欺骗网络体系的技术分析与实现模型

欺骗主机和欺骗网络作为一种网络安全资源，其安全价值在于被人们扫描、攻击和入侵时，通过创建一个高度可控的黑客攻击环境，从而捕获尽可能多的同入侵有关的信息。基于这些信息，获得互联网所面临的安全风险。本文在分析了它的实现方式技术基础上，形式化的定义了入侵诱骗系统，提出了入侵诱骗的体系结构，并给出了一个入侵诱骗系统的实现模型。     

网络攻击追踪方法的技术分析与实现模型

本文分析了IP地址追踪方法,结合现有的入侵检测技术提出了网络攻击源追踪系统的模型,阐述了该系统的体系结构和各部分的主要功能,给出了利用相关性分析对攻击者的攻击路径进行回溯的基本思想,对网络安全管理具有一定的借鉴意义。     

智能视频内容分析技术及功能应用

智能视觉分析技术的监控跟踪系统，应具有入侵检测、非法滞留检测、遗弃物检测、物品搬移检测和自动PTZ跟踪等功能。     

基于特征融合的机动车号牌识别

车辆号牌识别是城市道路交通管理中的重要技术,本文首先阐述了机动车号牌识别的基本流程,分析了卷积神经网络、方向梯度直方图特征以及局部二值模式特征提取方法。基于卷积神经网络,融合两种特征提取方法,给出了基于特征融合构造卷积神经网络输入端的号牌识别方法,分别通过与"方向梯度直方图特征+卷积神经网络"、"局部二值模式特征+卷积神经网络"方法的实验对比,证明了该方法能有效地提高部分字符的识别率,验证了所提出方法的有效性。     

基于GA-SVM的大型公共活动拥挤踩踏事故风险分析

大型公共活动拥挤踩踏事故诱因错综复杂,为预先获取其危险程度,本文借鉴前人研究成果,从人、物、环、管理四个方面,建立明确指标风险分级标准的评估指标体系,进而在此基础上引入遗传算法(GA)优化的支持向量机(SVM)方法,构建基于GA-SVM的大型公共活动拥挤踩踏事故风险评估模型。应用建成的风险评估模型,对实例进行评估,结果表明,该模型评估结果精确度较高,在风险评估中有较好的适用性和可靠性。     

泰科提出轨道交通多级安防管理措施

<正>泰科集成安防运用在全球轨道交通领域的丰富经验,提出多层级防护措施:外部入侵检测外部入侵保护是为了防止犯罪分子携带危险物品接近地铁站,并从非安检区、通风口、地铁隧道两端等非法闯入,在这些重点位置安装入侵检测系统。这     

夜间红外行人图像在可见光行人图像库的检索算法

本文旨在弥补夜间红外模式监控下行人图像与白天监控下行人图像间的模态差异和异构特性，较好实现多摄像头下红外人像与可见光人像数据间的检索和比对任务。设计一种局部参数共享的双路卷积神经网络模型，提取具有全局粗粒度和局部细粒度的两种人像特征，通过对两种特征分别进行特征度量，实现跨模态人像检索模型的训练和优化。结果表明，模型所提取的两种特征在测试数据集上具有较好的应用效果，准确率与目前主流算法相比具有竞争力。本研究能够服务视频侦查工作，提高现有的动态、静态人像比对的应用水平。     

交通流缺失数据的修补技术探究

城市道路交通中交通检测器获得的数据往往不完整,存在丢失现象,需要对其进行修补,以保证交通流预测模型的实际应用精度。以离散和连续缺失的线圈检测器交通流量数据为研究对象,提出一种基于最小二乘支持向量机(Least Squares Support Vector Machines,LS-SVM)的交通流时间序列数据修补模型,并将其结果与利用RBF神经网络模型和一元非线性回归模型的结果进行比较。研究结果表明,LS-SVM模型修补精度优于RBF(Radical Basis Function)神经网络模型和非线性回归模型。最后,针对历史数据缺失难以构建LS-SVM模型的问题,提出了两阶段故障数据修补组合模型,取得了好的效果。     

基于遗传算法的反恐风险评估指标体系应用研究

当前,如何进行风险评估已经成为反恐工作的重要内容,对恐怖主义的风险展开研究可以提高反恐能力。本文充分考虑了恐怖活动威胁能力、威胁措施、威胁意图与威胁时间的规律及特点,综合现有指标体系的优缺点,建立了4个一级指标和10个二级指标的反恐风险评估指标体系。构建了基于遗传算法的反恐风险评估指标体系模型。通过仿真实验,结合样本对该模型进行了运算、验证与分析。结果表明,本文建立的反恐风险评估指标体系与模型是有效可行的、准确的,并与当前反恐形势和重点相符,对实际工作有一定的参考价值和指导意义。     

