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
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
开式转子发动机具备高效率、高燃油经济性的优点,然而开式转子叶片巨大的噪声辐射是亟待解决的问题。基于非定常涡格法、涡粒子法和Ffowcs-Williams and Hawkings方程,发展了不依赖于空间体网格的开式转子气动-噪声高效预测方法,引入三...开式转子发动机具备高效率、高燃油经济性的优点,然而开式转子叶片巨大的噪声辐射是亟待解决的问题。基于非定常涡格法、涡粒子法和Ffowcs-Williams and Hawkings方程,发展了不依赖于空间体网格的开式转子气动-噪声高效预测方法,引入三维普朗特-葛劳渥法则,进行可压缩性修正。针对NASA(National Aeronautics and Space Administration)SR-7A单排开式转子的起飞工况进行了验证计算,气动结果与NASA试验值和商业软件计算值的误差均小于1%,主要单音噪声与总声压级指向性结果预测误差可低至1 dB以内。在同等研究对象和硬件条件下,本文方法计算所需CPU核时低于通用URANS(Unsteady Reynolds-Averaged Navier-Stokes)计算所需的1%。考虑起飞迎角下的周向不均匀来流畸变,系统研究了SR-7A单排转子在0°~20°来流迎角下的气动参数和噪声特性。结果表明,随着来流迎角的增大,转子的时均推力、功率、效率呈现指数增长趋势,转子桨盘内载荷时均值呈现线性增长趋势。转子各项载荷的非定常波动幅值随着迎角的增大而增加。单个叶片的载荷波动峰谷值与运动相位相比存在17°~56°的相位滞后现象,这与噪声周向指向性结果中的偏转非常相关。在0°~20°迎角内,转子上方总声压级从107 dB降低至99 dB,转子下方位置总声压级从107 dB升高至114 dB。此外,来流迎角与桨盘的相互作用会额外导致最高达73 dB的轴频率单音噪声。针对单排转子的噪声源项解耦分析表明,来流迎角对非定常载荷噪声的增益作用非常显著。本文开发的方法可以用较低成本得到开式转子的非定常气动及噪声特性,并且具备多角度的噪声源解耦能力,为未来低噪声开式转子设计提供了有力工具。展开更多
In Industrial Control Systems (ICS), security issues are getting more and more attention. The number of hacking attacks per year is endless, and the attacks on industrial control systems are numerous. Programmable Log...In Industrial Control Systems (ICS), security issues are getting more and more attention. The number of hacking attacks per year is endless, and the attacks on industrial control systems are numerous. Programmable Logic Controller (PLC) is one of the main controllers of industrial processes. Since the industrial control system network is isolated from the external network, many people think that PLC is a safety device. However, virus attacks in recent years, such as Stuxnet, have confirmed the erroneousness of this idea. In this paper, we use the vulnerability of Siemens PLC to carry out a series of attacks, such as S7-200, S7-300, S7-400, S7-1200 and so on. We read the data from the PLC output and then rewrite the data and write it to the PLC. We tamper with the writing of data to achieve communication chaos. When we attack the primary station, all slave devices connected to the primary station will be in a state of communication confusion. The attack methods of us can cause delay or even loss of data in the communications from the Phasor Data Concentrator (PMU) to the data concentrator. The most important thing is that our attack method generates small traffic and short attack time, which is difficult to be identified by traditional detection methods.展开更多
A numerical investigation of the structure of the vortical flowfield over delta wings at high angles of attack in longitudinal and with small sideslip angle is presented. Three-dimensional Navier-Stokes numerical simu...A numerical investigation of the structure of the vortical flowfield over delta wings at high angles of attack in longitudinal and with small sideslip angle is presented. Three-dimensional Navier-Stokes numerical simulations were carried out to predict the complex leeward-side flowfield characteristics that are dominated by the effect of the breakdown of the leading-edge vortices. The methods that analyze the flowfield structure quantitatively were given by using flowfield data from the computational results. In the region before the vortex breakdown, the vortex axes are approximated as being straight line. As the angle of attack increases, the vortex axes are closer to the root chord, and farther away from the wing surface. Along the vortex axes, as the adverse pressure gradients occur, the axial velocity decreases, that is, A is negativee, so the vortex is unstable, and it is possible to breakdown. The occurrence of the breakdown results in the instability of lateral motion for a delta wing, and the lateral moment diverges after a small perturbation occurs at high angles of attack. However, after a critical angle of attack is reached the vortices breakdown completely at the wing apex, and the instability resulting from the vortex breakdown disappears.展开更多
This article discusses improvements in a puzzle authentication method that adopts the interface of the Puzzle and Dragons game [1] and is tolerant against video-recording attacks. A problem that the conventional puzzl...This article discusses improvements in a puzzle authentication method that adopts the interface of the Puzzle and Dragons game [1] and is tolerant against video-recording attacks. A problem that the conventional puzzle authentication methods face is that they are time consuming and have low success rate in authentication. We evaluated improvements of the interface to verify the usability of the improved system. The results suggested that the usability in terms of operation time and authentication success rate attained a level that was comparable with other leading methods in the field.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
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.
