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网络犯罪调查技术与案例分析网络犯罪是随着互联网的普及而迅速发展起来的一种犯罪形式。
它利用网络技术进行违法犯罪活动，给社会造成了巨大的危害。
为了有效打击网络犯罪和维护网络安全，对网络犯罪的调查技术和案例分析显得尤为重要。
本文将介绍网络犯罪调查的技术手段，并结合实际案例进行分析，以期提高对网络犯罪的防范和打击能力。
一、网络犯罪调查的技术手段1. 数字取证技术数字取证是网络犯罪调查的关键技术之一。
当发生网络犯罪事件时，调查人员需要通过获取电子数据的方式收集相关证据，这就需要运用数字取证技术。
数字取证技术包括数据恢复、数据隐藏解析、数据验证等多个方面，它能够帮助调查人员找到、保留和分析作为证据的数字信息。
2. 网络溯源技术网络溯源是指通过网络上的各种技术手段，追查犯罪分子的真实身份和行踪。
网络溯源技术通常需要运用IP地址追踪、路由跟踪和数据流追踪等技巧，以确定犯罪分子所使用的网络设备和位置。
其中，IP地址追踪是最常见的网络溯源技术之一，它通过分析网络数据包中的源IP地址和目的IP地址，确定犯罪嫌疑人的网络活动轨迹。
3. 数据挖掘技术数据挖掘技术是一种通过分析大量数据，以发现其中的有价值信息的方法。
在网络犯罪调查中，数据挖掘技术可以应用于犯罪嫌疑人的行为分析、社交网络分析和关联规则发现等方面。
通过对犯罪嫌疑人的网络行为数据进行挖掘和分析，可以揭示他们的作案手法、作案动机和作案模式，为案件侦破提供重要线索。
二、网络犯罪调查案例分析1. 电信诈骗案电信诈骗是一种常见的网络犯罪行为，也是给人们造成较大损失的一种犯罪形式。
警方通过数字取证技术，成功追踪到了一个电信诈骗团伙的活动轨迹。
调查人员通过对电信诈骗团伙成员的通话记录、短信记录和银行交易记录进行分析，发现他们使用了大量虚假身份和银行账号进行电信诈骗活动。
通过综合运用数据挖掘技术，警方成功地查明了电信诈骗团伙的成员组成和犯罪模式，最终将其抓获归案。
2. 网络侵入案网络侵入是指通过网络对他人的计算机系统进行非法访问和操作的行为。
大数据分析师如何进行犯罪数据分析和预防在当今信息爆炸的时代，犯罪活动的种类和数量不断增加，对社会稳定和人民安全构成了严重威胁。
然而，随着大数据技术的迅猛发展，大数据分析师的作用变得愈发重要。
本文将介绍如何有效进行犯罪数据分析和预防，以帮助大数据分析师更好地履行自己的职责。
一、数据收集与整理犯罪数据分析的第一步是进行数据收集与整理。
大数据分析师需要广泛收集各种与犯罪有关的数据，包括犯罪记录、警方通报、人口普查数据等。
这些数据来源的多样性和大规模的特点使得大数据分析师在数据整理上面临着巨大的挑战。
为了提高工作效率，大数据分析师可以利用数据清洗工具对数据进行初步预处理，剔除重复、缺失或异常数据，确保数据的准确性和完整性。
二、数据挖掘与分析数据挖掘与分析是犯罪数据分析的核心环节。
大数据分析师需要利用数据挖掘技术和算法揭示数据背后的规律和关联。
首先，大数据分析师可以应用聚类分析算法对犯罪数据进行分组，识别出不同类型的犯罪活动。
其次，大数据分析师可以运用关联规则挖掘算法，找出不同犯罪现象之间的关联性，为之后的预测和预防提供依据。
此外，大数据分析师还可以借助时间序列分析，观察犯罪活动的变化趋势，为预测未来犯罪行为提供参考。
三、模型建立与预测基于挖掘到的规律和关联，大数据分析师可以建立预测模型，为犯罪预防提供参考和指导。
通过对历史犯罪数据的分析和建模，可以预测未来可能发生的犯罪行为和犯罪热点区域，从而帮助警方加大巡逻力度和加强监控措施。
此外，大数据分析师还可以利用社交网络分析技术，深入挖掘犯罪组织间的关系和活动规律，为打击犯罪网络提供有力支持。
四、实时监测与预警在犯罪数据分析的过程中，大数据分析师需要关注实时数据，及时发现和响应潜在的犯罪风险。
通过建立预警模型和警觉系统，大数据分析师可以在犯罪活动出现前就发现异常迹象，并及时向相关部门报告，从而有效提升犯罪预防和打击的效果。
同时，大数据分析师还可以借助智能监控技术，对关键区域进行实时监测和预警，实现犯罪数据分析的实时化和精准化。
基于网络数据分析的违法犯罪行为预测研究一、引言近年来，随着网络技术与数据科学的快速发展，基于网络数据分析的违法犯罪行为预测研究也逐渐成为研究热点。
通过对网络数据进行分析，可以更加准确地预测犯罪行为的发生和发展趋势，有利于相关部门及时制定有效的措施预防犯罪，维护社会稳定。
本文将从数据的来源与收集、预测模型与算法、准确度与实时性等方面对基于网络数据分析的违法犯罪行为预测进行探讨。
二、网络数据来源与收集网络数据来源与收集是基于网络数据分析的违法犯罪行为预测的基础。
目前，网络数据来源主要有以下几种：1.社交媒体数据。
如微博、微信等，这些数据包含大量的人际交往信息，通过对这些数据进行分析可以了解人们的思想倾向、情感态度等，有助于预测犯罪行为的发生趋势。
2.网络搜索数据。
如百度、谷歌等搜索引擎，网络搜索数据广泛、实时，可以通过对用户的搜索行为进行分析，了解他们的兴趣爱好、需求等，为犯罪行为的预测提供支持。
3.在线交易数据。
如淘宝、京东等电商平台，这些平台通过大数据分析可以了解顾客的消费习惯，为犯罪行为的预测提供线索。
收集这些数据需要具备相关的技术手段，当前主要的技术手段有以下几种：1.网络爬虫。
通过程序自动访问网络，获取数据，并将数据转换为结构化的数据，以供分析。
2.API接口。
通过API接口获取特定网站或应用程序的数据，这种方法更加稳定，具有较好的数据准确度。
3.传感器技术。
通过安装传感器来收集不同类型的数据，这种方法在一些场景下效果很好，如物流配送、城市交通等领域。
三、预测模型与算法预测模型与算法是实现基于网络数据分析的违法犯罪行为预测的核心技术。
目前，犯罪行为的预测主要采用以下两种模型：1.监督学习模型。
监督学习模型通过训练样本和标签来建立模型，这些训练样本来自于以往已知的犯罪案件信息，标签表示样本是否发生了犯罪行为。
建立好的模型可以用于预测新样本的标签值，即是否存在犯罪行为。
2.无监督学习模型。
无监督学习模型不需要事先标注样本的标签，它通过对数据空间的聚类、降维、异常检测等技术，对数据空间进行分割和分类，以发现可能的犯罪行为。
基于数据挖掘技术的违法犯罪预测分析引言犯罪是社会不可避免的现象。
犯罪的发生对社会稳定和经济发展都会带来严重影响。
因此，如何预测和预防犯罪已经成为当今社会面临的重要问题。
数据挖掘技术是一种可以利用历史数据进行分析和预测的工具，已经在违法犯罪预测分析中得到了广泛应用。
本文将介绍基于数据挖掘技术的违法犯罪预测分析。
一、数据预处理数据预处理是数据挖掘的重要步骤。
在实际应用中，原始数据通常存在着噪声、缺失值和异常值等问题，需要进行数据清洗和预处理。
对于违法犯罪预测分析，我们需要利用历史记录中的各种数据，并对其进行组合和处理。
这些数据包括个人信息、社会信息、经济信息等各种数据。
其中，个人信息如年龄、性别、婚姻状况、教育程度等；社会信息如就业情况、居住地、社交关系等；经济信息如收入、财产、债务等。
在实际应用中，数据的来源非常广泛，如社会保障、贷款记录、移动通信记录、互联网数据等。
对于这些数据，我们需要进行数据清洗、去掉噪声、填充缺失值、处理异常值。
同时，我们需要进行变量选择，根据相关性分析和经验来选择对预测结果影响最大的变量。
二、预测模型选择对于违法犯罪预测分析，需要选择合适的预测模型。
预测模型可以根据需求选择，包括逻辑回归、决策树、神经网络、支持向量机等多种算法。
这些算法在不同的情况下具有不同的优缺点，最终选择的模型需要综合考虑预测精度、速度和模型可解释性等因素。
逻辑回归模型是一种常用的分类方法。
它通过将输入数据映射到一个数值来表示分类的可能性。
逻辑回归模型适用于线性可分的分类问题，能够通过调整回归系数来适应不同数据集合。
此外，逻辑回归模型的结果也比较容易解释。
决策树模型是一种基于树形结构的分类方法。
它通过对数据集的划分来不断建立决策树，每个节点决定了数据的分类。
决策树模型具有良好的可解释性和灵活性，并且能够处理离散、连续和缺失数据。
神经网络模型是一种基于神经元层次结构的分类方法。
它通过多个神经元进行计算和传递信息，从而得到分类结果。
网络犯罪取证经验总结网络犯罪日益猖獗，给社会秩序和个人权益带来了严重的威胁。
为了打击网络犯罪行为，取证成为了一项重要而关键的工作。
本文将总结网络犯罪取证的经验，并介绍一些有效的方法和技巧，帮助读者在实际操作中取得成功。
一、充分理解网络犯罪行为要成功取证，首先需要对不同类型的网络犯罪有全面的了解。
网络犯罪可以包括黑客攻击、网络诈骗、网络盗窃等多种行为，每种行为都有其独特的特点和取证难度。
针对不同的犯罪行为，我们需要制定相应的调查策略和技术手段。
二、保护证据的完整性和可靠性在取证过程中，保护证据的完整性和可靠性至关重要。
