浅谈计算机网络犯罪

计算机网络犯罪,是指以计算机网络为媒介,以计算机网络资产为对象,运用网络技术,对存储于网络空间里的信息进行侵犯的严重危害网络安全的行为。它与传统的犯罪有着巨大的差异,具有鲜明的特点。网络犯罪的日益广泛性和给社会所带来的巨大破坏作用给我们提出了一个新的、紧迫的课题:如何防范日益猖獗的网络犯罪,减少网络犯罪造成的巨大损失,成为信息时代的当务之急。     

网络犯罪的法律研究

从基本概念看，网络犯罪是：行为主体以网络信息和信息网络为攻击对象，或借助信息网络作为犯罪工具和手段，故意实施的危害信息网络安全、侵犯社会主体合法权益，触犯有关法律规范的行为。在现实中，网络犯罪的概念应该是广泛的，只要在信息网络这个“虚拟世界”里实施的犯罪，或使用了信息网络实施犯罪，就可以认为是涉及网络的犯罪。     

论我国计算机犯罪的表现及其防治

一、计算机犯罪的特征所谓计算机犯罪,是指违反国家规定,利用计算机系统的特性,侵犯计算机信息系统,危害社会信息交流安全,并应受到刑罚惩罚的行为。它具有以下几方面的特征。1、技术性强。与其它犯罪形式相比,计算机犯罪突出的特点在于其技术性和专业性。犯罪者主要是一些掌握了电脑技术,特别是网络技术的专业人士或“网虫”。他们洞悉网络的缺陷和漏洞,运用丰富的电脑及网络技术,借助四通八达的网络对网络系统及各种电子数据资料等信息发动进攻,进行破坏。2、隐蔽性高。由于网络的开放性,可以使用匿名访问,犯罪作案不受时间、地点的限制,…     

浅谈电子证据的收集与运用

因特网无疑是20世纪人类最伟大的创造，网络出现为人们的社会生活开拓了一片新天地，并且正以惊人的速度改变着我们的生活，为人们进行对外交流、信息传递、商业行为提供了一个更为方便和快捷的渠道。然而科学是一柄双刃剑，它在成为“第一生产力”的同时，也成为“第一破坏力”，以数字化信息为特征的网络世界，同样存在纠纷与犯罪，虽然网络是一个虚拟的时空，但网络犯罪所造成的后果却是极为严重的。如何在司法工作中对网络上的侵权行为或不法内容进行科学取证，为打击网络犯罪、制裁网络上的侵权行为提供合法依据，成为一个新问题摆在我们的面前。     

我国网络监管工作面临的问题与应对

随着网络的普及应用和高速发展,网络犯罪呈现出新的特点和趋势,这对我国网络监管工作提出的新的要求,在严峻的网络犯罪形势下,如何更好地应对网络犯罪,作者提出以技术治网、以法治网、提高网监人员的素质、加强网上执法和网际间的司法合作等建议。     

对新型网络诈骗犯罪的思考

基于网络信息技术的迅速发展，计算机犯罪在很多领域出现了新问题，此后各种相关的计算机犯罪层出不穷。文章主要从计算机犯罪和网络犯罪的关系、具体的网络诈骗问题以及计算机犯罪的完善三方面进行分析，指出了计算机犯罪与网络犯罪在本质上的区别，进而对计算机犯罪的下位犯罪——网络犯罪进行深入研究。通过计算机犯罪的有关探讨，从而达到进一步完善计算机犯罪的相关立法、预防计算机犯罪的目的。     

网络犯罪的法律防控

随着网络技术的飞速发展,网络在不同程度上改变了人们的生活方式,也给社会带来了诸多问题,网络犯罪是其中最为严重的问题之一。本文主要通过对网络犯罪产生的社会背景和危害性进行分析,并阐述了网络犯罪法律防控的必要性和急迫性,在此基础上,提出建立以法律防控为主体的网络犯罪综合防控体系的相关建议。     

计算机网络犯罪浅析

本文通过诸多网络犯罪表象,来概括网络犯罪的概念、特点及犯罪构成特征,对网络犯罪类型进行分类,同时对网络犯罪的发展趋势做出判断,希望我们的网络犯罪的立法早日完善。     

浅析网络犯罪的特征及防控

现代信息社会,网络安全日趋重要,信息的勾连方便了人们的生活,但是也使得网络犯罪愈发猖獗。本文以今年的“分贝网”案为引,试图探讨网络犯罪的特征以及对策。     

黑客兴风作浪，警方疲于奔命——网络防盗门亟需加强

拍卖网站上的欺诈案件层出不穷，网络色情、毒品交易、金融犯罪泛滥成灾，网络犯罪将是未来十年国际间最严重的犯罪。建立安全网络，刻不容缓。     

Foreclosure,vacancy and crime

This paper examines the impact of residential foreclosures and vacancies on violent and property crime. To overcome confounding factors, a difference-in-difference research design is applied to a unique data set containing geocoded foreclosure and crime data from Pittsburgh, Pennsylvania. Results indicate that while foreclosure alone has no effect on crime, violent crime rates increase by roughly 19% once the foreclosed home becomes vacant – an effect that increases with length of vacancy. We find weak evidence suggesting a potential vacancy effect for property crime that is much lower in magnitude.     

