On measurement of operational security

Ideally, a measure of the security of a system should capture quantitatively the intuitive notion of “the ability of the system to resist attack.” That is, it should be operational, reflecting the degree to which the system can be expected to remain free of security breaches under particular conditions of operation (including attack). Instead, current security levels at best merely reflect the extensiveness of safeguards introduced during the design and development of a system. Whilst we might expect a system developed to a higher level than another to exhibit “more secure behavior” in operation, this cannot be guaranteed; more particularly, we cannot infer what the actual security behavior will be from knowledge of such a level. In the paper we discuss similarities between reliability and security with the intention of working toward measures of “operational security” similar to those that we have for reliability of systems. Very informally, these measures could involve expressions such as the rate of occurrence of security breaches, or the probability that a specified “mission” can be accomplished without a security breach. This new approach is based on the analogy between system failure and security breach, but it raises several issues which invite empirical investigation. We briefly describe a pilot experiment that we have conducted to judge the feasibility of collecting data to examine these issues     

基于条件随机场的实时入侵检测系统框架实现

入侵检测系统（IDS）如今是网络的重要组成部分,现在各种无线网络及专用网络都已配备检测系统。随着网络技术的迅猛发展,入侵检测的技术已经从简单的签名匹配发展成能充分利用上下文信息的基于异常和混合的检测方式。为了从网络环境大量记录信息中正确有效地识别出入侵,提出一种基于层叠条件随机场模型的入侵检测框架,该框架针对4类不同攻...     

全向攻击空空导弹的杀伤概率攻击区计算方法

 本文首先应用弹道计算和杀伤概率计算的方法,得到了空空导弹的全向攻击区和攻击区内各特征点上的导弹杀伤概率值,再由规范化处理求得各个规范化杀伤概率所对应的发射点坐标,最后通过选择性的曲线拟合方法获得全向攻击区内的等杀伤概率曲线方程。文中的方法和数学模型适用于各种空空导弹。     

机体结构AE信号的统计分析研究

对机体结构声发射数据的表征参数进行分析,建立了数据处理的统计模型,并运用该模型分析了某型飞机全机疲劳试验和某现役飞机随机机载声发射监测数据,提出了结构损伤判据,可供实际应用。     

HB-2 high-velocity correlation model at high angles of attack in supersonic wind tunnel tests

Responding to a need for experimental data on a standard wind tunnel model at high angles of attack in the supersonic speed range, and in the absence of suitable reference data, a series of tests of two HB-2 standard models of different sizes was performed in the T-38 trisonic wind tunnel of Vojnotehnickˇi Institut(VTI), in the Mach number range 1.5–4.0, at angles of attack up to+30°. Tests were performed at relatively high Reynolds numbers of 2.2 millions to 4.5 millions(based on model forebody diameter). Results were compared with available low angle of attack data from other facilities, and, as a good agreement was found, it was assumed that, by implication, the obtained high angle of attack results were valid as well. Therefore, the results can be used as a reference database for the HB-2 model at high angles of attack in the supersonic speed range, which was not available before. The results are presented in comparison with available reference data, but also contain data for some Mach numbers not given in other publications.     

Fence vibrations [intruder detection]

Many Perimeter Intrusion Detection Systems (PIDS) use microphonic sensors mounted on fences to detect intruder attacks. Vibrations in the fence are detected and analysed to determine whether they represent an intrusion attempt. The signals from the fence are processed to filter out as many false alarms as possible. It is important that the output from the sensors is linear along the fence, to ensure that there is no point on the perimeter less sensitive to intruder attack. The investigation reported in this paper aims to discover whether the performance of acoustic detection systems could be improved through research into fence acoustics. Using models of fence vibrations, sensors could be positioned to obtain a more linear response. In addition, through knowledge of the fence vibrations produced by both real attacks and the typical causes of false alarms, a system could be designed which could better distinguish between them, giving a lower false alarm rate and increased detection performance. Characteristic profiles were found for the different forms of attack to the fence. Although the investigation is still in its early stages, these attack profiles suggest that a new type of detection system could be designed based on profile recognition     

