This paper is published in Volume-3, Issue-1, 2017
Area
Computer Science
Author
Dr. Nilakshi Jain
Co-authors
Neha Bhanushali, Sayali Gawade, Gauri Jawale
Org/Univ
Shah and Anchor Kutchhi Engineering College, Mumbai, Maharashtra, India
Pub. Date
27 April, 2017
Paper ID
V3I1-1362
Publisher
Keywords
Digitized Forensic Tools, K-means, KNN, GMAPI, cybercrime.

Citationsacebook

IEEE
Dr. Nilakshi Jain, Neha Bhanushali, Sayali Gawade, Gauri Jawale. Physical and Cyber Crime Detection using Digital Forensic Approach: A Complete Digital Forensic Tool, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Dr. Nilakshi Jain, Neha Bhanushali, Sayali Gawade, Gauri Jawale (2017). Physical and Cyber Crime Detection using Digital Forensic Approach: A Complete Digital Forensic Tool. International Journal of Advance Research, Ideas and Innovations in Technology, 3(1) www.IJARIIT.com.

MLA
Dr. Nilakshi Jain, Neha Bhanushali, Sayali Gawade, Gauri Jawale. "Physical and Cyber Crime Detection using Digital Forensic Approach: A Complete Digital Forensic Tool." International Journal of Advance Research, Ideas and Innovations in Technology 3.1 (2017). www.IJARIIT.com.

Abstract

Criminalization may be a general development that has significantly extended in previous few years. In order, to create the activity of the work businesses easy, use of technology is important. Crime investigation analysis is a section record in data mining plays a crucial role in terms of predicting and learning the criminals. In our paper, we've got planned an incorporated version for physical crime as well as cyber crime analysis. Our approach uses data mining techniques for crime detection and criminal identity for physical crimes and digitized forensic tools (DFT) for evaluating cybercrimes. The presented tool named as Comparative Digital Forensic Process tool (CDFPT) is entirely based on digital forensic model and its stages named as Comparative Digital Forensic Process Model (CDFPM). The primary step includes accepting the case details, categorizing the crime case as a physical crime or cybercrime and sooner or later storing the data in particular databases. For physical crime analysis, we've used k-means approach cluster set of rules to make crime clusters. The k-means method effects are a lot advantageous by the utilization of GMAPI generation. This provides advanced and consumer-friendly visual aid to k-means approach for tracing the region of the crime. we have applied KNN for criminal identification with the help of observing beyond crimes and finding similar ones that suit this crime, if no past document is discovered then the new crime sample are introduced to the crime dataset. With the advancements of the web, the network form has become much more complicated and attacking methods are furthermore than that as well. For crime analysis, we're detecting the attacks executed on a host system through an outside the usage of assorted digitized forensic tools to produce information security with the help of generating reports for an event which could need any investigation. Our digitized technique aids the development of the society by helping the investigation businesses to follow a custom-built investigative technique in crime analysis and criminal identification as opposed to manually looking the database to analyze criminal activities, and as a result, facilitate them in combating crimes.
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