我们需要确保取得的证据真实可信，以便在法庭上有效地进行辩护。
为此，我们可以采取以下措施：1.及时保留证据：一旦发现可疑行为，我们应立即采取行动并保留相关证据。
这可能包括保存电子邮件、聊天记录、网页截图、视频文件等。
2.数字取证工具的运用：通过使用专业的数字取证工具，我们可以有效地提取、保存和分析数字证据。
这些工具可以帮助我们追踪黑客攻击路径、还原删除的文件和恢复丢失的数据。
3.数据备份：为了防止证据的丢失或被篡改，我们应将证据进行备份并存放在安全的地方。
同时，备份可以为我们提供多个角度的证明，增加案件的说服力。
三、运用网络侦查和监控技术网络侦查和监控技术是网络犯罪取证中不可或缺的一环。
以下是一些常用的技术方法：1.网络流量分析：通过分析网络流量，我们可以了解黑客的攻击路径和活动轨迹。
这些信息有助于我们追踪犯罪分子的真实身份和所在地。
2.数据挖掘和分析：通过应用数据挖掘和分析技术，我们可以从大量的数据中发现隐藏的模式和规律。
这些信息可能包含证据，帮助我们揭示犯罪背后的真相。
3.日志分析：网络设备和服务器通常会生成大量的日志文件，记录用户的操作和行为。
通过分析这些日志文件，我们能够还原犯罪过程并追踪犯罪嫌疑人。
四、与相关机构合作网络犯罪取证工作需要与相关机构合作，包括警察、网络安全公司、法律机构等。
司法部工作人员的犯罪情报收集与分析技巧在司法部工作的人员需要具备犯罪情报收集与分析的技巧，这是保障司法公正与打击犯罪的重要一环。
本文将介绍一些司法部工作人员可采用的犯罪情报收集与分析技巧，以提高犯罪侦查的效果。
一、有效的情报收集方法1. 网络搜索：现如今，互联网已经成为大量信息的来源之一。
通过使用搜索引擎和专业数据库，司法部工作人员可以快速获取到相关的犯罪情报。
然而，在进行网络搜索时，应注意核实信息的来源和可信度，避免误导性的信息。
2. 调查访问：通过调查访问，工作人员可以直接与相关当事人、证人、受害人和其他执法机构进行沟通。
这种面对面的交流可以提供更加准确、详细的情报信息。
在进行调查访问时，要注意收集证据、记录相关细节，并确保保护当事人的隐私和合法权益。
3. 数据库查询：参考和利用已有的数据库信息，是提高情报收集效率的重要途径。
例如，司法部工作人员可以查询有关犯罪记录、嫌疑人信息、法律法规等相关数据库，以获取更为全面和准确的情报。
4. 线索奖励制度：建立一套鼓励民众提供犯罪线索的奖励制度，可以激发社会各界积极参与到犯罪情报的收集中来。
通过向社会公开相关的线索奖励政策，可以吸引更多有价值的情报提交，并对贡献有突出成绩的举报人予以适当的奖励。
二、犯罪情报分析的技巧1. 收集和整理情报：在收集到大量情报后，工作人员需要对数据进行整理和分类。
可以使用信息系统和软件工具，将情报按照时间、地点、人员等维度进行分类和整理，以便更好地理解和分析。
2. 进行关联分析：通过对收集到的情报进行关联分析，工作人员可以找出线索和关系，揭示犯罪活动的背后。
例如，分析人员可以通过对通话记录、电子邮件等进行比对，找出嫌疑人和其他相关人员之间的联系。
3. 利用数据挖掘技术：数据挖掘技术可以帮助工作人员从大量数据中发现隐藏的模式和规律，提供更深入的分析。
可以利用数据挖掘软件进行关系网络、时间轴和趋势分析，帮助预测犯罪行为和确定关键嫌疑人。
公安学中的网络犯罪侦查与打击技术网络犯罪已成为当代社会不可忽视的问题。
随着互联网的普及和发展，网络犯罪手法不断翻新，给社会治安带来了巨大的挑战。
在公安学中，网络犯罪侦查与打击技术成为了一项重要内容。
本文将探讨公安学中的网络犯罪侦查与打击技术，并介绍相关的案例和技术手段。
一、网络犯罪侦查技术的意义网络犯罪侦查技术在公安学中的意义重大。
首先，通过网络犯罪的侦查，公安机关可以及时发现和捕获犯罪分子，提高了犯罪打击的效果。
其次，网络犯罪侦查技术可以有效打击网络犯罪活动，维护网络空间的安全和秩序。
网络犯罪不仅仅是一种个体行为，更是对社会公共利益的侵害。
公安机关通过网络犯罪侦查技术，才能够打破犯罪分子的隐匿性和匿名性，保障了网络空间的正常运行。
二、网络犯罪侦查的常用技术1. 网络监控技术网络监控技术是网络犯罪侦查中常用的技术手段之一。
公安机关通过在网络中设置监控节点，对网络通信进行实时监控和记录，可以获取到网络犯罪活动的关键信息。
例如，通过监控犯罪分子的上网记录和通信内容，可以追踪到犯罪分子的身份和活动轨迹，为进一步侦破案件提供了重要线索。
2. 数据挖掘技术数据挖掘技术在网络犯罪侦查中具有重要的作用。
公安机关通过大数据分析和挖掘，可以从各种数据中发现隐藏的模式和规律，为犯罪侦查提供有力的支持。
例如，通过分析大量的网络数据，可以发现犯罪分子的交流模式、行为特征等，从而对犯罪活动进行更准确的定性和定量分析。
3. 数字取证技术数字取证技术是网络犯罪侦查中不可或缺的技术手段。
公安机关通过对电子设备、存储介质等进行取证，可以获取到犯罪嫌疑人的电子痕迹和证据。
数字取证技术不仅包括传统的取证方式，如数据恢复和数据镜像，还包括对加密数据和隐蔽数据的解密和提取。
这些技术手段为网络犯罪侦查提供了全面和深入的证据来源。
三、网络犯罪打击技术的发展趋势随着技术的不断发展和创新，网络犯罪打击技术也在不断改进和完善。
未来的网络犯罪打击技术将主要体现在以下几个方面：1. 人工智能技术的应用人工智能技术作为一种新兴的技术手段，将对网络犯罪打击产生巨大的影响。
战胜网络犯罪网络安全工程师的数字取证与调查随着互联网的快速发展和广泛应用，网络犯罪也日益猖獗，给人们的生活和社会安全带来了巨大的威胁。
在这个信息时代，网络安全工程师的数字取证技能和调查能力显得尤为重要。
本文将介绍网络安全工程师在战胜网络犯罪中所需的数字取证与调查技术。
一、数字取证技术数字取证技术是网络安全工程师进行网络犯罪调查的重要工具。
数字取证的过程主要包括以下几个步骤：1. 保护现场：在开始数字取证前，网络安全工程师需要确保现场被保护，避免证据被篡改或破坏。
这意味着要尽力保持现场的完整性和真实性。
2. 采集证据：网络安全工程师需要使用专业的取证工具和技术，对网络犯罪现场进行证据采集。
这些证据可以包括网络日志、通信记录、文件和数据库等。
在采集证据的过程中，需要确保证据的完整性和可靠性。
3. 分析证据：采集到证据后，网络安全工程师需要进行证据的分析和研究，以确定网络犯罪的行为和手段。
这需要使用各种取证分析工具和技术，如数据恢复、文件解密和网络流量分析等。
4. 保护证据：在整个取证过程中，网络安全工程师需要妥善保护证据，避免证据被篡改或泄露。
这涉及到对证据进行数字签名、加密和存储等安全措施。
二、网络犯罪调查技术网络犯罪调查技术是网络安全工程师在战胜网络犯罪中的重要手段。
网络犯罪调查的过程主要包括以下几个方面：1. 网络追踪：网络安全工程师通过分析网络流量和日志记录，追踪网络犯罪的源头和传播路径。
这需要使用网络追踪工具和技术，以获取关于攻击者的IP地址、地理位置和使用的设备等信息。
2. 数据分析：网络安全工程师通过对网络犯罪相关数据的分析，了解攻击者的行为模式和特征，并推断出可能的攻击手段和目的。
这需要使用数据挖掘、模式识别和统计分析等技术。
3. 取证法律依据：网络安全工程师在进行网络犯罪调查时，需要遵守相关的取证法律规定，不违反个人隐私和人权。
他们需要了解国家和地区的法律法规，并在调查过程中获得合法的取证授权。
网络犯罪侦察技术的研究与应用第一章研究概述网络犯罪侦察技术是随着网络犯罪的不断增多而逐渐形成的一种新型技术。
网络犯罪形式繁多，侵害范围广泛，使得传统的犯罪侦查手段在网络犯罪的侦测和打击上已经显得力不从心。
为了更好地应对网络犯罪的威胁，研究网络犯罪侦察技术成为一项迫切的任务。
本文将从技术特点、技术原理、技术应用等方面对网络犯罪侦察技术进行深入探讨。
第二章技术特点网络犯罪侦察技术主要以网络信息采集、数据挖掘、犯罪行为分析、威胁情报监控等方面的技术为主体。
其中，主要特点如下：1. 高效性。
与传统侦查手段相比，网络犯罪侦察技术在信息收集和分析处理上速度更快、效率更高。
2. 自动化。
网络犯罪侦察技术的自动化程度比传统侦查手段更高，能够做到实时监测、自动分析和自动报警等操作。
3. 多元化。
网络犯罪侦察技术主要通过网络数据挖掘的方式获取信息，结合人工智能、大数据分析等技术，可实现更广泛、更全面的信息收集和分析。
第三章技术原理网络犯罪侦察技术主要依托以下原理：1. 数据挖掘。
通过对网络数据进行存储、建模和分析，提取出隐藏的信息和模式，为后续犯罪分析提供有力支持。
2. 可视化技术。
通过图表、地图等直观的形式呈现数据，便于用户观察、分析和理解，更加方便实用。
3. 自然语言处理。
通过对文本、语音等信息内容进行解析和处理，提取关键信息，增强信息的可读性和可理解性。
第四章技术应用网络犯罪侦察技术在打击网络犯罪方面发挥了重要作用。
应用范围主要包括以下方面：1. 犯罪威胁分析。
