Investigating Social Networking applications on smartphones detecting Facebook, Twitter, LinkedIn and Google+ artefacts on Android and iOS platforms

Dezfouli, Farhood Norouzizadeh; Dehghantanha, Ali; Eterovic-Soric, Brett; Choo, Kim‐Kwang Raymond

doi:10.1080/00450618.2015.1066854

Cited by 67 publications

(25 citation statements)

References 30 publications

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“…In [ 57 ], for instance, researchers were able to recover important details related to bank accounts and financial transactions from mobile banking applications. It was also found that remnants of social networking applications installed on smartphones (such as Twitter and LinkedIn applications) could be utilized for revealing personal information about smartphone users and understanding their social contexts [ 60 ]. Cloud storage mobile applications have also been found to store important information that could be useful for inferring knowledge about the types of files users share and for constructing useful information about users’ activities [ 61 – 67 ].…”

Section: Related Workmentioning

confidence: 99%

Smartphone users: Understanding how security mechanisms are perceived and new persuasive methods

2017

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Protecting smartphones against security threats is a multidimensional problem involving human and technological factors. This study investigates how smartphone users’ security- and privacy-related decisions are influenced by their attitudes, perceptions, and understanding of various security threats. In this work, we seek to provide quantified insights into smartphone users’ behavior toward multiple key security features including locking mechanisms, application repositories, mobile instant messaging, and smartphone location services. To the best of our knowledge, this is the first study that reveals often unforeseen correlations and dependencies between various privacy- and security-related behaviors. Our work also provides evidence that making correct security decisions might not necessarily correlate with individuals’ awareness of the consequences of security threats. By comparing participants’ behavior and their motives for adopting or ignoring certain security practices, we suggest implementing additional persuasive approaches that focus on addressing social and technological aspects of the problem. On the basis of our findings and the results presented in the literature, we identify the factors that might influence smartphone users’ security behaviors. We then use our understanding of what might drive and influence significant behavioral changes to propose several platform design modifications that we believe could improve the security levels of smartphones.

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Section: Related Workmentioning

confidence: 99%

Smartphone users: Understanding how security mechanisms are perceived and new persuasive methods

2017

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“…Iqbal et al, Hirst and Feiguina, Afroz et al, and Chen et al all profile users' writing styles by extracting lexical and syntactic information from the users' text. Iqbal et al used decision trees and support vector machine to determine authorship of 2000 emails from about 160 employees in the Enron Company (this data set has also been used in a number of forensic investigations). Their first experiment shows that the accuracy can reach up to 80% when there are no more than 10 suspects .…”

Section: Related Workmentioning

confidence: 99%

Astroturfing detection in social media: a binary n‐gram–based approach

Peng

Detchon

Choo

et al. 2016

Concurrency and Computation

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Summary Astroturfing is appearing in numerous contexts in social media, with individuals posting product reviews or political commentary under a number of different names, and is of concern because of the intended deception. An astroturfer works with the aim of making it seem that a large number of people hold the same opinion, promoting a consensus based on the astroturfer's intentions. It is generally done for commercial or political advantage, often by paid writers or ideologically motivated writers. This paper brings the notion of authorship attribution to bear on the astroturfing problem, collecting quantities of data from public social media sites and analyzing the putative individual authors to see if they appear to be the same person. The analysis comprises a binary n‐gram method, which was previously shown to be effective at accurately identifying authors on a training set from the same authors, while this paper shows how authors on different social media turn out to be the same author. The method has identified numerous instances where multiple accounts are apparently being operated by a single individual.

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“…For example, researchers are focusing their efforts on extracting cloud storage information from client cloud software such as Dropbox and Google Drive [32,33,35], social networking applications such as Facebook, Twitter and Google+ [65], and different mobile devices [37,38]. Other researchers are working on techniques to deal with the large amount of data found on the cloud.…”

Section: Discussionmentioning

confidence: 99%

Scenario-Based Digital Forensics Challenges in Cloud Computing

2016

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Abstract:The aim of digital forensics is to extract information to answer the 5Ws (Why, When, Where, What, and Who) from the data extracted from the evidence. In order to achieve this, most digital forensic processes assume absolute control of digital evidence. However, in a cloud environment forensic investigation, this is not always possible. Additionally, the unique characteristics of cloud computing create new technical, legal and architectural challenges when conducting a forensic investigation. We propose a hypothetical scenario to uncover and explain the challenges forensic practitioners face during cloud investigations. Additionally, we also provide solutions to address the challenges. Our hypothetical case scenario has shown that, in the long run, better live forensic tools, development of new methods tailored for cloud investigations and new procedures and standards are indeed needed. Furthermore, we have come to the conclusion that forensic investigations biggest challenge is not technical but legal.

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Investigating Social Networking applications on smartphones detecting Facebook, Twitter, LinkedIn and Google+ artefacts on Android and iOS platforms

Cited by 67 publications

References 30 publications

Smartphone users: Understanding how security mechanisms are perceived and new persuasive methods

Smartphone users: Understanding how security mechanisms are perceived and new persuasive methods

Astroturfing detection in social media: a binary n‐gram–based approach

Scenario-Based Digital Forensics Challenges in Cloud Computing

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