Text Mining for Suspicious Contents in Mobile Cloud Computing Environment

  • Salim AlamiEmail author
  • Omar Elbeqqali
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 366)


Mobile devices, such as tablets and smartphones, have become the main computing platform for many people. Ubiquitous computing, as a concept, has developed with the emerging of cloud computing technology that has forced the mobile devices industry to prerequisite the bringing of cloud computing to mobile domain. Mobile Cloud Computing MCC is a service that allows users of mobile devices high availability of their personal applications as well as their own content, everywhere and anytime. Unfortunately, malicious people take advantage of this technological achievement in the sense that they store all illegal information on cloud in order to hide all digital illegal records justifying their illicit acts served by their mobile devices. What is more, mobile forensic expertise on those mobile devices cannot be accomplished by digital investigators of law enforcement, simply because all the storage is done in the cloud. In this vein, Mobile Cloud Computing MCC technology is a double-edged weapon; it has made life easier on one hand. And, it has complicated the work of law enforcement authorities to find truth, on the other hand. In cloud environment, malicious users can be stored several and various formats of suspicious content (text, image, video…), so in this work we will focus only on textual content. Text mining is an effective way to add semantics aspect to this communication’s form presenting a significant research challenge. Similarity approach is used in text analysis to detect suspicious text contents in cloud storage. So, in this paper we will present a state-of-the-art and research challenges of mobile cloud computing. We will also discuss the problem of data management and data analysis on a cloud environment. Ultimately, we will suggest an approach to come up with the aforementioned problems.


Cloud computing Mobile Cloud Computing Digital forensics Mobile forensics Cloud forensics Text analysis Text mining Profiling 


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© Springer Science+Business Media Singapore 2016

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Authors and Affiliations

  1. 1.LIIAN LaboratorySidi Mohammed Ben Abdellah UniversityFezMorocco

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