通过大数据分析等技术，对犯罪威胁进行准确预测和分析，采取有效措施防治网络犯罪。
2. 指挥调度。
实时监测、自动预警、自动发送警报等功能，有助于指挥调度部门更好地响应网络犯罪行为。
3. 犯罪行为分析。
通过数据挖掘等技术，针对网络犯罪活动进行研究和分析，识别犯罪者、监测犯罪活动，为打击网络犯罪提供有效支持。
第五章技术未来趋势网络犯罪侦察技术已经成为了网络犯罪打击的重要工具，未来的发展方向还将更加多元和先进：1. 强化数据安全。
基于大数据技术的网络犯罪行为分析与模拟预警网络犯罪是指利用互联网和相关信息技术进行各种非法活动的行为。
随着互联网的迅速发展和普及，网络犯罪越来越成为一个全球性的威胁。
为了有效应对网络犯罪，大数据技术被引入网络犯罪行为分析与模拟预警的领域，以帮助警方和相关部门及时识别和应对网络犯罪活动。
1. 基于大数据技术的网络犯罪行为分析网络犯罪行为分析是通过收集、分析和挖掘大量网络犯罪相关数据，揭示网络犯罪行为的规律和趋势。
大数据技术的应用能够帮助警方快速有效地识别出潜在的网络犯罪威胁，从而有效打击和防范网络犯罪活动。
首先，大数据技术能够收集和整合大量不同来源的数据，包括网络活动日志、电话记录、交易数据等。
通过建立合适的数据处理和存储系统，警方能够对这些数据进行高效的检索和分析，从而发现潜在的网络犯罪线索。
其次，大数据技术能够利用数据挖掘和机器学习算法分析网络犯罪行为模式和特征。
通过对历史数据进行模式识别和预测分析，可以发现网络犯罪的潜在规律和趋势。
这种分析结果可用于指导警方制定合理有效的打击网络犯罪的策略和措施。
此外，大数据技术还能够通过图像识别和人脸识别等技术手段实现对网络犯罪嫌疑人的身份识别，提高犯罪侦查和追踪的效率。
通过大数据技术的应用，警方可以更好地分析和了解网络犯罪行为，从而更加准确地预测和预警潜在的网络犯罪活动。
2. 基于大数据技术的网络犯罪模拟预警网络犯罪模拟预警是通过建立合适的模型和算法，利用大数据技术模拟网络犯罪活动的发展趋势和可能的后果，为警方提供有效的预警信息。
大数据技术能够利用历史数据和实时数据建立网络犯罪模型，通过对网络犯罪行为的模拟和预测，为警方提供可能的网络犯罪发展趋势和潜在风险的预警信息。
利用数据分析和挖掘技术，可以发现网络犯罪活动的演化规律和趋势，达到预测和预警的目的。
通过模拟预警系统，警方可以根据不同场景和情景进行模拟实验，评估网络犯罪事件对社会造成的影响和损失。
利用大数据技术的模拟分析，可以量化和评估不同网络犯罪活动对经济、社会和安全的影响，为决策者提供科学依据，制定更加有效的预警和防控策略。
Criminal Network Analysis and Visualization: A Data Mining Perspective(Forthcoming article accepted for publication in Communications of the ACM)By Jennifer Xu and Hsinchun ChenCriminal Network AnalysisAfter the tragic events of September 11, 2001, academics have been called on for possible contributions in uncovering terrorist networks to enhance public safety and national security. Both the public and the Pentagon have realized that knowledge of the structure of terrorist networks and how those networks operate is one of the key factors in winning the so-called “netwar”. In this new war against terrorists probably the most critical weapons that our intelligence and law enforcement agencies should be armed with are reliable data and sophisticated techniques that help discover useful knowledge from the data.Study of terrorist networks falls into the larger category of criminal network analysis, which is often applied to investigations of organized crimes (e.g., terrorism, narcotics trafficking, fraud, gang-related crimes, armed robbery, etc.). Unlike other types of crimes such as homicide and sex offenses that often are committed by single or a few offenders, organized crimes are carried out by multiple, collaborating offenders. These offenders may form groups and teams and play different roles. In a narcotics network, for instance, different groups may be responsible for handling the drug supply, distribution, sales, smuggling, and money laundering. In each group, there may be a leader who issues commands and provides steering mechanisms to the group, as well as gatekeepers who ensure that information and drugs flow effectively to and from other groups. Criminal network analysis therefore requires the ability to integrate information frommultiple crime incidents or even multiple sources and discover regular patterns about the structure, organization, operation, and information flow in criminal networks.To untangle and disrupt criminal networks, both reliable data and sophisticated techniques are indispensable. However, intelligence and law enforcement agencies often are faced with the dilemma of having too much data, which in effect makes too little value. On one hand, they have large volumes of “raw data” collected from multiple sources: phone recor ds, bank accounts and transactions, vehicle sales and registration records, surveillance reports, etc. [9, 10]. On the other hand, they lack sophisticated network analysis tools and techniques to utilize the data effectively and efficiently. Currently, criminal network analysis is primarily a manual process that consumes much human time and efforts, thus has limited