基于非均衡数据层次学习的案件案由预测方法

案件案由是对案件所涉及法律关系性质的描述,科学、完善的案由设置有利于正确适用法律,是人民法院实行案件分类管理的重要途径.案件案由预测技术指基于案件案情的文本描述由计算机自动给出案件所属类别.在案件属性预测研究中,由于低频类别的样本数量较少且难以学习相关特征,因此已有方法在数据处理部分通常会对低频类别样本进行剔除.然而,在案件案由预测问题中,关键的挑战正是如何对属于低频案由的案件做出准确预测.为此,文中提出了一种基于非均衡数据层次学习的案件案由预测方法.在案件案由预测中,根据案由层次结构将案由划分为一级案由和二级案由,二级案由中的大量尾部类别被汇聚成上层样本数较多的大类,进而通过层次学习的方式来实现二级案由的预测,使二级案由有一级案由的信息支撑.最后,引入调整数据不平衡的损失函数来实现案件案由的预测.实验结果表明,所提方法整体优于对比方法,其平均精确率比现有方法提高了4.81％,这表明通过层次学习和引入非均衡数据损失函数能较好地解决案件案由预测问题.     

关于存储原理与数据恢复课程的建设与思考

从当前公安侦查专业计算机犯罪侦查方向学生的培养要求出发,对"存储原理与数据恢复"课程的培养目标、教学内容、教学方法等方面进行探讨,指出该课程建设的主要方向与具体思路,目的是使公安侦查专业计算机犯罪侦查方向的学生能够更好地掌握数据存储的理论知识以及数据恢复的原理与方法,并将其所学运用到计算机犯罪取证中去,为以后从事公安计算机犯罪取证工作打下基础。     

公安院校计算机犯罪侦查专业教学中信息安全相关课程的探讨

本文探讨如何在计算机犯罪侦查专业教学中设置信息安全学科内容。基于作者教学实践,在对计算机犯罪侦查专业人才需求的深刻研究基础上制定完善培养方案,通过合理的课程设置、专业实验实践的设计,使得计算机侦查专业的毕业生可以较为系统的掌握信息安全的基本理论、技术和方法以及计算机犯罪侦查知识,以胜任公安、安全部门专业化的计算机犯罪侦查与网络监查工作。     

公安政法院校开设电子数据课程探讨

基于教学实践,在对电子数据取证专业人才需求的研究基础上,探讨如何在网络安全与执法本科专业和网络保卫执法技术方向警务硕士教学中设置电子数据相关课程,包括课程设置和实验设计等内容,使相关专业的毕业生能够较为系统地掌握电子数据的基本理论、技术和方法,以胜任公安和法庭等对专业化计算机犯罪的侦破与电子数据取证工作。     

犯罪情报分析中的数据挖掘应用

本文基于公安业务中的治安防控原理,构建了面向情报分析和决策指挥的犯罪情报数据挖掘框架.首先,对案事件数据库进行预处理和空间编码的基础上得到标准化的案件信息数据,随后,利用聚类分析、关联分析和分类分析中的相关方法可得到治安案件的时空风险、重点人特征和作案手段特征等信息.通过对北京市实际盗窃案件数据进行挖掘,证明了数据挖掘技术能够很好的应用于犯罪情报的分析.     

基于区块链的食药环知犯罪情报共享模型研究

食药环知犯罪需要公安机关跨部门协同共治，而犯罪情报共享则是协同共治一体化的引擎与关键。在食药环知犯罪情报共享工作中应用区块链技术，目的利用其去中心化、去信任等特性打破食药环知犯罪情报共享部门间的信息壁垒，解决公安、环境监察、药监、银行等部门信任缺失和数据丢失等痛点。本文提出一种基于区块链的食药环知犯罪情报共享模型，并阐述其系统架构、分层模型及运行流程。该系统的构建为区块链技术在犯罪情报共享领域的应用提供新思路，有力促进“智慧公安”建设。     

基于特征提取与改进SVM的电价预测

针对传统电价预测方法由于冗余数据量庞大，特征选择和特征提取准确率低，导致电价预测精度低，预测时间过长的问题，提出构建基于DGCA-PCA的特征提取的改进DE-SVM的电价预测模型GGPDS。首先，采用考虑周期性特征的GCA算法和时段关联性特征的改进GCA算法进行电价特征数据选择；然后采用主成分分析PCA方法进行特征提取；之后将提取数据特征输入改进DE-SVM模型中进行电价预测。实验结果表明，提出的特征提取方法可对海量数据进行有效处理，为后续电价预测模型提供了准确的数据，并进一步提升了电价预测模型的预测精度，降低了模型训练时间成本。日预测实验结果中，本模型的MAPE指标和MAE指标分别取值为7.44%和3.71,对比于传统的电价预测方法电价预测误差更小，预测精度更高。由此说明，本模型可提升电价数据特征提取准确率，从而提高电价预测精度，可在短时间内实现电价准确预测。     

