没有合适的资源?快使用搜索试试~ 我知道了~
首页大数据分析在内部威胁检测中的应用
大数据分析在内部威胁检测中的应用
需积分: 9 8 下载量 35 浏览量
更新于2024-07-18
1
收藏 74.93MB PDF 举报
"《大数据分析在内部威胁检测中的应用》是一本由Bhavani Thuraisingham、Pallabi Parveen和Mehedy Masud、Latifur Khan合著的专业书籍,针对当前IT安全领域的一个重要挑战——内部威胁检测进行了深入探讨。随着现代恶意软件的进化,它们变得越来越智能,通过随机变异以躲避传统的反病毒软件检测。这种被称为反应性适应性恶意软件(reactively adaptive malware)的新型威胁,利用与反病毒软件相似的算法,不断学习和适应新的计算机防御策略,从而得以绕过检测。
本书详述了用于对抗这种新型威胁的工具和技术细节,包括这些工具能够识别的各种类型的恶意软件。作者重点介绍了如何在云计算环境中实施这些工具,以提升检测效率和覆盖范围。此外,书中还深入讨论了大数据分析在这个框架下的应用,因为大数据能够提供丰富的数据源,帮助发现潜在的内部威胁行为模式。
内部威胁,如员工滥用权限或恶意泄露信息,是企业信息安全的关键关注点。这本书通过大数据分析技术,展示了如何通过监控和分析海量数据,来提早识别出可能的内部威胁信号,从而采取预防措施或及时响应。这不仅有助于保护企业的关键信息资产,也对提高整体网络安全水平具有重要意义。
总结来说,《大数据分析在内部威胁检测中的应用》是一本实用的指南,它结合了最新的IT技术和数据分析方法,旨在帮助企业和组织更好地理解和应对内部威胁,确保数据安全和业务连续性。对于IT专业人士和安全管理人员来说,这是一本不可或缺的参考资料,对于理解当今复杂威胁环境下的防护策略至关重要。"
![](https://csdnimg.cn/release/download_crawler_static/10572212/bg10.jpg)
xvContents
23.5.5 Breaking Ties by Summary Statistics .............................................. 277
23.5.6 MapReduce Join Execution ..............................................................278
23.6 Results ...........................................................................................................279
23.6.1 Experimental Setup .......................................................................... 279
23.6.2 Evaluation .........................................................................................280
23.7 Security Extensions ....................................................................................... 281
23.7.1 Access Control Model ...................................................................... 282
23.7.2 Access Token Assignment ................................................................283
23.7.3 Conicts ...........................................................................................284
23.8 Summary and Directions ...............................................................................285
References ................................................................................................................286
Chapter 24 Big Data Analytics for Multipurpose Social Media Applications ...........................289
24.1 Introduction ...................................................................................................289
24.2 Our Premise ...................................................................................................290
24.3 Modules of Inxite .......................................................................................... 291
24.3.1 Overview .......................................................................................... 291
24.3.2 Information Engine .......................................................................... 291
24.3.2.1 Entity Extraction ...............................................................292
24.3.2.2 Information Integration ....................................................293
24.3.3 Person of Interest Analysis ...............................................................293
24.3.3.1 InXite Person of Interest Prole Generation and
Analysis ............................................................................293
24.3.3.2 InXite POI Threat Analysis ..............................................294
24.3.3.3 InXite Psychosocial Analysis ...........................................296
24.3.3.4 Other features ...................................................................297
24.3.4 InXite Threat Detection and Prediction ...........................................298
24.3.5 Application of SNOD .......................................................................300
24.3.5.1 SNOD++ ..................................................................300
24.3.5.2 Benets of SNOD++ ..................................................300
24.3.6 Expert Systems Support ...................................................................300
24.3.7 Cloud-Design of Inxite to Handle Big Data .....................................301
24.3.8 Implementation .................................................................................302
24.4 Other Applications.........................................................................................302
24.5 Related Work .................................................................................................303
24.6 Summary and Directions ...............................................................................304
References ................................................................................................................304
Chapter 25 Big Data Management and Cloud for Assured Information Sharing .......................307
25.1 Introduction ...................................................................................................307
25.2 Design Philosophy ......................................................................................... 308
25.3 System Design ...............................................................................................309
25.3.1 Design of CAISS .............................................................................. 309
25.3.2 Design of CAISS++ ................................................................. 312
25.3.2.1 Limitations of CAISS ....................................................... 312
25.3.3 Formal Policy Analysis .................................................................... 321
25.3.4 Implementation Approach ................................................................ 321
25.4 Related Work ................................................................................................. 321
![](https://csdnimg.cn/release/download_crawler_static/10572212/bg11.jpg)
xvi Contents
25.4.1 Our Related Research .......................................................................322
25.4.2 Overall Related Research ................................................................. 324
25.4.3 Commercial Developments ..............................................................326
25.5 Extensions for Big Data-Based Social Media Applications .......................... 326
25.6 Summary and Directions ............................................................................... 327