applicability to crime investigation. The objective of this paper is to provide a data mining perspective for criminal network analysis. In following sections, we discuss the challenges in data processing, review existing network analysis and visualization tools, and recommend the Social Network Analysis (SNA) approaches. Although SNA is not traditionally considered as a data mining technique, it is especially suitable for mining large volumes of association data to discover hidden structural patterns in criminal networks [9, 10]. We also report some data mining projects for criminal network analysis in the COPLINK research, which is the NIJ- and NSF- funded research for management of law enforcement knowledge [3].Challenges in Data ProcessingLike data mining applications in many other domains, mining law enforcement data involves many challenges. First, incomplete, incorrect, or inconsistent data can create problems. Second, the special characteristics of criminal networks cause difficulties that are not common in other data mining applications.∙Incompleteness [10]. Criminal networks are covert networks that operate in secrecy and stealth [8]. Criminals may minimize interactions to avoid attracting police attention and their interactions are hidden behind various illicit activities. Thus, data about criminals and their interactions and associations are inevitably incomplete, causing missing nodes and links in networks.∙Incorrectness. Incorrect data regarding criminals’ identities, physical characteristics, and addresses may result either from unintentional data entry errors or from intentional deception by criminals. Many criminals lie about their identity information when caught and investigated.∙Inconsistency. Information about a criminal who has multiple police contacts may be entered into law enforcement databases multiple times. These records are not necessarily consistent. Multiple data records could make a single criminal to appear to be different individuals. When seemingly different individuals are included in a network under study, misleading information may result.Problems specific to criminal network analysis lie in data transformation, fuzzy boundaries, and network dynamics:∙Data transformation. Network analysis requires that data be presented in a specific format, in which network members are represented by nodes, and their associations or interactions are represented by links. However, information about criminal associations usually is not explicit in raw data. The task of extracting criminal associations from raw data and transforming them to the required format can be fairly labor-intensive and time-consuming.∙Fuzzy boundaries[10]. Boundaries of criminal networks are likely to be ambiguous. It can be quite difficult for an analyst to decide whom to include and whom to exclude froma network under study.∙Network dynamics [10]. Criminal networks are not static, but are subject to changes over time. New data and even new methods of data collection may be required to capture the dynamics of criminal networks.Some techniques have been developed to address these problems. For example, to improve data correctness and consistency, many heuristics are employed in the FinCEN system at the U.S. Department of the Treasury to disambiguate and consolidate financial transactions into uniquely identified individuals in the system [5]. Other approaches like the concept space method [3] can transform crime incident data into a networked format [12].Criminal Network Analysis and Visualization ToolsKlerks [7] categorized existing criminal network analysis approaches and tools into three generations.First generation: manual approachRepresentative of the first generation is the Anacpapa Chart [6]. With this approach, an analyst