Distributed sensor data fusion with binary decision trees

A distributed sensor object recognition scheme that uses object features collected by several sensors is presented. Recognition is performed by a binary decision tree generated from a training set. The scheme does not assume the availability of any probability density functions, thus it is practical for nonparametric object recognition. Simulations have been performed for Gaussian feature objects, and some of the results are presented     

高速风洞试验模型姿态角的视频测量及不确定度研究

高速风洞测力试验中,阻力系数精度σc x的先进指标为0.0001,按误差分配原理,要求试验模型迎角的测量精度σα≤0.01°。为此,研究风洞试验中模型姿态角视频测量及其不确定度,给出其系统误差的补偿方法。实测数据（马赫数为1.5、2.0、3.0和4.0）表明：在2m暂冲式超声速风洞试验中,各阶梯迎角测量数据的标准差（含风洞气流脉动致模型姿态角振动产生的误差）在0.0018°和0.0094°之间,迎角实测估计值的标准不确定度≤0.003°,由此可知,姿态角视频测量系统的σα≤0.0094°。本方法既不破坏模型的外形,又不改变模型的刚度与强度,具有实用价值。     

Spatially distributed target detection in non-Gaussian clutter

Two detection schemes for the detection of a spatially distributed, Doppler-shifted target in non-Gaussian clutter are developed. The non-Gaussian clutter is modeled as a spherically invariant random vector (SIRV) distribution. For the first detector, called the non-scatterer density dependent generalized likelihood ratio test (NSDD-GLRT), the detector takes the form of a sum of logarithms of identical functions of data from each individual range cell. It is shown under the clutter only hypothesis, that the detection statistic has the chi-square distribution so that the detector threshold is easily calculated for a given probability of false alarm P_F. The detection probability P_D is shown to be only a function of the signal-to-clutter power ratio (S/C)_opt of the matched filter, the number of pulses N, the number of target range resolution cells J, the spikiness of the clutter determined by a parameter of an assumed underlying mixing distribution, and P_F. For representative examples, it is shown that as N, J, or the clutter spikiness increases, detection performance improves. A second detector is developed which incorporates a priori knowledge of the spatial scatterer density. This detector is called the scatterer density dependent GLRT (SDD-GLRT) and is shown for a representative case to improve significantly the detection performance of a sparsely distributed target relative to the performance of the NSDD-GLRT and to be robust for a moderate mismatch of the expected number of scatterers. For both the NSDD-GLRT and SDD-GLRT, the detectors have the constant false-alarm rate (CFAR) property that P_F is independent of the underlying mixing distribution of the clutter, the clutter covariance matrix, and the steering vector of the desired signal     

Optimal distributed decision fusion

The problem of decision fusion in distributed sensor systems is considered. Distributed sensors pass their decisions about the same hypothesis to a fusion center that combines them into a final decision. Assuming that the sensor decisions are independent of each other for each hypothesis, the authors provide a general proof that the optimal decision scheme that maximizes the probability of detection at the fusion for fixed false alarm probability consists of a Neyman-Pearson test (or a randomized N-P test) at the fusion and likelihood-ratio tests at the sensors     

基于落角约束的超声速导弹制导方案研究

针对超声速导弹具有落角约束条件下的制导问题,提出了一种新的设计方案.首先,在中制导俯冲过程中,采用攻击预先设定的虚拟目标方案,达到期望的中制导末端性能指标;然后,开始末制导俯冲段,雷达开机实时攻击实际目标,使导弹在需用过载和导引头框架角约束条件下,以期望落角命中目标.仿真结果表明,该制导律具有期望的性能要求,易于工程实现.     

