Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

CIAA-RepDroid: A Fine-Grained and Probabilistic Reputation Scheme of Android Apps Based on Sentiment Analysis of Reviews

Version 1 : Received: 31 July 2020 / Approved: 2 August 2020 / Online: 2 August 2020 (15:49:51 CEST)

A peer-reviewed article of this Preprint also exists.

Tchakounté, F.; Yera Pagor, A.E.; Kamgang, J.C.; Atemkeng, M. CIAA-RepDroid: A Fine-Grained and Probabilistic Reputation Scheme for Android Apps Based on Sentiment Analysis of Reviews. Future Internet 2020, 12, 145. Tchakounté, F.; Yera Pagor, A.E.; Kamgang, J.C.; Atemkeng, M. CIAA-RepDroid: A Fine-Grained and Probabilistic Reputation Scheme for Android Apps Based on Sentiment Analysis of Reviews. Future Internet 2020, 12, 145.

Journal reference: Future Internet 2020, 12, 145
DOI: 10.3390/fi12090145

Abstract

To keep its business reliable, Google is concerned to ensure quality of apps on the store. One crucial aspect concerning quality is security. Security is achieved through Google Play protect and anti-malware solutions. However, they are not totally efficient since they rely on application features and application execution threads. Google provides additional elements to enable consumers to collectively evaluate applications providing their experiences via reviews or showing their satisfaction through rating. The latter is more informal and hides details of rating whereas the former is textually expressive but requires further processing to understand opinions behind. Literature lacks approaches which mine reviews through sentiment analysis to extract useful information to improve security aspects of provided applications. This work goes in this direction and in a fine-grained way, investigates in terms of confidentiality, integrity, availability and authentication (CIAA). While assuming that reviews are reliable and not fake, the proposed approach determines review polarities based on CIAA-related keywords. We rely on the popular classifier Naive Bayes to classify reviews into positive, negative and neutral sentiment. We then provide an aggregation model to fusion different polarities to obtain application global and CIAA reputations. Quantitative experiments have been conducted on 13 applications including e-banking, live messaging and anti-malware apps with a total of 1050 security-related reviews and 7.835.322 functionality-related reviews. Results show that 23% of applications (03 apps) have a reputation greater than 0.5 with an accent on integrity, authentication and availability, while the remaining 77% has a polarity under 0.5. Developers should make lot of efforts in security while developing codes and that more efforts should be made to improve confidentiality reputation. Results also show that applications with good functionality-related reputation generally offer bad security-related reputation. This situation means that even if the number of security reviews is low, it does not mean that security aspect is not a consumer preoccupation. Unlike, developers put much more time to test whether applications works without errors even if they include possible security vulnerabilities. A quantitative comparison against well-known rating systems reveals effectiveness and robustness of CIAA-RepDroid to repute apps in terms of security. CIAA-RepDroid can be associated to existing rating solutions to recommend developers exact CIAA aspects to improve within source codes.

Subject Areas

Reputation; Android; application; sentiment analysis; reviews; security service; NLP; Google Play; polarity

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