NDLI: Computerized Forensic Approach Using Data Mining Techniques

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Computerized Forensic Approach Using Data Mining Techniques

Content Provider	ACM Digital Library
Author	Bhosale, Apoorva Anchan, Rashmi Sharma, Priyanka Anchan, Pooja Jain, Nilakshi Kalbande, Dhananjay
Abstract	Criminal activities are a manifestation of unseen termites that are slowly but steadily decaying the deep rooted pillars of ethics and values established in our society. The evolving technology can be very well be utilized as arms and ammunitions by the law agencies against this social evil of criminalization. In our paper, we propose a novel and unified approach to examine and investigate digital crimes as well as physical crimes. Our model works on the principle of integrating various computerized forensic tools to analyze the reported digital crime and adopts data mining techniques for detecting crime and predicting the criminal in the case of physical crimes. In the first phase the user registered in the system can file a valid case by entering the details of the crime occurred. Depending on the type of crime the case will be evaluated. For detecting and investigating intruder attacks launched on a user's system, a set of digital tools is used and the generated report is sent to the intended user. In the event of a physical crime, k-means clustering algorithm is used to generate crime clusters. Based on the crime location the clusters are diagrammatically represented on google maps. We have further incorporated the use of Naïve Bayes classification algorithm for predicting the criminals for a particular crime case based on similar crime activities that happened in the past. If no previous record is found then the new crime pattern is added to the existing crime dataset. Our computerized forensic model aids the victim to amicably cooperate with the law agencies and aims to accelerate the process of crime investigation in order to combat rapidly growing criminal activities.
Starting Page	55
Ending Page	60
Page Count	6
File Format	PDF
ISBN	9781450342780
DOI	10.1145/2909067.2909076
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-03-21
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Naïve bayesian K-means Digitized forensic tools Google maps Classification Clustering Data mining
Content Type	Text
Resource Type	Article

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