Cybercrime Analysis and Data Mining Methodologies
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International Journal on Advanced Computer Theory and Engineering (IJACTE) _______________________________________________________________________________________________ Cybercrime Analysis and Data Mining Methodologies 1Deepti Gaur, 2Neha Aggarwal Department of Computer Science & Information Technology ITM University, Gurgaon, Haryana, India. Email: [email protected] critical, new and special problems of crime, although the Abstract— In this paper authors presented about the crime data mining a latest emerging area in the field of crime problem is as old as man himself. In addition to information security. Paper also include the complete this, the techniques employed to commit crime are new survey of all the mining methodologies available along with in the sense that they make use of modern knowledge the of data mining steps involved in the crime. Crime can and technique. The rise in crime both national and be national or international but its always a distractive international is generally thought as the result of process in the society. interplay between socio-economic changes. The Index Terms—Crime Data Mining, Precision, Recall, circumstances surrounding the individual offender such Hotspots, Techniques ,CRISP-DM methodology. as his personality, physical characteristics intelligence, family background, environmental surrounding such as I. INTRODUCTION peer groups, neighbors etc have been subject of the Crime is identified as an act which is punishable by study of crime. (Andargachew, 1988). So by, legislation in accordance with Thakur[9]. However, an understanding the attributes of criminals will be helpful act that is considered as a crime in one place and time to design and implement proper crime prevention may not be true in another place or time. According to strategies. The Governments usually establish Andargachew (1988), a criminal is an individual person organizations such as courts, prosecutions and police, who has violated the legally forbidden act. In fact, there which are responsible for the maintenance of law and are some factors that have to be taken into account to order in their respective country. These agencies and convict whether a person should be considered as a other related organizations are responsible to curb the criminal or not. Among these, an individual should be of rate and occurrence of crimes. The crime prevention competent age in light with the law ; and there must be a agencies need to issue and implement crime prevention well-predefined punishment for the particular act strategies[8]: committed. Prevention safeguards the life and property of Offense has increasingly become as complex as human the society whom the authorities are in duty to nature. Contemporary technological improvement and protect. huge development in communication have facilitated It avoids much of difficulty to the prey equally criminals of every place of the planet to spend a crime bodily and mental. applying advanced equipment in one single place and then escape to a different place[9]. Now adays the globe Crime elimination rules out litigation, which is facing the proliferation of problems such as for follows along the way of sensing a crime. example illicit drug trafficking, smuggling, hijacking, Prevention also saves the authorities from the kidnapping, and terrorism. difficulty of producing crime at all strange The level of crime also depends upon the situation and hours of the afternoon and evening and of using also varies from state to state . immediate activity for the investigation. Crime Prevention II. DATA MINIG The causes for the growing rate of crime include Data Mining could be the computational procedure for unemployment, economic backwardness, over exploring patterns in large information sets involving population, illiteracy and inadequate equipment of the practices at the junction of synthetic intelligence, police force. The form of seriousness and size of the machine understanding, data, and repository programs. crime, may rely on the form of a society and thus its The entire goal of the info mining process is always to nature changes with the growth and development of the remove information from a information collection and social system. In every generation it has its own most convert it in to an understandable framework for further _______________________________________________________________________________________________ ISSN (Print): 2319-2526, Volume -3, Issue -4, 2014 37 International Journal on Advanced Computer Theory and Engineering (IJACTE) _______________________________________________________________________________________________ use. Besides the organic examination step, it requires B. Empty Grid Cells repository and information management aspects, Empty grid cells need to be taken from the datasets information pre-processing, product and inference because they have a detrimental yet counter instinctive considerations, interestingness metrics, complexity area effect. They enhance the efficiency of the considerations, post-processing of found structures, classifiers. It is simple for almost any given classifier to visualization, and on the web updating. precisely estimate that nothing may happen in an empty The actual information mining job could be the grid cell. That ―intelligence‖ is really artificial. An automated or semi-automatic examination of large empty grid cell is defined as missing any rely for the amounts of information to remove previously unknown reason that cell in some of the investigated classes interesting patterns such as for instance categories of around the entire schedule being analyzed. Many empty information records (cluster examination), unusual grid cells have two explanations. One, the limits of the records (anomaly detection) and dependencies city aren't rectangular like the grid getting used is, and (association concept mining). That usually requires two, there are many places within the city limits such as applying repository methods such as spatial indices. for example airport runways, bodies of water, and These patterns will then be seen as a kind of overview of community start spaces wherever these activities only the feedback information, and may be used in further don't happen. The result is empty grid cells that have to examination, for instance, in machine understanding and be removed[5][6]. predictive analytics. For example, the info mining step 2. Handling Information may recognize multiple communities in the info, which will then be properly used to acquire more precise One challenge in offense prediction, just like different prediction effects by a choice support program. Neither unusual occasion prediction, is that locations and cool the information variety, information preparation, or places are unbalanced. That's cool places are a whole lot effect model and revealing are part of the information more widespread than hotspots. Inside our dataset, that mining step, but do fit in with the general KDD process is especially true with the bigger quality 41-by-40 grid. as additional steps[3][7]. This research paper contain the It has the consequence of puzzling the necessary following sections: Data Generation that describes the measures of detail, recall, and F1. In particular, the F1 data set ; Handling of information; techniques involved report of locations is far less than the F1 report of cool in Data Mining. places as the classifiers are properly qualified on cool spots. The computation on F1 report inside our examine 1. Data Generation is defined the following: The research data was gleaned from multiple cities F1= (2*precision*Recall)/ Precision + Recall agencies. Every real data entry is a record for an crime or related event. Each record contains the type of crime , Where, the location of crime in longitude and latitude, and time Precision = TP / (TP+FP) - date of the crime incident happened . Before beginning with data mining , a preprocessing is required to make it Recall = TP / (TP+FN ) suitable for classification. Where, A. Data Grid TP= predicts the true Hotspots i.e., no. Of true positives For the deployment of this crime prediction model the police-department requirement is to forecast the crime FP= predicts the false Hotspots i.e., no. Of false such as residential burglary over space and time. positives Accordingly, across a uniform grid the model classifies FN= predicts the false Coldspots i.e., no. Of false burglaries monthly. The city is divided into negatives checkerboard-like cells by the help of grid. Now each cell contain data combined into six categories namely To solve this matter, we adjust the weight of hotspots Arrest, Residential Burglary[4], Commercial Burglary , and cold spots. By raising the weight of hotspots on the Motor Vehicle Larceny and Street Robbery, basis of the proportion between hotspots and coldspots, Foreclosure. On a monthly basis each cell is populated. the information set may be balanced ahead of the The researched data was of two resolutions . The first classification process. The weight function is identified measure is 24-by-20 square grid cells and the other by these: measure is 41-by-40. The cells in the 24-by-20 grid measure distance is one-half mile square. In 41-by-40 grid, the distance measure is over one-quarter mile square. In both cases, data set is a matrix on monthly basis of the six earlier mentioned categories. The two where, resolutions as finer resolution make grid to be interrogated with more detail toward the inherent spatial C = Total number of coldspots