非法获取计算机信息系统数据罪犯罪对象认定

非法获取计算机信息系统数据罪的保护对象是作为信息载体的电子数据，并以数据安全法益中的保密性、可用性为保护法益。对本罪犯罪对象的认定涉及数据范围的判断，本罪中的数据不仅限于身份认证信息，且应包含存储于网络、云端服务器的非本地计算机系统数据。对于司法实践中反映的数据与传统罪名犯罪对象交叉冲突问题，完全明确非法获取计算机信息系统数据罪与传统犯罪适用边界这一方法不存在可行性，现阶段实务中势必面对罪名的冲突，应采想象竞合理论对犯罪行为加以归责。     

基于PCA-XGBoost联合算法的盗窃犯罪预测模型

结合大量犯罪数据特征和行为特征,提出一种PCA-XGBoost联合预测模型。采用PCA算法提取数据集的主要特征;应用XGBoost算法提升预测优化和泛化能力,并通过三种检验方法进行准确率检验。此外,经与XGBoost、CART、RF、NB和LR等分类算法模型的预测结果进行对比,表明PCA-XGBoost联合预测模型对盗窃犯罪数量的预测准确度明显高于其他预测模型,具有较高的应用价值。     

“情报主导警务”背景下电信诈骗 犯罪的防控对策研究

随着经济社会及移动互联通信技术的迅猛发展,电信网络诈骗犯罪日益高发,其犯罪手段和特点也呈现出许多新 的趋势,使得侦查人员的打防侦破困难重重。在大数据、云计算的时代背景下,“情报主导警务”作用日益突出,以此模式指导 电信诈骗犯罪的模式预测与打防侦控,能够很大程度上优化目前的打击电信网络诈骗机制,提升基层公安实践对于电信网络 犯罪的治理效率与质量。     

网络赌博犯罪现状分析与污染探讨

随着计算机网络技术的迅速发展和普及,网络犯罪接踵而来。网络空间最大的特征是其虚拟性,利用网络犯罪的行为和造成的后果却是客观存在的、应给与否定评价的现实实际。网络赌博犯罪中,网络因素的介入,使得传统赌博犯罪的构成要素、表现形态有了新的样态。网络赌博犯罪活动越来越猖獗,已经严重危害社会治安秩序。传统刑法理论如何应对网络赌博犯罪的冲击,摆脱适用的尴尬局面,是研究网络犯罪迫切需要解决的难题。     

刑法学视野下的“维基争密”事件

“维基解密”事件较典型的反映了当前网络犯罪的特征和类型。对网络犯罪的处理,既要传承传统的刑法规则和刑法理论,又要适当根据新情况进行立法调整。网络犯罪包括两种类型,一种是以网络信息系统为对象的犯罪,这类犯罪符合全新的犯罪构成要件,应由刑事立法单独规定为新类型犯罪;另一种是以网络信息系统为工具的犯罪,这类犯罪侵害的是传统法益,只是行为方式、社会危害稍有不同。国内当前刑事立法也正是反映了这一分类思想。     

多尺度视角特征动态融合的盗窃犯罪预测模型

针对盗窃犯罪时空预测特征融合不精、时序动态适应性不足问题,提出自注意力和多尺度多视角特征动态融合的预测模型。首先,以盗窃发案的位置信息为基础,将数据投射到地图栅格内,通过构建一种可将不同时序长度案件数据匹配为自适应长度数据的方法,并组合向量映射后的天气、作案时间、地理位置等属性,构造多维度特征融合的输入向量;其次,采用自注意力机制生成多视角特征动态融合的向量;最后,通过采用多尺度窗口CNN对多视角特征动态融合向量进行编码后送入分类器,预测出每个地图栅格内的发案态势。在某市盗窃数据集上验证,本文方法在3种地理栅格尺度下,预测准确率最高可达到0.899,显著优于其他对比模型。     

网络吸贩毒犯罪初探

近年来,公安机关打击互联网犯罪行动成果显著,然而,最近发生的＂8.31特大网络吸贩毒案＂又给我们敲响了警钟——打击网络犯罪任重道远。本文主要介绍了网络吸贩毒犯罪的现状、特点以及公安机关应该如何治理网络吸贩毒犯罪。     

CrimeTelescope: crime hotspot prediction based on urban and social media data fusion

Crime is a complex social issue impacting a considerable number of individuals within a society. Preventing and reducing crime is a top priority in many countries. Given limited policing and crime reduction resources, it is often crucial to identify effective strategies to deploy the available resources. Towards this goal, crime hotspot prediction has previously been suggested. Crime hotspot prediction leverages past data in order to identify geographical areas susceptible of hosting crimes in the future. However, most of the existing techniques in crime hotspot prediction solely use historical crime records to identify crime hotspots, while ignoring the predictive power of other data such as urban or social media data. In this paper, we propose CrimeTelescope, a platform that predicts and visualizes crime hotspots based on a fusion of different data types. Our platform continuously collects crime data as well as urban and social media data on the Web. It then extracts key features from the collected data based on both statistical and linguistic analysis. Finally, it identifies crime hotspots by leveraging the extracted features, and offers visualizations of the hotspots on an interactive map. Based on real-world data collected from New York City, we show that combining different types of data can effectively improve the crime hotspot prediction accuracy (by up to 5.2%), compared to classical approaches based on historical crime records only. In addition, we demonstrate the usability of our platform through a System Usability Scale (SUS) survey on a full prototype of CrimeTelescope.     