must first construct an association matrix by identifying criminal associations from raw data. A link chart for visualization purposes can then be drawn based on the association matrix. For example, to map the terrorist network containing the 19 hijackers in the September 11 attacks, Krebs [8] gathered data about the relationships among the hijackers from publicly released information reported in several major newspapers. He then manually constructed an association matrix to integrate these relations [8] and drew a network representation to analyze the structural properties of the network (Figure 1).Figure 1: The terrorist network containing the 19 hijackers on September 11, 2001 (Source: Business 2.0 Magazine, December 2001).Although such a manual approach for criminal network analysis is helpful in crime investigation, when data sets are very large it becomes an extremely ineffective and inefficient method.Second generation: graphic-based approachSecond-generation tools can automatically produce graphical representations of criminal networks. Most existing network analysis tools belong to this generation. Among them Analyst’s Notebook [7], Netmap [5], and Watson [1] are the most popular. For example, Analyst’sNotebook can automatically generate a link chart based on relational data from a spreadsheet or text file (Figure 2A).Recently, two second-generation network analysis approaches have been developed by the COPLINK research and its co-developer, the Knowledge Computing Corporation. The first approach employs a hyperbolic tree metaphor to visualize crime relationships [3]. It is especially helpful for visualizing a large amount of relationship data because it simultaneously handles both focus and context (Figure 2B). The second approach uses a spring embedder algorithm [4] to adjust positions of nodes automatically to prevent a network display from being too cluttered. In such a view, icons represent different types of entities. A filtering function allows a user to select only those entity types of interest. (Figure 2C-E).Although second-generation tools are capable of using various methods to visualize criminal networks, their sophistication level remains modest because they produce only graphical representations of criminal networks without much analytical functionality. They still rely on analysts to study the graphs with awareness to find structural properties of the network.ABC D EFigure 2. Second-generation criminal network analysis and visualization tools. (A) Analyst’s Notebook (Source: i2, Inc.). The system can automatically arrange network nodes and allows one to drag nodes around for easier interpretation. (B) The hyperbolic tree view of relations among multiple criminal entities (Source: COPLINK). (C) The initial layout of a criminal network produced by the network view (Source: Knowledge Computing Corporation). (D) The network layout is adjusted automatically. The system moves the nodes having the largest number of links to the center of the display. A user can actually see movements of the nodes while their positions are adjusted and can fix the display at any time during the adjustment. (E) A user may choose only the entity type that is of interest (e.g., person) and view textual explanations (e.g., person name, address, and relations).Third generation: structural analysis approachThe third generation approach is expected to provide more advanced analytical functionality to assist crime investigation. Sophisticated structural analysis tools are needed to go from merely drawing networks to mining large volumes of data to discover useful knowledge about the structure and organization of criminal networks.Data Mining PerspectiveIntelligence and law enforcement agencies often are interested in finding structural properties of criminal networks [9]:∙What subgroups exist in the network?