Big Data Analytics and Mining for Crime Data Analysis, Visualization and Prediction

Feng, Mingchen; Zheng, Jiangbin; Han, Yukang; Ren, Jinchang; Liu, Qiaoyuan

doi:10.1007/978-3-030-00563-4_59

Cited by 27 publications

(10 citation statements)

References 19 publications

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“…e outcomes showed that the tree classification models had performed better on classification tasks when compared with naive Bayesian methods and KNN. Holt-Winters model multiplicative seasonality provided good results when predicting the criminal tendency [31].…”

Section: Related Workmentioning

confidence: 95%

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Employing Deep Learning and Time Series Analysis to Tackle the Accuracy and Robustness of the Forecasting Problem

Tariq

Hanif

Sarwar

et al. 2021

Security and Communication Networks

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Crime is a bone of contention that can create a societal disturbance. Crime forecasting using time series is an efficient statistical tool for predicting rates of crime in many countries around the world. Crime data can be useful to determine the efficacy of crime prevention steps and the safety of cities and societies. However, it is a difficult task to predict the crime accurately because the number of crimes is increasing day by day. The objective of this study is to apply time series to predict the crime rate to facilitate practical crime prevention solutions. Machine learning can play an important role to better understand and analyze the future trend of violations. Different time-series forecasting models have been used to predict the crime. These forecasting models are trained to predict future violent crimes. The proposed approach outperforms other forecasting techniques for daily and monthly forecast.

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Section: Related Workmentioning

confidence: 95%

“…Feng et al [31] investigated crimes in Chicago, Philadelphia, and San Francisco by applying the Holt-Winters model. Firstly, the authors predicted the trend of crime in the next few years.…”

Section: Related Workmentioning

confidence: 99%

Employing Deep Learning and Time Series Analysis to Tackle the Accuracy and Robustness of the Forecasting Problem

Tariq

Hanif

Sarwar

et al. 2021

Security and Communication Networks

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“…Time series of crime data in San Francisco, Chicago and Philadelphia were used for predicting crimes in the following years. Decision Tree (DT) classification model performed better than K-nearest neighbors (KNN) and Naive Bayes (NB) ( Feng et al, 2018 ). Crime data were passed through two different data processing procedures in Canada.…”

Section: Related Workmentioning

confidence: 99%

Cyber-attack method and perpetrator prediction using machine learning algorithms

BİLEN¹,

Özer

2021

PeerJ Computer Science

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Cyber-attacks have become one of the biggest problems of the world. They cause serious financial damages to countries and people every day. The increase in cyber-attacks also brings along cyber-crime. The key factors in the fight against crime and criminals are identifying the perpetrators of cyber-crime and understanding the methods of attack. Detecting and avoiding cyber-attacks are difficult tasks. However, researchers have recently been solving these problems by developing security models and making predictions through artificial intelligence methods. A high number of methods of crime prediction are available in the literature. On the other hand, they suffer from a deficiency in predicting cyber-crime and cyber-attack methods. This problem can be tackled by identifying an attack and the perpetrator of such attack, using actual data. The data include the type of crime, gender of perpetrator, damage and methods of attack. The data can be acquired from the applications of the persons who were exposed to cyber-attacks to the forensic units. In this paper, we analyze cyber-crimes in two different models with machine-learning methods and predict the effect of the defined features on the detection of the cyber-attack method and the perpetrator. We used eight machine-learning methods in our approach and concluded that their accuracy ratios were close. The Support Vector Machine Linear was found out to be the most successful in the cyber-attack method, with an accuracy rate of 95.02%. In the first model, we could predict the types of attacks that the victims were likely to be exposed to with a high accuracy. The Logistic Regression was the leading method in detecting attackers with an accuracy rate of 65.42%. In the second model, we predicted whether the perpetrators could be identified by comparing their characteristics. Our results have revealed that the probability of cyber-attack decreases as the education and income level of victim increases. We believe that cyber-crime units will use the proposed model. It will also facilitate the detection of cyber-attacks and make the fight against these attacks easier and more effective.

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“…Neutrosophic logic and game theory analysis are playing crucial role for crime prediction. [5][6][7] Game theoretic models of data crime prediction conflict in neutrosophic logic proposed by Pradhan. 8 Smarandache 9 refers to a generic kind of logic in which each statement has distinct truth, falsehood, and indeterminacy values in Neutrosophic Logic-A Generalization of the Intuitionistic Fuzzy Logic, 2016.…”

Section: Introductionmentioning

confidence: 99%

Crime data optimization using neutrosophic logic based game theory

Remani¹,

Naresh²,

Reddi³

et al. 2022

Concurrency and Computation

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The significance of prisoner's dilemma in making two completely rational individuals might not cooperate even their best interest to do so. Every criminal investigation is strongly reliant on crime data classification and optimization. In this work, we extended prisoners' dilemma for crime data classification and its optimization. A confusion matrix-based optimization technique for crime data with game theory model in order to identify the person involvement in crime is to be predicted and clustered them into confess, not confessed, and indeterministic states based on the neutrosophic logic principle. In this technique, first we map neutrosophic logic into crime system to break the uncertainties in crime clustering. Next, we preliminary cluster the crime data pairs that indicates two offenders neutrosophic values into three clusters.However, some pairs lie in intersections of two or three clusters. So we optimize the clustering into disjoint clusters will be done based on the ratio of intra-cluster and inter-cluster distances. We have implemented the proposed method. The experimental result shows that the quality of proposed method based on accuracy, precision, and recall parameters, observed more than 90% accuracy.

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Big Data Analytics and Mining for Crime Data Analysis, Visualization and Prediction

Cited by 27 publications

References 19 publications

Employing Deep Learning and Time Series Analysis to Tackle the Accuracy and Robustness of the Forecasting Problem

Employing Deep Learning and Time Series Analysis to Tackle the Accuracy and Robustness of the Forecasting Problem

Cyber-attack method and perpetrator prediction using machine learning algorithms

Crime data optimization using neutrosophic logic based game theory

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