A review: Crime analysis using data mining techniques and algorithms

Chauhan, Chhaya; Sehgal, Smriti

doi:10.1109/ccaa.2017.8229823

Cited by 30 publications

(11 citation statements)

References 8 publications

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“…Chauhan et al [27], presented a review report on crime analysis using data mining techniques. It discussed how the data mining approaches like k-means algorithm, random forest, etc.…”

Section: Related Workmentioning

confidence: 99%

Computational System to Classify Cyber Crime Offenses using Machine Learning

et al. 2020

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Particularly in the last decade, Internet usage has been growing rapidly. However, as the Internet becomes a part of the day to day activities, cybercrime is also on the rise. Cybercrime will cost nearly $6 trillion per annum by 2021 as per the cybersecurity ventures report in 2020. For illegal activities, cybercriminals utilize any network computing devices as a primary means of communication with a victims’ devices, so attackers get profit in terms of finance, publicity and others by exploiting the vulnerabilities over the system. Cybercrimes are steadily increasing daily. Evaluating cybercrime attacks and providing protective measures by manual methods using existing technical approaches and also investigations has often failed to control cybercrime attacks. Existing literature in the area of cybercrime offenses suffers from a lack of a computation methods to predict cybercrime, especially on unstructured data. Therefore, this study proposes a flexible computational tool using machine learning techniques to analyze cybercrimes rate at a state wise in a country that helps to classify cybercrimes. Security analytics with the association of data analytic approaches help us for analyzing and classifying offenses from India-based integrated data that may be either structured or unstructured. The main strength of this work is testing analysis reports, which classify the offenses accurately with 99 percent accuracy.

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“…Chauhan et al [27], presented a review report on crime analysis using data mining techniques. It discussed how the data mining approaches like k-means algorithm, random forest, etc.…”

Section: Related Workmentioning

confidence: 99%

Computational System to Classify Cyber Crime Offenses using Machine Learning

et al. 2020

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“…They emphasize on the various factor of spatial and temporal data that a crime analyst should consider while analyzing the situation. Again, data mining approaches are considered vital for crime analysis [20] [46], [47], [52]. They discussed predictive policing using analytical and predicting to identify criminals.…”

Section: Related Workmentioning

confidence: 99%

Spatio-Temporal Crime HotSpot Detection and Prediction: A Systematic Literature Review

et al. 2020

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The primary objective of this study is to accumulate, summarize, and evaluate the state-ofthe-art for spatio-temporal crime hotspot detection and prediction techniques by conducting a systematic literature review (SLR). The authors were unable to find a comprehensive study on crime hotspot detection and prediction while conducting this SLR. Therefore, to the best of author's knowledge, this study is the premier attempt to critically analyze the existing literature along with presenting potential challenges faced by current crime hotspot detection and prediction systems. The SLR is conducted by thoroughly consulting top five scientific databases (such as IEEE, Science Direct, Springer, Scopus, and ACM), and synthesized 49 different studies on crime hotspot detection and prediction after critical review. This study unfolds the following major aspects: 1) the impact of data mining and machine learning approaches, especially clustering techniques in crime hotspot detection; 2) the utility of time series analysis techniques and deep learning techniques in crime trend prediction; 3) the inclusion of spatial and temporal information in crime datasets making the crime prediction systems more accurate and reliable; 4) the potential challenges faced by the state-of-the-art techniques and the future research directions. Moreover, the SLR aims to provide a core foundation for the research on spatio-temporal crime prediction applications while highlighting several challenges related to the accuracy of crime hotspot detection and prediction applications.

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“…Based on the possibilities offered by the evolution of technologies, especially Big Data analytics, representational models, semantic reasoning Francisco J. Pérez frapecar@upvnet.upv.es Extended author information available on the last page of the article. and augmented intelligence, MAGNETO [23] will contribute to counteracting this threat to society [4,14].…”

Section: Introductionmentioning

confidence: 99%

Multimedia analysis platform for crime prevention and investigation

Pérez

Garrido

García-García

et al. 2021

Multimed Tools Appl

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Nowadays,the use of digital technologies is promoting three main characteristics of information, i.e. the volume, the modality and the frequency. Due to the amount of information generated by tools and individuals, it has been identified a critical need for the Law Enforcement Agencies to exploit this information and carry out criminal investigations in an effective way. To respond to the increasing challenges of managing huge amounts of heterogeneous data generated at high frequency, the paper outlines a modular approach adopted for the processing of information gathered from different information sources, and the extraction of knowledge to assist criminal investigation. The proposed platform provides novel technologies and efficient components for processing multimedia information in a scalable and distributed way, allowing Law Enforcement Agencies to make the analysis and a multidimensional visualization of criminal information in a single and secure point.

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A review: Crime analysis using data mining techniques and algorithms

Cited by 30 publications

References 8 publications

Computational System to Classify Cyber Crime Offenses using Machine Learning

Computational System to Classify Cyber Crime Offenses using Machine Learning

Spatio-Temporal Crime HotSpot Detection and Prediction: A Systematic Literature Review

Multimedia analysis platform for crime prevention and investigation

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