# A Risk Assessment Framework for Mobile Apps in Mobile Cloud Computing Environments
## 4 Discussion  
As seen in Table 8, none of the 20 apps in \(ZV\) was categorized as a high-risk app. This was not unexpected, as all the apps in the validation sample were benign. The ML classifier correctly classified 18 apps as benign and wrongly classified two apps as malicious. We further analyzed these two apps (ZV7 and ZV17) to get an insight into this inaccurate prediction. It appeared that these apps used a relatively high number of dangerous permissions from the set \(PV\) (eight and ten, respectively). However, the apps' risk scores were between 0.25 and 0.65, hence the app risk evaluator categorized them as medium-risk apps (rather than as high-risk apps). An app's risk category depends not so much on the number of known dangerous permissions requested by the app but on the particular risk scores of the dangerous permissions requested. For example, apps ZV11 and ZV19 requested the same number of dangerous permissions (five) but were categorized as low and medium risk, respectively. Indeed, ZV19 requested the second highest scoring dangerous permission, P3, while ZV11 requested the lowest-scoring permission, P11. In another example, apps ZV15 and ZV16 each requested just one dangerous permission (P1). However, P1 is a high-risk permission, second only to P2. Both apps were categorized as medium risk. Only one app (ZV5) from the seven apps (ZV1, ZV2, ZV4, ZV5, ZV11, ZV14, ZV18) that were categorized as low risk used a highly dangerous permission \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Classification**} & \multirow{2}{*}{**Risk Score**} & \multirow{2}{*}{**Risk Category**} & \multicolumn{2}{c}{**Requested Dangerous Permissions**} \\  & & & & & & & **from the Set PV** \\ \hline ZV1 & Beauty & IPSY & Benign & 0.4759 & Low & P1, P3, P4, P6, P10, P11 \\ ZV2 & Beauty & Sephora & Benign & 0.4888 & Low & P1, P4, P6, P10 \\ ZV3 & Books & Read Extra & Benign & 0.5293 & Medium & P1, P6 \\ ZV4 & Books & GoodNovel & Benign & 0 & Low & None \\ ZV5 & Business & Slack & Benign & 0.4709 & Low & P1, P2, P6, P10, P11, P12 \\ ZV6 & Business & Office Suite & Benign & 0.6764 & Medium & P1, P2, P5 \\ ZV7 & Dating & Tinder & Malicious & 0.5385 & Medium & P1, P2, P3, P4, P5, P6, P10, P11 \\ ZV8 & Dating & Zoosk & Benign & 0.5506 & Medium & P1, P2, P3, P4, P6, P10, P12 \\ ZV9 & Education & Moodle & Benign & 0.5288 & Medium & P1, P2, P3, P4, P6, P10, P11 \\ ZV10 & Education & Linked Learning & Benign & 0.5049 & Medium & P1, P2, P3, P4, P6, P10, P11, P12 \\ ZV11 & Entertainment & Tubit & Benign & 0.4496 & Low & P1, P5, P6, P10, P11 \\ ZV12 & Entertainment & Xbox & Benign & 0.655 & Medium & P1, P2, P3, P6 \\ ZV13 & Game & Solitaire & Benign & 0.5293 & Medium & P1, P6 \\ ZV14 & Game & Bubble Shooter & Benign & 0.4381 & Low & P1, P6, P7 \\ ZV15 & Medical & FollowMyHealth & Benign & 0.5769 & Medium & P1 \\ ZV16 & Medical & Davis’s Drug Guide & Benign & 0.5769 & Medium & P1 \\ ZV17 & Shopping & Amazon & Malicious & 0.4901 & Medium & P1, P2, P3, P4, P6, P7, P10, P11, P12 \\ ZV18 & Shopping & eBay & Benign & 0.4455 & Low & P1, P3, P4, P6, P10, P11, P14 \\ ZV19 & Social & Facebook & Benign & 0.5341 & Medium & P1, P3, P4, P6, P10 \\ ZV20 & Social & WhatsApp & Benign & 0.5016 & Medium & P1, P2, P3, P4, P5, P6, P10, P11, P12, P15 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Resident app risk score risk category, and dangerous permissions requested. (P2). Generally, the medium-risk apps in the set \(ZV\) tended to use not just a high number of permissions but high-risk score permissions. According to [35], the activities of malicious and unwanted apps have contributed significantly to the growth of the number of attacks on mobile devices. Installing fake apps on Google Play is one of the methods used by adversaries to obtain personal data about the MD user [36]. Indeed, it is pointed out in [37] that uploading malicious apps on Google Play has become quite common.