文摘开式转子发动机具备高效率、高燃油经济性的优点,然而开式转子叶片巨大的噪声辐射是亟待解决的问题。基于非定常涡格法、涡粒子法和Ffowcs-Williams and Hawkings方程,发展了不依赖于空间体网格的开式转子气动-噪声高效预测方法,引入三维普朗特-葛劳渥法则,进行可压缩性修正。针对NASA(National Aeronautics and Space Administration)SR-7A单排开式转子的起飞工况进行了验证计算,气动结果与NASA试验值和商业软件计算值的误差均小于1%,主要单音噪声与总声压级指向性结果预测误差可低至1 dB以内。在同等研究对象和硬件条件下,本文方法计算所需CPU核时低于通用URANS(Unsteady Reynolds-Averaged Navier-Stokes)计算所需的1%。考虑起飞迎角下的周向不均匀来流畸变,系统研究了SR-7A单排转子在0°~20°来流迎角下的气动参数和噪声特性。结果表明,随着来流迎角的增大,转子的时均推力、功率、效率呈现指数增长趋势,转子桨盘内载荷时均值呈现线性增长趋势。转子各项载荷的非定常波动幅值随着迎角的增大而增加。单个叶片的载荷波动峰谷值与运动相位相比存在17°~56°的相位滞后现象,这与噪声周向指向性结果中的偏转非常相关。在0°~20°迎角内,转子上方总声压级从107 dB降低至99 dB,转子下方位置总声压级从107 dB升高至114 dB。此外,来流迎角与桨盘的相互作用会额外导致最高达73 dB的轴频率单音噪声。针对单排转子的噪声源项解耦分析表明,来流迎角对非定常载荷噪声的增益作用非常显著。本文开发的方法可以用较低成本得到开式转子的非定常气动及噪声特性,并且具备多角度的噪声源解耦能力,为未来低噪声开式转子设计提供了有力工具。
文摘In Industrial Control Systems (ICS), security issues are getting more and more attention. The number of hacking attacks per year is endless, and the attacks on industrial control systems are numerous. Programmable Logic Controller (PLC) is one of the main controllers of industrial processes. Since the industrial control system network is isolated from the external network, many people think that PLC is a safety device. However, virus attacks in recent years, such as Stuxnet, have confirmed the erroneousness of this idea. In this paper, we use the vulnerability of Siemens PLC to carry out a series of attacks, such as S7-200, S7-300, S7-400, S7-1200 and so on. We read the data from the PLC output and then rewrite the data and write it to the PLC. We tamper with the writing of data to achieve communication chaos. When we attack the primary station, all slave devices connected to the primary station will be in a state of communication confusion. The attack methods of us can cause delay or even loss of data in the communications from the Phasor Data Concentrator (PMU) to the data concentrator. The most important thing is that our attack method generates small traffic and short attack time, which is difficult to be identified by traditional detection methods.
基金Project supported by the Foundation of Aeronautical Science (No.99A53001)
文摘A numerical investigation of the structure of the vortical flowfield over delta wings at high angles of attack in longitudinal and with small sideslip angle is presented. Three-dimensional Navier-Stokes numerical simulations were carried out to predict the complex leeward-side flowfield characteristics that are dominated by the effect of the breakdown of the leading-edge vortices. The methods that analyze the flowfield structure quantitatively were given by using flowfield data from the computational results. In the region before the vortex breakdown, the vortex axes are approximated as being straight line. As the angle of attack increases, the vortex axes are closer to the root chord, and farther away from the wing surface. Along the vortex axes, as the adverse pressure gradients occur, the axial velocity decreases, that is, A is negativee, so the vortex is unstable, and it is possible to breakdown. The occurrence of the breakdown results in the instability of lateral motion for a delta wing, and the lateral moment diverges after a small perturbation occurs at high angles of attack. However, after a critical angle of attack is reached the vortices breakdown completely at the wing apex, and the instability resulting from the vortex breakdown disappears.
文摘This article discusses improvements in a puzzle authentication method that adopts the interface of the Puzzle and Dragons game [1] and is tolerant against video-recording attacks. A problem that the conventional puzzle authentication methods face is that they are time consuming and have low success rate in authentication. We evaluated improvements of the interface to verify the usability of the improved system. The results suggested that the usability in terms of operation time and authentication success rate attained a level that was comparable with other leading methods in the field.
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.
