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To keep its business reliable, Google is concerned to ensure the 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 it. Literature lacks approaches which mine reviews through sentiment analysis to extract useful information to improve the 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 a lot of effort 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 a bad security-related reputation. This situation means that even if the number of security reviews is low, it does not mean that the security aspect is not a consumer preoccupation. Unlike, developers put much more time to test whether applications work without errors even if they include possible security vulnerabilities. A quantitative comparison against well-known rating systems reveals the effectiveness and robustness of CIAA-RepDroid to repute apps in terms of security. CIAA-RepDroid can be associated with existing rating solutions to recommend developers exact CIAA aspects to improve within source codes.

Background: A major challenge to malaria vaccine development is identification of protective epitopes and respective protective immune responses., Objective: To characterize naturally acquired Immunoglobulin G (IgG) responses to the synthetic peptide AS202.11, a malaria vaccine candidate., Methodology: This community based cross-sectional study enrolled 320 participants aged 1 year and above. Demographic information was recorded through interviews. Detection of P. falciparum infection was done by microscopy, malaria rapid diagnostic test, and polymerase chain reaction. ELISA was used to detect IgG antibody. Data was analyzed using STATA., Results: The overall AS202.11 IgG seropositivity was 78.8% (73.9-82.9). Seropositivity by age categories was ≤12 years [74.3% (67.4-80.2)], 13-40 years [85.3% (76.5-91.1)], and >40 years [82.6% (68.7-91.1)]. Compared to the ≤ 12-year-old group, aORs for the other groups were 2.22 (1.14-4.32), p = 0.019, and 1.87 (0.81-4.35), p = 0.143, for the 13-40-year-old and >40-year-old groups, respectively. The 13-40-year-old group had more seropositive individuals compared to the ≤ 12-year-old group., Conclusion: We report a high degree of recognition of AS202.11 by IgG elicited by field P. falciparum strains, suggesting its close similarity to native P. falciparum antigens and possible suitability of the peptide as a future malaria vaccine candidate.