∙How do these subgroups interact with each other?∙What is the overall structure of the network?∙What are the roles (central/peripheral) network members play?Clear understanding of these structural properties of a criminal network may help analysts target critical network members for removal or surveillance, and locate network vulnerabilities where disruptive actions can be effective. Appropriate network analysis techniques, therefore, are needed to mine criminal networks and gain insight into these problems.Social Network AnalysisBecause Social Network Analysis (SNA) techniques are designed to discover patterns of interaction between social actors in social networks [11], they are especially appropriate for studying criminal networks [9, 10].Specifically, SNA is capable of detecting subgroups, discovering their patterns of interaction, identifying central individuals, and uncovering network organization and structure.Subgroup detectionA criminal network often can be partitioned into subgroups consisting of individuals who closely interact with each other. Given a network, traditional data mining techniques such as cluster analysis may be employed to detect underlying groupings that are not otherwise apparent in the data. Hierarchical clustering methods have been proposed to partition a network into subgroups [11]. Cliques whose members are fully or almost fully connected can also be detected based on clustering results. Discovery of patterns of interactionPatterns of interaction between subgroups can be discovered using an SNA approach called blockmodeling [11]. This approach was originally designed to interpret and validate theories of social structures. When used in criminal network analysis, it can reveal patterns of between-group interactions and associations and can help reveal the overall structure of criminal networks under study. Given a partitioned network, blockmodel analysis determines the presence or absence of an association between a pair of subgroups based on a link density measure. In a network with undirected links, for example, the link density between two subgroups i and j can be calculated by j i ijij n n m d , where m ij is the actual number of links between subgroups i and j ; n iand n j represent the number of nodes within subgroups i and j , respectively. When the density of the links between the two subgroups is greater than a predefined threshold value, a between-group association is present, indicating that the two subgroups interact with each other constantly and thus have a strong association. By this means, blockmodeling summarizes individual interaction details into interactions between groups so that the overall structure of the network becomes more prominent.CentralityCentrality deals with the roles of individuals in a network. Several centrality measures, such as degree, betweenness, and closeness can suggest the importance of a node in a network [11]. The degree of a particular node is its number of links; its betweenness is the number of geodesics (shortest paths between any two nodes) passing through it; and its closeness is the sum of all the geodesics between the particular node and every other node in the network. An individual’s having a high degree, for instance, may imply his leadership; whereas an individual with a high betweenness may be a gatekeeper in the network. Baker and Faulkner [2] employed these three measures, especially degree, to find the central individuals in a price-fixing conspiracy network in the electrical equipment industry. Krebs [8] found that in the network consisting of the 19 hijackers, Mohamed Atta scored the highest on degree and closeness, but not on