R. Bulli Babu ¹, Talatam Durga Rao ¹, Tummala Harika ¹

Affiliations
1 Department of Electronics and Computer Engineering, KL University, Guntur District - 522502, Andhra Pradesh, IN

Abstract

Objective: The main motto of this work is to trace the cyber fraud people and moreover we had extended this work in multiple way usage in the forensic mode. Method: So in this method let us imagine a scenario in which something incorrectly happen on the server and on the off chance that if an association will misfortune some kind of information (means if an association will hack and misfortune some critical information. Yes this may happen, it may happen in light of the fact that an assailant more keen than an infiltration analyzer. So after this the time is to get the programmer, for this reason you require a scientific device, so in this article we will talk about DEFT Linux a complete distro for measurable purposes. Findings: The Digital Evidence and Forensic Toolkit or DEFT Linux, is a live distro that gives devices to investigating PC frameworks and gathering criminological confirmation. The distro contains apparatuses for scanning and breaking down circle drives, databases and system movement of Windows and Linux frameworks. Application: DEFT incorporates instruments to bolster Incident Response, Cyber Intelligence and Computer Forensics. It incorporates WINE and the Digital Advanced Response Toolkit (DART). The essential center of the distro is the precise and dependable gathering of proof for use in criminal procedures. Thus, most DEFT devices are arranged to gathering data without changing the framework being analyzed. And more over we had used FTK imager and Winhex, data recovery tools to find suspect. So, finally in this paper we are going to test an unapproved information with a planned results.

Keywords

DART, DEFT, Computer Forensic, Cyber Intelligence, Penetration.

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R. Bulli Babu ¹, G. Snehal ¹, P. Aditya Satya Kiran ¹

Affiliations
1 Department of Electronics and Computer Engineering, K. L. University, Guntur - 522502, Andhra Pradesh, IN

Abstract

Objectives: The main objective of this paper is to solve the criminal problems with in less amount of time. There are many methods to do so but this paper concentrates in solve the easily and reduce the time in solving the case. Methods: To solve the criminal cases with in less time there are many methods but here we used clustering technique. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). When a case is enrolled into the data base before if there is any case similar to it then we can solve the case easily by doing the same procedure. Findings: Before they used to file a case on FIR. But now a day, they are using data bases to file a case. By getting any new case they are comparing the new case with the older case so that it will be easy to find the suspect as it takes less time to solve the case. Before they used for other techniques like classification etc. But in my findings and research work clustering is simple, more accurate and takes less time to solve the case easily. In clustering techniques also we have different type of algorithm, but in this paper we are using the k-means algorithm and expectation - maximization algorithm. We are using these techniques because these two techniques come under the partition algorithm. Partition algorithm is one of the best method to solve crimes and to find the similar data and group it. K-means algorithm is done by partitioning data into groups based on their means. K-means algorithm has an extension called expectation- maximization algorithm here we partition the data based on their parameters. Applications: This system can be used for the Indian crime departments for reducing the crime and solving the crimes with less time. This technique can be used to solve the crimes with in less time.

Keywords

Clustering, Data Mining, Expectation– Maximization, K-Means, Unsupervised

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R. Bulli Babu ¹, V. Vanitha ¹, K. Sai Anish ¹

Affiliations
1 Department of Electronics and Computer Engineering, KL University, Guntur District - 522502, Andhra Pradesh, IN

Abstract

Background/Objectives: The most effective and efficient tool for managing large image database is Image Retrieval. Content Based Image Retrieval is strategy for recovering images based on the content of the image. Color, Shape, Texture are said to be Content of an image. Methods/Statistical Analysis: The necessity for Content Based Image Retrieval has been increasing over a decade in different domains such as Data mining, Medical Imaging, Education, Crime prevention etc. It is still an research area where research is going on how to recover the images based on their content. The existing system has a flaw that is image retrieving is based on the keywords presented along with the image. Findings: The images retrieved based on the keywords are inaccurate and it also consumes more time. In this technique we present query image as an input and we get related images as the output which match the content of the query image. Here, the findings are not only based on the color, texture and shape but also trace the underlying points of the image. At first the pictures are recovered based on the color, then took after by texture and finally tracing the underlying graphical structure. Application/Improvements: The proposed system is efficient in recovering images based on the content of the image presented as query. Retrieving images based on underlying graphical structure will help in removal of many irrelevant images and makes the system efficient.

Keywords

Content based Image Retrieval, Color, Ranking, Shape, Texture.

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