References ................................................................................................................ 327
Chapter 26 Big Data Management for Secure Information Integration ..................................... 331
26.1 Introduction ................................................................................................... 331
26.2 Integrating Blackbook with Amazon s3 ........................................................ 331
26.3 Experiments ................................................................................................... 336
26.4 Summary and Directions ............................................................................... 336
References ................................................................................................................ 336
Chapter 27 Big Data Analytics for Malware Detection .............................................................. 339
27.1 Introduction ...................................................................................................339
27.2 Malware Detection ........................................................................................340
27.2.1 Malware Detection as a Data Stream Classication Problem ..........340
27.2.2 Cloud Computing for Malware Detection ........................................ 341
27.2.3 Our Contributions ............................................................................. 341
27.3 Related Work ................................................................................................. 342
27.4 Design and Implementation of the System .................................................... 344
27.4.1 Ensemble Construction and Updating ..............................................344
27.4.2 Error Reduction Analysis .................................................................344
27.4.3 Empirical Error Reduction and Time Complexity ........................... 345
27.4.4 Hadoop/MapReduce Framework .....................................................345
27.5 Malicious Code Detection ............................................................................. 347
27.5.1 Overview ..........................................................................................347
27.5.2 Nondistributed Feature Extraction and Selection ............................347
27.5.3 Distributed Feature Extraction and Selection ..................................348
27.6 Experiments ...................................................................................................349
27.6.1 Datasets ............................................................................................349
27.6.2 Baseline Methods ............................................................................. 350
27.7 Discussion ...................................................................................................... 351
27.8 Summary and Directions ............................................................................... 352
References ................................................................................................................ 353
Chapter 28 A Semantic Web-Based Inference Controller for Provenance Big Data .................. 355
28.1 Introduction ................................................................................................... 355
28.2 Architecture for the Inference Controller ...................................................... 356
28.3 Semantic Web Technologies and Provenance ...............................................360
28.3.1 Semantic Web-Based Models ...........................................................360
28.3.2 Graphical Models and Rewriting ..................................................... 361
28.4 Inference Control through Query Modication ............................................ 361
28.4.1 Our Approach ................................................................................... 361
28.4.2 Domains and Provenance ................................................................. 362
28.4.3 Inference Controller with Two Users ...............................................363
28.4.4 SPARQL Query Modication ..........................................................364
![](https://csdnimg.cn/release/download_crawler_static/10572212/bg12.jpg)
xviiContents
28.5 Implementing the Inference Controller .........................................................365
28.5.1 Our Approach ...................................................................................365
28.5.2 Implementation of a Medical Domain .............................................365
28.5.3 Generating and Populating the Knowledge Base .............................366
28.5.4 Background Generator Module ........................................................ 366
28.6 Big Data Management and Inference Control ...............................................367
28.7 Summary and Directions ...............................................................................368
References ................................................................................................................368
Conclusion to Part IV ............................................................................................... 373
PART V Next Steps for BDMA and BDSP
Introduction to Part V .............................................................................................. 377
Chapter 29 Condentiality, Privacy, and Trust for Big Data Systems ........................................ 379
29.1 Introduction ...................................................................................................379
29.2 Trust, Privacy, and Condentiality ................................................................ 379
29.2.1 Current Successes and Potential Failures ........................................380
29.2.2 Motivation for a Framework ............................................................. 381
29.3 CPT Framework ............................................................................................ 381
29.3.1 The Role of the Server ...................................................................... 381
29.3.2 CPT Process .....................................................................................382