改进的MMPHD机动目标跟踪方法

基于序列蒙特卡罗方法的经典多模概率假设密度滤波方法及其各种衍生方法,在预测过程中依据多个并行的状态转移模型,通过将大量粒子散布到下一时刻目标所有可能出现的状态空间实现目标状态的捕获,造成计算量大、目标跟踪精度差。为此,提出一种改进的多模粒子概率假设密度机动目标跟踪方法。该方法利用最新量测信息估计目标运动模型概率及模型参数,并将估计得到的目标模型应用到粒子概率假设密度滤波方法的预测过程中生成预测粒子,从而将大部分粒子聚合在目标最可能出现的状态空间邻域中,实现粒子的有效利用。数值仿真表明,所提方法不仅显著地减少了目标丢失个数,而且提高了目标跟踪精度。     

结冰条件下人-机-环系统的飞行风险概率

以结冰条件下的飞行风险量化概率为研究对象,基于蒙特卡罗飞行仿真实验对结冰条件下人-机-环系统的耦合特性进行了分析,并获取了飞行参数极值样本。构建了飞行风险发生的判定条件;对飞行参数极值样本进行了统计特性分析,验证了其厚尾分布特征。一维分布类型辨识结果表明广义极值分布对相对速度和迎角极值的描述精度最高。为描述二维变量对相关性的各自影响程度,提出了一种新的双参数变权重Copula模型;辨识结果表明该Copula模型能以较高的精度通过假设检验。相关性分析的结果表明相对速度和迎角同时出现极大值和极小值的概率较大。基于二维极值样本的Copula分布模型求出了不同结冰程度下的飞行风险概率值,探讨了飞行风险的非线性增长趋势。     

Integrated track maintenance for the PMHT via the hysteresis model

Unlike other tracking algorithms the probabilistic multi-hypothesis tracker (PMHT) assumes that the true source of each measurement is an independent realisation of a random process. Given knowledge of the prior probability of this assignment variable, data association is performed independently for each measurement. When the assignment prior is unknown, it can be estimated provided that it is either time independent, or fixed over the batch. This paper presents a new extension of the PMHT, which incorporates a randomly evolving Bayesian hyperparameter for the assignment process. This extension is referred to as the PMHT with hysteresis. The state of the hyperparameter reflects each model's contribution to the mixture, and thus can be used to quantify the significance of mixture components. The paper demonstrates how this can be used as a method for automated track maintenance in clutter. The performance benefit gained over the standard PMHT is demonstrated using simulations and real sensor data     

Data Association and Track Management for the Gaussian Mixture Probability Hypothesis Density Filter

The Gaussian mixture probability hypothesis density (GM-PHD) recursion is a closed-form solution to the probability hypothesis density (PHD) recursion, which was proposed for jointly estimating the time-varying number of targets and their states from a sequence of noisy measurement sets in the presence of data association uncertainty, clutter, and miss-detection. However the GM-PHD filter does not provide identities of individual target state estimates, that are needed to construct tracks of individual targets. In this paper, we propose a new multi-target tracker based on the GM-PHD filter, which gives the association amongst state estimates of targets over time and provides track labels. Various issues regarding initiating, propagating and terminating tracks are discussed. Furthermore, we also propose a technique for resolving identities of targets in close proximity, which the PHD filter is unable to do on its own.     

A Real-Time Statistical Radar Target Model

Radar glint arises from the spatial phase perturbations of the radar signal echoed from a complex target. The glint phenomenon is closely related to the target radar cross section (RCS). This relationship plays a significant part in modern missile seeker signal processing. We present a statistical glint/RCS target model for realtime simulation of target signatures. Particular emphasis is placed upon the modeling and simulation of the appropriate glint/RCS statistical dependency. The fundamental approximation of locating uniformly distributed scatterers around the instantaneous radar centroid employed in the Delano-Gubonin [1, 2, 3] model is removed. A key result which follows from this representation is that the mean glint estimator is unbiased. This enables the estimation of model parameters from the first-order glint and RCS statistics which can easily be computed from measured data. A method of estimating model parameters is presented, and the results are applied to data from a typical combat aircraft target. It is shown that the Delano-Gubonin results are a special case of the results presented here. The 14.6 percent probability of glint falling beyond the target extent as derived by Delano [1] is not true in general. It is further shown that glint and RCS are uncorrelated but are statistically dependent. A Monte-Carlo simulation is performed to verify the assumptions made and to demonstrate the feasibility of the working models.     