BS-SC Model: A Novel Method for Predicting Child Abuse Using Borderline-SMOTE Enabled Stacking Classifier

For a long time, legal entities have developed and used crime prediction methodologies. The techniques are frequently updated based on crime evaluations and responses from scientific communities. There is a need to develop type-based crime prediction methodologies that can be used to address issues at the subgroup level. Child maltreatment is not adequately addressed because children are voiceless. As a result, the possibility of developing a model for predicting child abuse was investigated in this study. Various exploratory analysis methods were used to examine the city of Chicago’s child abuse events. The data set was balanced using the Borderline-SMOTE technique, and then a stacking classifier was employed to ensemble multiple algorithms to predict various types of child abuse. The proposed approach successfully predicted crime types with 93% of accuracy, precision, recall, and F1-Score. The AUC value of the same was 0.989. However, when compared to the Extra Trees model (17.55), which is the second best, the proposed model’s execution time was significantly longer (476.63). We discovered that Machine Learning methods effectively evaluate the demographic and spatial-temporal characteristics of the crimes and predict the occurrences of various subtypes of child abuse. The results indicated that the proposed Borderline-SMOTE enabled Stacking Classifier model (BS-SC Model) would be effective in the real-time child abuse prediction and prevention process.     

Dynamic road crime risk prediction with urban open data

Crime risk prediction is helpful for urban safety and citizens’life quality.However,existing crime studies focused on coarse-grained prediction,and usually failed to capture the dynamics of urban crimes.The key challenge is data sparsity,since that 1)not all crimes have been recorded,and 2)crimes usually occur with low frequency.In this paper,we propose an effective framework to predict fine-grained and dynamic crime risks in each road using heterogeneous urban data.First,to address the issue of unreported crimes,we propose a cross-aggregation soft-impute(CASI)method to deal with possible unreported crimes.Then,we use a novel crime risk measurement to capture the crime dynamics from the perspective of influence propagation,taking into consideration of both time-varying and location-varying risk propagation.Based on the dynamically calculated crime risks,we design contextual features(i.e.,POI distributions,taxi mobility,demographic features)from various urban data sources,and propose a zero-inflated negative binomial regression(ZINBR)model to predict future crime risks in roads.The experiments using the real-world data from New York City show that our framework can accurately predict road crime risks,and outperform other baseline methods.     

犯罪热点分布与驱动因子分析

盗窃和抢劫作为最普遍的犯罪形态,是各级公共安全部门的工作重点.而发现犯罪热点的时空分布和掌握驱动因子对于犯罪宏观规律的把握非常重要.一方面,基于特定时间尺度的连续的犯罪空间热点分析,有助于帮助公安部门发现特定犯罪类型的犯罪热点的分布形态和变化规律;另一方面,基于主成分分析法,通过对案件发生的诸多驱动因子进行选择,可以发现犯罪热点的主要影响因素;采用Getis-Ord Gi＊热点分析和主成分分析方法,对某市2009年盗窃、抢劫犯罪的月度数据进行了深入分析,得出了相应的结论,为警力在特定时间和空间上的合理分配以及主要的管理方向提供了建议.     

An anticrime information support system design: Application of K-means-VMD-BiGRU in the city of Chicago

The sharp rise in urban crime rates is becoming one of the most important issues of public security, affecting many aspects of social sustainability, such as employment, livelihood, health care, and education. Therefore, it is critical to develop a predictive model capable of identifying areas with high crime intensity and detecting trends of crime occurrence in such areas for the allocation of scarce resources and investment in the prevention and reduction of criminal strategies. This study develops a predictive model based on K-means clustering, signal decomposition technique, and neural networks to identify crime distribution in urban areas and accurately forecast the variation tendency of the number of crimes in each area. We find that the time series of the number of crimes in different areas show a correlation in the long term, but this long-term effect cannot be reflected in the short period. Therefore, we argue that short-term joint law enforcement has no theoretical basis because data show that spatial heterogeneity and time lag cannot be timely reflected in short-term prediction. By combining the temporal and spatial effects, a high-precision anticrime information support system is designed, which can help the police to implement more targeted crime prevention strategies at the micro level.