betweenness. ImplicationsEffective use of SNA techniques to mine criminal network data can have important implications for crime investigations. For example, clustering with blockmodeling can help show the hidden structure of a criminal network. The knowledge gained may aid law enforcement agencies fighting crime proactively, e.g., allocating an appropriate amount of police effort to prevent a crime’s taking place, or ensuring a police presence whe n the crime is carried out [9]. Sometimes, new structures discovered may even modify investigators’ conventional views of certain crimes. For instance, Klerks [7] has found that the stereotype of organized crime’s consisting of stable and hierarchical organizations is being replaced by an image of more fluid and flattened networks. Traditional police strategies targeting leaders of a hierarchical criminal organization, as represented by the Italian Mafia, may have become less effective in fighting organized crimes today. The work by Krebs also demonstrates that the network consisting of the19 hijackers in the September 11 attacks is fairly flat and dispersed [8]. The advantage of such a structure is an increase in the network’s resilience and an emphasis on minimizing damage should some network members be captured or compromised.SNA may also help address the challenges of data processing. Blockmodeling, for example, can easily detect “structural holes” [7] in which the link density is lower than a threshol d density value. According to McAndrew [9], structural holes may indicate incomplete or missing data thereby drawing analysts’ attention to further data collection and improvement.Data Mining Projects for Criminal Network Analysis and Visualization: The COPLINK Research TestbedSeveral data mining projects in the COPLINK research have begun to employ these SNA techniques for criminal network analysis. The goal has been to provide law enforcement and intelligence agencies with third-generation network analysis techniques that not only produce graphical representations of criminal networks but also provide structural analysis functionality to facilitate crime investigations. Prior to these data mining activities, several methods were employed to address the challenges of data processing. For inconsistency and incorrectness problems, we used the record linkage algorithm to relate multiple database records that actually refer to a single individual. For data transformation, we used the concept space approach [3] to extracting criminal associations from crime incident data.The first stage of our network analysis development was intended to automatically identify the strongest association paths, or geodesics, between two or more network members using shortest-path algorithms. In practice, such a task often entails crime analysts to manually explore links and try to find association paths that might be useful for generating investigative leads. In the user study our domain expert evaluated the paths identified automatically and those identifiedmanually. He considered the former to be useful around 70% of time and the latter to be useful around only 30% of time.Extending this attempt, a more sophisticated system for mining criminal network data has been developed. In addition to the visualization functionality, the system is intended to help detect subgroups in a network, discover interaction patterns between groups, and identify central members in a network.Based on the crime incident data provided by the Tucson Police Department, several networks consisting of criminals who were involved in different types of crimes have been created and analyzed. Several domain experts validated the results of analysis (e.g., subgroups, leaders, gatekeepers, etc.). Moreover, interesting patterns of