Adaboost and SVM based cybercrime detection and prevention model

Deylami, Hanif Mohaddes; Singh, Yashwant

doi:10.5430/air.v1n2p117

Cited by 5 publications

References 28 publications

(25 reference statements)

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A review of data mining applications in crime

Hassani

Huang

Silva

et al. 2016

Statistical Analysis

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Crime continues to remain a severe threat to all communities and nations across the globe alongside the sophistication in technology and processes that are being exploited to enable highly complex criminal activities. Data mining, the process of uncovering hidden information from Big Data, is now an important tool for investigating, curbing and preventing crime and is exploited by both private and government institutions around the world. The primary aim of this paper is to provide a concise review of the data mining applications in crime. To this end, the paper reviews over 100 applications of data mining in crime, covering a substantial quantity of research to date, presented in chronological order with an overview table of many important data mining applications in the crime domain as a reference directory. The data mining techniques themselves are briefly introduced to the reader and these include entity extraction, clustering, association rule mining, decision trees, support vector machines, naive Bayes rule, neural networks and social network analysis amongst others

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A review of data mining applications in crime

Hassani

Huang

Silva

et al. 2016

Statistical Analysis

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Crime Data Mining, Threat Analysis and Prediction

Farsi

Daneshkhah

Hosseinian-Far

et al. 2018

Advanced Sciences and Technologies for Security Applications

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the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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On the Advancement of Using Data Mining for Crime Situation Recognition

Isafiade

Bagula

Berman

Advances in Data Mining and Database Management

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The primary role of intelligence organisations and public safety agencies encompasses protecting the lives and property of citizens. However, the urban population growth rate tends to overshadow the available security resources. Thus, the security agencies appear to be more reactive than proactive. Public safety agencies usually have a plethora of under-utilised crime incident reports at their disposal, which if efficiently analysed could reveal some previously unknown useful information. Such information reveals insights into a range of functions in a crime investigation, which can assist in determining criminal trends and in knowledge-driven decision support. This chapter provides an overview of data mining techniques and existing applications used in this domain of interest. Features of existing applications and techniques, such as exploratory basis, model selection, algorithm advancement and result summary, are compared. Future potential of crime data mining, and open research issues, are also discussed.

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Adaboost and SVM based cybercrime detection and prevention model

Cited by 5 publications

References 28 publications

A review of data mining applications in crime

A review of data mining applications in crime

Crime Data Mining, Threat Analysis and Prediction

On the Advancement of Using Data Mining for Crime Situation Recognition

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