• International Journal of 

     Soft Computing and Software Engineering [JSCSE]

    ISSN:  2251-7545

    Prefix DOI  :  10.7321/jscse

    URL: http://JSCSE.com


    A Peer-Reviewed Journal 

  •  The International Journal of 

    Soft Computing and Software Engineering [JSCSE]


Publication Year: [ 2011 ] [ 2012 ] [ 2013 ] [ 2014 ] [ 2015 ] [ 2016 ] [ 2017 ]

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Table of Contents [Vol. 3, No.1, Jan]

Adetunji Adebiyi, Chris Imafidon
Doi : 10.7321/jscse.v3.n1.1
Page : 1 - 11
Show Summary
Abstract . In the last decade, a lot of effort has been put into securing software application during development in the software industry. Software security is a research field in this area which looks at how security can be weaved into software at each phase of software development lifecycle (SDLC). The use of attack patterns is one of the approaches that have been proposed for integrating security during the design phase of SDLC. While this approach help developers in identify security flaws in their software designs, the need to apply the proper security capability that will mitigate the threat identified is very important. To assist in this area, the uses of security patterns have been proposed to help developers to identify solutions to recurring security problems. However due to different types of security patterns and their taxonomy, software developers are faced with the challenge of finding and selecting appropriate security patterns that addresses the security risks in their design. In this paper, we propose a tool based on Neural Network for proposing solutions in form of security patterns to threats in attack patterns matching attacking patterns. From the result of performance of the neural network, we found out that the neural network was able to match attack patterns to security patterns that can mitigate the threat in the attack pattern. With this information developers are better informed in making decision on the solution for securing their application.
Keyword : Attack Pattern, Security Pattern, Software Security, Neural networks

Sidra Ashraf Khan, Abdul Wahab Muzaffar, Dr. Farooque Azam
Doi : 10.7321/jscse.v3.n1.2
Page : 12 - 28
Show Summary
Abstract . Mobile users’ data are becoming increasingly vulnerable due to the many location based services now offered by endless applications in the new mobile app stores. The paper provides an overview of knowledge discovery in databases (KDD) process, and how data mining techniques are used in it. Then, the challenges faced today by user's social-networking habits, which have compromised privacy in an increasingly smart-phone connected world, are addressed. It provides a survey of how research is being carried out in this new and emerging field of knowledge discovery and data mining with respect to data gathered through mobile devices. The different mobility scenarios, possible attack and defense mechanisms for maintaining mobile user privacy is the main focus of this paper.
Keyword : data mining, mobility data, anonymization techniques, privacy