interactions between criminal groups and network structures were revealed in the networks. For example, a network of criminals dealing with narcotic drugs and a network of gang members were found to have different structures, which became evident after cluster and blockmodel analysis (Figure 3). It appears that the chain-structure network (e.g., the narcotics network), for instance, may be disrupted by removing any part of the chain. Removing the central members from a star-structure network (e.g., the gang network) might cause fatal damage to it.To evaluate the system’s performance we conducted a laboratory experiment involving 30 (student) subjects who performed 14 investigative tasks under two experimental conditions: (a) structural analysis plus visualization (characteristics of third-generation tools), and (b) visualization only (characteristics of second-generation tools). These 14 tasks were divided into two types: identifying interaction patterns between subgroups and identifying central members within a given subgroup. Our main performance metrics were effectiveness (defined as the total number of correct answers a subject generated for a given type of tasks) and efficiency (definedas the average time a subject spent to complete a given type of tasks). The results showed the average time spent on interaction pattern identification tasks under condition (a) (7.13 seconds) was significantly shorter than that under condition (b) (12.10 seconds; t = 6.92, p < 0.001). The difference in efficiency was also significant for central member identification tasks (6.24 seconds vs. 26.93 seconds; t = 10.66, p < 0.001). Such a gain in efficiency has important implications because time is of the essence for law enforcement and intelligence agencies seeking to prevent or respond to terrorist attacks or other serious crimes. No significant improvement in effectiveness was present (t= 1.80, p> 0.05), probably due to the small sizes and relatively simple structures of the testing networks [12].Concluding RemarksIt is believed that reliable data and sophisticated analytical techniques are critical for law enforcement and intelligence agencies to understand and possibly disrupt terrorist or criminal networks. Using automated social network analysis and visualization techniques to reveal various structures and interactions within a network is a promising step forward. The continued advancement of criminal network analysis techniques will enable us finally to win the new “netwar”.Figure 3.The interaction patterns (and overall structures of networks) discovered from two criminal networks. (A) The network consisted of 60 criminals dealing with narcotic drugs. It is difficult to detect subgroups and interaction patterns from this original network manually. (B) A chain structure becomes apparent using clustering and blockmodeling (see the red mark). Circles represent groups, which are labeled by their leaders’ name, and straight lines represent between-group relationships. 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The application of network analysis to criminal intelligence: An assessmentof the prospects. Social Networks, 13, 251-274 (1991).11.Wasserman, S., and Faust, K. Social Network Analysis: Methods and Applications(Cambridge: Cambridge University Press, 1994).12.Xu, J., and Chen, H. CrimeNet explorer: A framework for criminal network knowledgediscovery. Technical Report, January 2003.Jennifer Xu(jxu@) is a doctoral candidate in Management Information Systems (MIS) at the University of Arizona, Tucson, AZ.Hsinchun Chen (hchen@) is McClelland Professor of Management Information Systems and head of Artificial Intelligence Lab at the Management Information Systems (MIS) Department at the University of Arizona, Tucson, AZ.。