29.3.3 Advanced CPT ................................................................................. 382
29.3.4 Trust, Privacy, and Condentiality Inference Engines .................... 383
29.4 Our Approach to Condentiality Management ............................................. 384
29.5 Privacy for Social Media Systems ................................................................. 385
29.6 Trust for Social Networks ..............................................................................387
29.7 Integrated System .......................................................................................... 387
29.8 CPT within the Context of Big Data and Social Networks ........................... 388
29.9 Summary and Directions ...............................................................................390
References ................................................................................................................390
Chapter 30 Unied Framework for Secure Big Data Management and Analytics ..................... 391
30.1 Overview ....................................................................................................... 391
30.2 Integrity Management and Data Provenance for Big Data Systems ............. 391
30.2.1 Need for Integrity ............................................................................. 391
30.2.2 Aspects of Integrity ..........................................................................392
30.2.3 Inferencing, Data Quality, and Data Provenance ............................. 393
30.2.4 Integrity Management, Cloud Services and Big Data ......................394
30.2.5 Integrity for Big Data .......................................................................396
30.3 Design of Our Framework ............................................................................. 397
30.4 The Global Big Data Security and Privacy Controller .................................. 400
30.5 Summary and Directions ............................................................................... 401
References ................................................................................................................ 401
Chapter 31 Big Data, Security, and the Internet of Things ........................................................403
31.1 Introduction ...................................................................................................403
![](https://csdnimg.cn/release/download_crawler_static/10572212/bg13.jpg)
xviii Contents
31.2 Use Cases .......................................................................................................404
31.3 Layered Framework for Secure IoT ............................................................... 406
31.4 Protecting the Data ........................................................................................ 407
31.5 Scalable Analytics for IoT Security Applications .........................................408
31.6 Summary and Directions ............................................................................... 411
References ................................................................................................................ 411
Chapter 32 Big Data Analytics for Malware Detection in Smartphones.................................... 413
32.1 Introduction ................................................................................................... 413
32.2 Our Approach ................................................................................................ 414
32.2.1 Challenges ........................................................................................ 414
32.2.2 Behavioral Feature Extraction and Analysis .................................... 415
32.2.2.1 Graph-Based Behavior Analysis ...................................... 415
32.2.2.2 Sequence-Based Behavior Analysis ................................. 416
32.2.2.3 Evolving Data Stream Classication ............................... 416
32.2.3 Reverse Engineering Methods ......................................................... 417
32.2.4 Risk-Based Framework .................................................................... 417
32.2.5 Application to Smartphones ............................................................. 418
32.2.5.1 Data Gathering ................................................................. 419
32.2.5.2 Malware Detection ........................................................... 419
32.2.5.3 Data Reverse Engineering of Smartphone
Applications .................................................................... 419
32.3 Our Experimental Activities.......................................................................... 419
32.3.1 Covert Channel Attack in Mobile Apps ...........................................420
32.3.2 Detecting Location Spoong in Mobile Apps .................................420
32.3.3 Large Scale, Automated Detection of SSL/TLS
Man-in-the-Middle Vulnerabilities in Android Apps ...................... 421
32.4 Infrastructure Development ........................................................................... 421
32.4.1 Virtual Laboratory Development ..................................................... 421
32.4.1.1 Laboratory Setup ............................................................. 421
32.4.1.2 Programming Projects to Support the Virtual Lab .........423
32.4.1.3 An Intelligent Fuzzier for the Automatic Android
GUIApplication Testing ................................................. 423
32.4.1.4 Problem Statement ........................................................... 423
32.4.1.5 Understanding the Interface.............................................423
32.4.1.6 Generating Input Events...................................................424
32.4.1.7 Mitigating Data Leakage in Mobile Apps Using a
Transactional Approach ..................................................424
32.4.1.8 Technical Challenges ....................................................... 425
32.4.1.9 Experimental System ....................................................... 425
32.4.1.10 Policy Engine ................................................................... 426
32.4.2 Curriculum Development ................................................................. 426
