A Proposed Model for Cybercrime Detection Algorithm Using A Big Data Analytics Hossam Abdel Rahaman Dept of Computer and Information Sciences Faculty of Statistical Studies and Research Cairo University, Egypt Hossam_mm7@yahoo.com Abstract— Cybercrime today is evolving as part of our day-to- day lives, and The challenges of cybercrime reduction and prevention are becoming increasingly complex, that needs a new technique to handle the vast amount of this data, The capabilities of the traditional activities of police mostly drop brief in portraying the original division of criminal activities, hence contribute less to the appropriate allotment of police services. In this paper, methods are described for cybercrime Prediction, by using the Hadoop technique for big data analytics, through examining the geological zones which incorporate more noteworthy chance and exterior the conventional policing capabilities. The used method makes the utilize of a topographical cybercrime mapping algorithm to distinguish regions that have generally high cases of cybercrime. This method will identify exceedingly cases of cybercrime clusters which assist can show the patterns of cybercrime. the estimation approach is enhanced by the processing capability of the Hadoop platform. Keywords-component; formatting; style; styling; insert (key words) I. INTRODUCTION Cybercrimes are getting increased with expanding dangers through online fraud and unscrupulous hacking. With both cyber safety threats and data increasing, the organizations must be prepared to prepare themselves with foreseeing and anticipating cybercrime. the specialists of cybercrime are using digital Forensic tools to identify cybercrime episodes and recognize any potential threats like credit card frauds. Big data analytics is empowering companies to analyze the gigantic sum of information they collect amid the monetary transactions; cybercrime could be a greater significance nowadays due to the increased risk of cybercrime. Big data tools are being utilized to combat cybercrime attacks. big data analytics can offer to detect forgery and can facilitate digital forensic analysis. [1] The utilize of K-Mean algorithms to analyze the data and predict where cybercrime is likely to happen is getting to be more common in law authorization. Frequently referred to as predictive analysis, which gets to be the police agency's successes to cybercrime reduction efforts by applying the predictive investigation. [2] The detection algorithm presented in this paper has three stages as appeared in Figure 1. The first phase is the distribution geographic of cybercrime data analysis which identifies spatial clusters that have a greater risk of cybercrime. In the second phase a K-Mean clustering algorithm that utilized to determine the quality of each identified cluster. [3] Figure 1. Predictive Process This paper delineates a cybercrime detection algorithm on the Hadoop platform in big data analytics that will be able to predict the near likely cybercrime. also, a brief overview is made about several techniques utilized in analyzing big data to detect online fraud and unethical hacking by analyzing large sets of data. One aim of this study is to identify the model that best identifies online fraud cases. [4] A. Problem Statement The predictive of big data analysis has not been broadly examined and studied from an objective, perspective scientific. Whereas beginning experiences by the police agencies that have either fully implemented or experimented with predictive policing techniques appear to be positive, predictive policing’s affecting on cybercrime has yet to be definitively determined. this problem is troublesome because the utilize of predictive analysis in policing is so modern that small objective research has been conducted on its cybercrime reduction applications. [5] B. Challenges Of Research • The distinctive techniques and infrastructures that are used for recording data on cybercrime. • The diverse techniques that can analyze with precision and efficiency for this expanding volume of data on cybercrime. • The accessible data are inconsistent and fragmented are making the task increasingly difficult formal analysis International Journal of Computer Science and Information Security (IJCSIS), Vol. 18, No. 6, June 2020 146 https://sites.google.com/site/ijcsis/ ISSN 1947-5500