Blowing it up and knocking it down: The local and city-wide effects of demolishing high concentration public housing on crime

This paper estimates the effect that the closure and demolition of roughly 20,000 units of geographically concentrated high-rise public housing had on crime in Chicago. We estimate local effects of closures on crime in the neighborhoods where high-rises stood and in proximate neighborhoods. We also estimate the impact that households displaced from high-rises had on crime in the neighborhoods to which they moved and neighborhoods close to those. Overall, reductions in violent crime in and near the areas where high-rises were demolished greatly outweighed increases in violent crime associated with the arrival of displaced residents in new neighborhoods.     

Poverty and crime in 19th century Germany

We estimate the impact of poverty on crime in 19th century Bavaria, Germany. Rainfall is used as an instrumental variable for grain (rye) prices to address econometric identification problems in the existing literature. The rye price was a major determinant of living standards during this period. The rye price has a positive effect on property crime: a one standard deviation increased property crime by 8%. OLS estimates are twice as large as instrumental variable estimates, highlighting the value of our empirical approach. Higher rye prices lead to significantly less violent crime, though, and we argue that higher beer prices, caused by the higher rye prices, are a likely explanation.     

Marked point process hotspot maps for homicide and gun crime prediction in Chicago

Crime hotspot maps are a widely used and successful method of displaying spatial crime patterns and allocating police resources. However, hotspot maps are often created over a single timescale using only one crime type. In the case of short-term hotspot maps that utilize several weeks of crime data, risk estimates suffer from a high variance, especially for low frequency crimes such as homicide. Long-term hotspot maps that utilize several years of data fail to take into account near-repeat effects and emerging hotspot trends. In this paper we show how point process models of crime can be extended to include leading indicator crime types, while capturing both short-term and long-term patterns of risk, through a marked point process approach. Several years of data and many different crime types are systematically combined to yield accurate hotspot maps that can be used for the purpose of predictive policing of gun-related crime. We apply the methodology to a large, open source data set which has been made available to the general public online by the Chicago Police Department.     

Modelling and predicting recorded property crime trends in England and Wales—a retrospective

In 1999 the Home Office published, for the first time ever, 3-year ahead projections of property crime in England and Wales. The projections covered the period 1999–2001 and indicated strong upward pressure after five full years of falling crime. This pressure was generated by three factors: the number of young men in the general population, the state of the economy and the fact that property crime appeared to be well below its underlying trend level. The projections received a mixed response, with some agreeing that crime was set to rise while questioning the scale of any increase, to others who doubted the value of this type of econometric modelling. In fact, property crime did increase in 1999, although not at the rate suggested by the models—and indeed levels of burglary continued to fall. This paper addresses some of the reasons for this disparity as well as considering various criticisms of the Home Office approach.     

Is there a compensating wage differential for high crime levels? First evidence from Europe

This paper investigates whether high regional crime levels lead to a compensating wage differential paid by firms in the respective region. Using data from German social-security records, official police statistics and official statistics for 2003–2006, I consider both violent and non-violent crimes and use three-way error-components estimators to control for individual and regional heterogeneity. The findings suggest that wages are practically unrelated to changes in crime rates. This result is robust over a wide range of subgroups. There is, however, some evidence that crime rates influence land prices.     

The effect of spatiotemporal resolution on predictive policing model performance

Being able to anticipate crime such that new crime events can be dealt with effectively or prevented entirely, leads police forces worldwide to look at applying predictive policing, which provides predictions of times and places at risk for crime, such that proactive preventative measures can be taken. Ideally, predictive policing models predict crime at a high spatio-temporal level, while also providing optimal prediction performance. The main objective of this paper is therefore to evaluate the impact of varying grid resolution, temporal resolution and historical time frame on prediction performance. To investigate this, we analyse home burglary data from a large city in Belgium and predict new crime events using a range of parameter values, comparing the resulting prediction performances. Given the potential prediction performance costs associated with prediction at a high spatio-temporal resolution, consideration should be given to balance practical requirements with performance requirements.     

Short-term forecasting of crime

The major question investigated is whether it is possible to accurately forecast selected crimes 1 month ahead in small areas, such as police precincts. In a case study of Pittsburgh, PA, we contrast the forecast accuracy of univariate time series models with naïve methods commonly used by police. A major result, expected for the small-scale data of this problem, is that average crime count by precinct is the major determinant of forecast accuracy. A fixed-effects regression model of absolute percent forecast error shows that such counts need to be on the order of 30 or more to achieve accuracy of 20% absolute forecast error or less. A second major result is that practically any model-based forecasting approach is vastly more accurate than current police practices. Holt exponential smoothing with monthly seasonality estimated using city-wide data is the most accurate forecast model for precinct-level crime series.     

Edge versus center: finding common ground in the capitalization debate

A continuing debate questions whether capitalization of taxes and public services into house price occurs. The current study argues for an inverse relationship between housing supply elasticity and capitalization rates. A sample is split into houses on the interior and edge of the urban area. Capitalization of schooling and crime occurs everywhere, but it is weaker toward the edge of an urban area where housing supply elasticities and developer activity are greater. Tax results are less robust, but the capitalization rates of crime and school quality are roughly twice as strong on the interior than on the urban fringe.