络预测法;最后,提出了网络控制系统研究在未来面临的挑战。
文摘Underground mine fire always exists since the mining activity was practiced.It poses a severe safety hazard to the mine workers and may also cause a tremendous economic loss to the mines.Methods for controlling and extinguishing fires in underground mine have long been studied and there have been significant improvements.In order to know clearly about the firefighting technology used,this paper summarizes most of the underground mine firefighting methods used in the United States the past 150 years.This paper describes not only the accepted firefighting theories,but also the technologies,both direct and indirect attacking,in accordance to regulations or codes,with special attention is given to the indirect attack method and its related technologies.Further research needed is also briefly discussed at the end of this paper.
基金Supported by National 242 Plan Project(2005C48)the Technology Innovation Programme Major Projects of Beijing Institute of Technology(2011CX01015)
文摘Three kinds of vulnerabilities that may exist in some of current virtualization-based security monitoring systems were proposed： page mapping problem,lack of overall protection,and inherent limitations. Aiming at these vulnerabilities,relative attack methods were presented in detail. Our experiments show that the attack methods,such as page mapping attack,data attack,and non-behavior detection attack,can attack simulated or original security monitors successfully. Defenders,who need to effectively strengthen their security monitors,can get an inspiration from these attack methods and find some appropriate solutions.
文摘随着化工行业朝着智能化方向发展,化工反应系统频繁遭受网络攻击,产生了严重的后果。现有工控安全仿真研究大多单独在网络领域进行,缺少针对化工反应系统的工控安全仿真技术。因此,针对该问题,以化工反应系统中常见的连续搅拌式反应釜(continuous stirred tank reactor,CSTR)为研究对象,提出一种针对CSTR的工控安全虚实融合仿真技术。首先建立CSTR控制系统模型,提出了基于攻击类型分析、攻击仿真方法、响应分析方法与攻击监测和控制方法的工控安全虚实融合仿真技术框架,利用CSTR工控安全仿真平台,实现了针对CSTR系统的攻击模拟和响应分析,并验证了提出的针对网络攻击的监测和控制方法,为推动网络安全在化工领域内的研究提供了借鉴。