Statistical tests for higher order analysis of radar clutter: their application to L-band measured data

We design three statistical tests to ascertain whether radar data comply with the hypotheses of multivariate Gaussianity, spatial homogeneity, and covariance persymmetry, respectively. For the first issue we develop a statistical procedure based on quadratic distributional distances, which exploits the representation of Gaussian vectors in generalized spherical coordinates. As to the spatial homogeneity we propose a technique, based on the Kolmogorov-Smirnov (KS) test, relying on the properties of quadratic forms constructed from Gaussian vectors and Wishart distributed matrices. Finally, in order to analyze the persymmetry property of the disturbance covariance matrix, we design a testing procedure based on the generalized likelihood ratio test (GLRT). We thus apply the new tests to L-band experimentally measured clutter data, collected by the MIT Lincoln Laboratory Phase One radar, at the Katahdin Hill site. The results show that the multivariate Gaussian hypothesis for the considered data file is reasonable. On the contrary the assumption of spatial homogeneity can be done only within small clutter regions which, in general, exhibit also a persymmetric covariance matrix.     

Track-before-detect for Infrared Maneuvering Dim Multi-target via MM-PHD

In this paper, we present a novel and efficient track-before-detect (TBD) algorithm based on multiple-model probability hypothesis density (MM-PHD) for tracking infrared maneuvering dim multi-target. Firstly, the standard sequential Monte Carlo probability hypothesis density (SMC-PHD) TBD-based algorithm is introduced and sequentially improved by the adaptive process noise and the importance re-sampling on particle likelihood, which result in the improvement in the algorithm robustness and convergence speed. Secondly, backward recursion of SMC-PHD is derived in order to ameliorate the tracking performance especially at the time of the multi-target arising. Finally, SMC-PHD is extended with multiple-model to track maneuvering dim multi-target. Extensive experiments have proved the efficiency of the presented algorithm in tracking infrared maneuvering dim multi-target, which produces better performance in track detection and tracking than other TBD-based algorithms including SMC-PHD, multiple-model particle filter (MM-PF), histogram probability multi-hypothesis tracking (H-PMHT) and Viterbi-like.     

基于BiGRU-SVDD的ADS-B异常数据检测模型

广播式自动相关监视（ADS-B）作为新一代空管监视技术,由于采用明文方式广播发送数据,因而存在易遭受网络攻击的安全问题。为了准确检测ADS-B数据攻击行为,在充分考虑时间相关性的基础上,提出了针对ADS-B数据的异常数据检测模型。首先利用双向门控循环单元（BiGRU）神经网络预测ADS-B数据,得到了ADS-B数据预测值。再将预测值和实际值作差,将差值放入支持向量数据描述（SVDD）训练,得到了能检测ADS-B异常数据的超球体分类器。并且,选择了合适的滑动窗口,在保证异常检测准确率的同时,缩短BiGRU神经网络的训练时长。实验结果表明,BiGRU-SVDD模型能检测出随机位置偏移攻击、高度偏差攻击、重放攻击、拒绝服务（DOS）等攻击下的ADS-B异常数据。并且,与其他机器学习和深度学习方法相比,BiGRU-SVDD异常检测模型的准确率更佳,适应性更优。     

Novel data association schemes for the probability hypothesis density filter

The probability hypothesis density (PHD) filter is a practical alternative to the optimal Bayesian multi-target Alter based on finite set statistics. It propagates the PHD function, a first-order moment of the full multi-target posterior density. The peaks of the PHD function give estimates of target states. However, the PHD filter keeps no record of target identities and hence does not produce track-valued estimates of individual targets. We propose two different schemes according to which PHD filter can provide track-valued estimates of individual targets. Both schemes use the probabilistic data-association functionality albeit in different ways. In the first scheme, the outputs of the PHD filter are partitioned into tracks by performing track-to-estimate association. The second scheme uses the PHD filter as a clutter filter to eliminate some of the clutter from the measurement set before it is subjected to existing data association techniques. In both schemes, the PHD filter effectively reduces the size of the data that would be subject to data association. We consider the use of multiple hypothesis tracking (MHT) for the purpose of data association. The performance of the proposed schemes are discussed and compared with that of MHT.