## 4 Results and Discussions
### Comparison of CIAA-RepDroid to SERS and Google Play Rating  
Table 7 summarizes results concerning reputation determined in our proposed scheme compared to ratings in SERS work and the traditional rating of Google. In this experimentation, the dataset of communication applications in [48] has been used. In this work, authors have computed their ratings shown in columns (R2), (R3) and (R4). They also have collected traditional rating available in column R5. What we did is to apply our approach to their samples to obtain R1. R11 is the fine-grained reputation in term of confidentiality, R12 in term of integrity, R13 in term of authentication and R14 in term of availability. As it is defined in the SERS's work, R2 is a rating value of the application based only on sentiment analysis of the whole reviews (They consider all the reviews of the app to have R2); R3 is a rating value of the application based only on static analysis of required permissions; R4 is the global rating aggregating R2 and R3. To obtain R4. we have computed the average of weighted R2 and R3. The column #total reviews is the total number of reviews per application and # security reviews is the number of reviews related to security. Figure 4: Reputation-based security vs. reputation-based functionality. \begin{table}
\begin{tabular}{l l l l l l l l l l l l l l} \hline \hline \multicolumn{2}{l}{**App Characteristics**} & \multicolumn{4}{c}{**Our Model**} & \multicolumn{4}{c}{**SERS**} & \multicolumn{2}{c}{**Google**} \\ \hline \multirow{2}{*}{**App Id**} & **\#Total** & **\#Security** & **Global** & **Con.** & **Int.** & **Aut.** & **Ava.** & **Ratio Based** & **Rating Based** & **Rating Based** & **Static** & **Global Rating** & **Traditional** \\  & **Reviews** & **Reviews** & **Reviews** & **Reputation (R1)** & **R11** & **R12** & **R13** & **R14** & **Reviews (R2)** & **Analysts (R3)** & **(R4)** & **Rating (R5)** \\ \hline
1 & 2573 & 21 & 1.9 & 1.7 & 2.6 & 1.0 & 1.8 & 4.09 & 4.22 & 3.66 & 4.1 \\
2 & 2896 & 5 & 2.2 & 1.8 & 2.4 & 3.0 & 2.0 & 4.44 & 3.98 & 3.95 & 4.6 \\
3 & 2590 & 52 & 1.7 & 1.6 & 1.9 & 1.6 & 1.8 & 4.21 & 4.08 & 3.82 & 4.5 \\
4 & 2567 & 41 & 1.6 & 1.5 & 1.7 & 1.7 & 1.9 & 4.23 & 4.46 & 3.86 & 4.5 \\
5 & 2598 & 28 & 1.6 & 1.8 & 1.4 & 1.5 & 1.6 & 4.37 & 4.92 & 4.46 & 4.5 \\
6 & 2586 & 62 & 1.6 & 1.6 & 1.8 & 1.6 & 1.5 & 2.81 & 4.51 & 3.43 & 4.3 \\
7 & 2566 & 24 & 1.8 & 1.5 & 2.1 & 1.8 & 1.5 & 4.22 & 4.37 & 3.95 & 4.1 \\
8 & 2546 & 22 & 1.6 & 1.6 & 1.4 & 1.6 & 1.7 & 4.32 & 2.81 & 4.52 & 4.4 \\
9 & 2578 & 37 & 1.6 & 1.6 & 1.7 & 1.7 & 1.5 & 3.79 & 4.22 & 2.51 & 4 \\
10 & 2580 & 40 & 1.7 & 1.6 & 1.8 & 1.8 & 1.6 & 3.61 & 4.32 & 4.25 & 4.1 \\
11 & 2572 & 54 & 1.6 & 1.6 & 1.5 & 1.6 & 1.8 & 4.27 & 3.79 & 4.19 & 4.6 \\
12 & 2598 & 43 & 1.7 & 1.8 & 1.5 & 1.7 & 1.5 & 3.91 & 3.61 & 3.44 & 4.6 \\
13 & 2500 & 42 & 1.6 & 1.5 & 1.8 & 1.5 & 1.6 & 3.51 & 4.27 & 3.29 & 4.2 \\
14 & 2598 & 43 & 1.7 & 1.7 & 1.6 & 1.8 & 1.7 & 2.79 & 3.91 & 3.89 & 4.2 \\
15 & 2552 & 65 & 1.7 & 1.6 & 1.6 & 1.9 & 1.7 & 3.32 & 3.51 & 2.55 & 4.1 \\ Average & 2573 & 39 & 1.70 & 1.63 & 1.78 & 1.72 & 1.68 & 3.85 & 4.06 & 3.71 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results about reputation and rating without gaps. The first observation is that app ID #3 is the one having the higher number of reviews while app ID #15 is the one having more security-related reviews. Concerning column R5, we observe also that users have rated these apps of at least 4. meaning that they are very well satisfied about these apps. R2 slightly coincides with R4 except some apps such as 6, 9, 10, 13, 14 and 15 with which the gaps is around 1. This observation is similar between R3 and R4 and R4 and R5. The trend from the above observations (according to [24]) is that traditional ratings fit with rating based on reviews. Our work comes to explicitly determine security-related rating of app based on security-related reviews.