32.4.2.1 Extensions to Existing Courses .......................................426
32.4.2.2 New Capstone Course on Secure Mobile Computing ..... 428
32.5 Summary and Directions ............................................................................... 429
References ................................................................................................................ 429
Chapter 33 Toward a Case Study in Healthcare for Big Data Analytics and Security ............... 433
33.1 Introduction ................................................................................................... 433
![](https://csdnimg.cn/release/download_crawler_static/10572212/bg14.jpg)
xixContents
33.2 Motivation ...................................................................................................... 433
33.2.1 The Problem ..................................................................................... 433
33.2.2 Air Quality Data ............................................................................... 435
33.2.3 Need for Such a Case Study ............................................................. 435
33.3 Methodologies ............................................................................................... 436
33.4 The Framework Design ................................................................................. 437
33.4.1 Storing and Retrieving Multiple Types of Scientic Data ............... 437
33.4.1.1 The Problem and Challenges ............................................ 437
33.4.1.2 Current Systems and Their Limitations ........................... 438
33.4.1.3 The Future System ............................................................ 439
33.4.2 Privacy and Security Aware Data Management for
Scientic Data ..................................................................................440
33.4.2.1 The Problem and Challenges ............................................ 440
33.4.2.2 Current Systems and Their Limitations ...........................440
33.4.2.3 The Future System ............................................................441
33.4.3 Ofine Scalable Statistical Analytics...............................................442
33.4.3.1 The Problem and Challenges ............................................442
33.4.3.2 Current Systems and Their Limitations ...........................443
33.4.3.3 The Future System ............................................................ 444
33.4.3.4 Mixed Continuous and Discrete Domains .......................444
33.4.4 Real-Time Stream Analytics ............................................................446
33.4.4.1 The Problem and Challenges ............................................ 446
33.4.5 Current Systems and Their Limitations ...........................................446
33.4.5.1 The Future System ............................................................ 446
33.5 Summary and Directions ...............................................................................448
References ................................................................................................................448
Chapter 34 Toward an Experimental Infrastructure and Education Program for BDMA
and BDSP ................................................................................................................. 453
34.1 Introduction ................................................................................................... 453
34.2 Current Research and Infrastructure Activities in BDMA
and BDSP ...................................................................................................... 454
34.2.1 Big Data Analytics for Insider Threat Detection ............................. 454
34.2.2 Secure Data Provenance ...................................................................454
34.2.3 Secure Cloud Computing .................................................................454
34.2.4 Binary Code Analysis ...................................................................... 455
34.2.5 Cyber-Physical Systems Security ..................................................... 455
34.2.6 Trusted Execution Environment ....................................................... 455
34.2.7 Infrastructure Development ............................................................. 455
34.3 Education and Infrastructure Program in BDMA ......................................... 455
34.3.1 Curriculum Development ................................................................. 455
34.3.2 Experimental Program ..................................................................... 457
34.3.2.1 Geospatial Data Processing on GDELT ...........................458
34.3.2.2 Coding for Political Event Data ........................................ 458
34.3.2.3 Timely Health Indicator ....................................................459
34.4 Security and Privacy for Big Data ................................................................. 459
34.4.1 Our Approach ...................................................................................459
34.4.2 Curriculum Development .................................................................460
34.4.2.1 Extensions to Existing Courses ........................................460
34.4.2.2 New Capstone Course on BDSP ....................................... 461
剩余578页未读,继续阅读
2023-06-02 上传
2023-04-11 上传
2023-07-28 上传
2023-04-25 上传
2023-06-02 上传
2023-06-19 上传
2023-08-07 上传
2023-10-11 上传
2023-05-10 上传
![](https://profile-avatar.csdnimg.cn/default.jpg!1)
wang1062807258
- 粉丝: 13
- 资源: 272
上传资源 快速赚钱
我的内容管理 展开
我的资源 快来上传第一个资源
我的收益
登录查看自己的收益我的积分 登录查看自己的积分
我的C币 登录后查看C币余额
我的收藏
我的下载
下载帮助
![](https://csdnimg.cn/release/wenkucmsfe/public/img/voice.245cc511.png)
最新资源
- 多模态联合稀疏表示在视频目标跟踪中的应用
- Kubernetes资源管控与Gardener开源软件实践解析
- MPI集群监控与负载平衡策略
- 自动化PHP安全漏洞检测:静态代码分析与数据流方法
- 青苔数据CEO程永:技术生态与阿里云开放创新
- 制造业转型: HyperX引领企业上云策略
- 赵维五分享:航空工业电子采购上云实战与运维策略
- 单片机控制的LED点阵显示屏设计及其实现
- 驻云科技李俊涛:AI驱动的云上服务新趋势与挑战
- 6LoWPAN物联网边界路由器:设计与实现
- 猩便利工程师仲小玉:Terraform云资源管理最佳实践与团队协作
- 类差分度改进的互信息特征选择提升文本分类性能
- VERITAS与阿里云合作的混合云转型与数据保护方案
- 云制造中的生产线仿真模型设计与虚拟化研究
- 汪洋在PostgresChina2018分享:高可用 PostgreSQL 工具与架构设计
- 2018 PostgresChina大会:阿里云时空引擎Ganos在PostgreSQL中的创新应用与多模型存储
安全验证
文档复制为VIP权益,开通VIP直接复制
![](https://csdnimg.cn/release/wenkucmsfe/public/img/green-success.6a4acb44.png)