双三鉴称雄 主被动红外势优

入侵探测器是用来探测入侵者的移动或其他行为的电子及机械部件所组成的装置，入侵探测器归属于安防产业的防盗报警行业，其系统极其庞杂，主要有主动红外对射入侵探测器。被动红外幕帘入侵探测器、微波入侵探测器、微波和被动红外复合入侵探测器、超声波入侵探测器、振动入侵探测器、磁簧开关入侵探测器、超声和被动红外复合入侵探测器等。由于版面所限，我们仅从主／被动入侵探测器方面对该类产品作个详细论述。[编者按]     

Intrusion detection: issues and challenges in evidence acquisition

As the dangers of hacking and cyber‐warfare for network security become a reality, the need to be able to generate legally admissible evidence of criminal or other illegal online behaviours has become increasingly important. While technical systems providing intrusion detection and network monitoring are constantly being improved, the security they provide is never absolute. As a result, when assessing the value and nature of the data that these systems produce, it becomes critical to be aware of a number of factors: these systems themselves are susceptible to attack and/or evasion; these systems may collect only a partial data set; and, these data sets may themselves be flawed, erroneous or may already have been tampered with. Additionally, the issue of privacy and data protection is emerging as a central debate in forensic computing research. In this context, this paper examines intrusion detection systems (IDS) and provides the results of a case study on the use of the SNORT IDS on a university department World Wide Web (WWW) server. The case study is analysed and discussed using a forensic computing perspective. This perspective considers the nature of the intrusion detection and network monitoring security provided and evaluates the system in terms of its evidence acquisition (‘forensic’) capabilities and the legal admissibility of the digital evidence generated.     

The image-database REBEZO for shoeprints with developments on automatic classification of shoe outsole designs

A database for footwear outsole designs has been developed on a PC. The database consists of three files: shoes of suspects, shoeprints from the scene of crime and shoes available from the shops. For REBEZO an algorithm is implemented for the automatic classification of outsole patterns. The algorithm first segments the shoeprofiles in different profiles. The Fourier-features are calculated for those profiles. The best Fourier features are selected and are classified with a neural network. By using this algorithm many different shapes can be recognised. Integrating the results of the invariant moments in the neural network will give better results.     

Optimization of the separation of organic explosives by capillary electrophoresis with artificial neural networks

The separation of 12 explosives by capillary electrophoresis was optimized with the aid of artificial neural networks (ANNs). The selectivity of the separation was manipulated by varying the concentration of sodium dodecyl sulfate (SDS) and the pH of the electrolyte, while maintaining the buffer concentration at 10 mM borate. The concentration of SDS and the electrolyte pH were used as input variables and the mobility of the explosives were used as output variables for the ANN. In total, eight experiments were performed based on a factorial design to train a variety of artificial neural network architectures. A further three experiments were required to train ANN architectures to adequately model the experimental space. A product resolution response surface was constructed based on the predicted mobilities of the best performing ANN. This response surface pointed to two optima; pH 9.0-9.1 and 60-65 mM SDS, and pH 8.4-8.6 and 50-60 mM SDS. Separation of all 12 explosives was achieved at the second optimum. The separation was further improved by changing the capillary to an extended cell detection window and reducing the diameter of the capillary from 75 microm to 50 microm. This provided a more efficient separation without compromising detection sensitivity.     

Deep learning for sex determination: Analyzing over 200,000 panoramic radiographs

The objective of this study is to assess the performance of an innovative AI-powered tool for sex determination using panoramic radiographs (PR) and to explore factors affecting the performance of the convolutional neural network (CNN). The study involved 207,946 panoramic dental X-rays and their corresponding reports from 15 clinical centers in São Paulo, Brazil. The PRs were acquired with four different devices, and 58% of the patients were female. Data preprocessing included anonymizing the exams, extracting pertinent information from the reports, such as sex, age, type of dentition, and number of missing teeth, and organizing the data into a PostgreSQL database. Two neural network architectures, a standard CNN and a ResNet, were utilized for sex classification, with both undergoing hyperparameter tuning and cross-validation to ensure optimal performance. The CNN model achieved 95.02% accuracy in sex estimation, with image resolution being a significant influencing factor. The ResNet model attained over 86% accuracy in subjects older than 6 years and over 96% in those over 16 years. The algorithm performed better on female images, and the area under the curve (AUC) exceeded 96% for most age groups, except the youngest. Accuracy values were also assessed for different dentition types (deciduous, mixed, and permanent) and missing teeth. This study demonstrates the effectiveness of an AI-driven tool for sex determination using PR and emphasizes the role of image resolution, age, and sex in determining the algorithm's performance.     

Using shortest path to discover criminal community

Extracting communities using existing community detection algorithms yields dense sub-networks that are difficult to analyse. Extracting a smaller sample that embodies the relationships of a list of suspects is an important part of the beginning of an investigation. In this paper, we present the efficacy of our shortest paths network search algorithm (SPNSA) that begins with an ‘algorithm feed’, a small subset of nodes of particular interest, and builds an investigative sub-network. The algorithm feed may consist of known criminals or suspects, or persons of influence. This sets our approach apart from existing community detection algorithms. We apply the SPNSA on the Enron Dataset of e-mail communications starting with those convicted of money laundering in relation to the collapse of Enron as the algorithm feed. The algorithm produces sparse and small sub-networks that could feasibly identify a list of persons and relationships to be further investigated. In contrast, we show that identifying sub-networks of interest using either existing community detection algorithms or a k-Neighbourhood approach produces sub-networks of much larger size and complexity. When the 18 top managers of Enron were used as the algorithm feed, the resulting sub-network identified 4 convicted criminals that were not managers and so not part of the algorithm feed. We directly validate the SPNSA by removing one of the convicted criminals from the algorithm feed and re-running the algorithm; in 5 out of 9 cases the left out criminal occurred in the resulting sub-network.     

基于深度卷积神经网络的语音降噪研究

目的为了提高实际工作中获取到的音频资料中语音的质量,降低噪声对语音质量及可懂度的影响,提出了一种基于深度卷积神经网络的语音降噪模型。方法该模型通过卷积、加偏置、批量归一化、Relu激活的多层循环结构,能够有效地对低信噪比条件下语音中的洗衣机噪声、鼓掌噪声、汽车内部噪声等多种常见的环境噪声进行降噪处理。结果最终含噪语音经过模型处理后的MOS评分达到3.91分,其中最高分4.05分,最低分3.81分。结论该模型能够切实提高含噪语音的质量及可懂度,对于实际的公安工作、智慧警务建设、语音分析、语音文本识别等具有重要的意义和价值。     

Estimation of bullet striation similarity using neural networks

A new method that searches for similar striation patterns using neural networks is described. Neural networks have been developed based on the human brain, which is good at pattern recognition. Therefore, neural networks would be expected to be effective in identifying striated toolmarks on bullets. The neural networks used in this study deal with binary signals derived from striation images. This signal plays a significant role in identification, because this signal is the key to the individually of the striations. The neural network searches a database for similar striations by means of these binary signals. The neural network used here is a multilayer network consisting of 96 neurons in the input layer, 15 neurons in the middle, and one neuron in the output layer. Two signals are inputted into the network and a score is estimated based on the similarity of these signals. For this purpose, the network is assigned to a previous learning. To initially test the validity of the procedure, the network identifies artificial patterns that are randomly produced on a personal computer. The results were acceptable and showed robustness for the deformation of patterns. Moreover, with ten unidentified bullets and ten database bullets, the network consistently was able to select the correct pair.     

刑事侦查中网络反腐的思考

随着互联网的飞速发展,网络技术不断融入到反腐的全球浪潮之中,并发挥了超乎想象的作用力和影响力,形成了一股强大的有技术支撑的公众反腐力量。但由于普通公众和侦查机关对于腐败的理解有差别,形成了腐败行为与腐败犯罪之分,如何能够将日趋高涨的网络反腐民间力量与检察机关专门反腐机构有效的结合起来就成为亟待处理的难题,然而不容置疑的是,如果民间反腐与官方反腐、权力反腐与权利反腐两股力量能实现有效结合,我国反腐斗争将会获得实质性进展。     

基于深度学习的人体肋骨骨折智能检测技术

目的将人工智能中的深度学习技术应用到人体肋骨骨折识别,实现人体肋骨骨折智能检测,提高法医肋骨骨折诊断效率。方法采集3143例人体胸部X线数字影像(2602例用于训练,541例用于测试),标注肋骨骨折特征点,通过多层网络堆叠,分层、分级主动学习原始数据高度抽象的特征表述,并将此特征反馈至检测器进行骨折检测,输出骨折位置及相应置信度。结果基于深度学习的人体肋骨骨折检测准确率在90%以上。结论基于深度学习的人体肋骨骨折检测准确率较高,可用于辅助法医进行肋骨骨折识别诊断、检验鉴定等,本研究可为人体其他部位骨骼损伤智能检测提供参考。     

Neural networks in forensic science.

Neural networks were developed to study and mimic the functioning of the human brain. Humans are good at pattern recognition; the question is how good neural networks are at it, particularly with problems of forensic science interest. Simulation experiments with a type of neural network known as a Hopfield net indicate that it may have value for the storage of toolmark patterns (including bullet striation patterns) and for the subsequent retrieval of the matching pattern using another mark by the same tool for input. Another type of neural network, the back-propagation network (BPN), is useful for applications similar to those for which standard statistical methods of pattern classification can be used. This would be an appropriate approach to the matching of general component patterns, such as gas chromatograms of gasoline, or pyrolysis patterns from materials of forensic science interest, such as paint. The BPN may provide better results than statistical methods, but it is currently necessary to try both to determine which would be best for any given situation.