Android Communication Analysis with Intent Revision
mca php project topics 2019 computer science
Android applications (also called Androidapps) have been proved the effective target.Google Play store has provided billions of Android apps, but unfortunately, the advance has a dark side because security cannot be ensured by many Android apps. Hence, more and more attention has been paid to Android malware. Taint flow analysis has been proved an effective approach to providing potential malicious data flows. It aims at determining whether a sensitive data flows from a source to a sink. The analysis can be executed either dynamically or statically. Dynamic taint analysis [5] relies on testing to reach a appropriate code coverage criterion. It is able to precisely pinpoint leaks, but may be incomplete in exploring all possible executing paths. In contrast, static analysis takes all the possible paths for consideration. But most of the static analyses available for Android apps [1,3] are inner-component based analysis which are unable to detect leaks across-components.Even though most of the privacy leaks happen in a single com-ponent, lots of inter-components privacy leaks have been reported. mca php project topics 2019 computer science Thus, inner-component taint analysis is not enough to detect leaks.Efforts have also been devoted to implement static analysis for An-droid [2]to supply us with a relatively satisfactory outcome. Among them, Inter-Component Communication (ICC) [4] analysis plays important roles since ICC values can facilitate a precise consequent.However, the current ICC analyses only consider ICC links be-tween components where reuse and revision of an Intent across-component are not considered. Thus, lots of potential leaks will escape from being tracked in the succeeding ICC leak detection.With this motivation, in this paper, we devote to ICC analysis on reused and revised Intents. To do so, first, ICC values are analyzed by taking reused and revised Intents into account. With this basis,target components of Intents are analyzed and ICC Graphs are built.On an ICCG, all the ICC flows, which are useful in tracking leaks across-components, are contained. This will lay a critical foundation to the succeeding taint flow analysis. The proposed approach has been implemented in a tool called ICC-Analyzer (ICCA) where IC3is integrated in for providing ICC values of the Intents which are not reused or revised.
We have implemented our approach in a tool named ICCA to analyze ICC values with ICIR and construct ICCGs of Android apps for the convenience of the succeeding ICC leak detection. The evaluation of our approach addresses the following two research questions:(1) How does ICCA perform when analyze ICC values with ICIR?and (2) As an ICC analysis tool for Android apps, how ICCA can precisely match the targets of Intents?
CC Analysis with ICIRBy experiments, we can obtain that 37 and 36revised Intents existin GooglePlay and MalGenome, respectively. We apply ICCA in analyzing ICC values of the 73revised Intents. Table 1 illustrates abird’s eye view of the whole experiment. The l.h.s of Table 1 shows the seven attributes Action,Category,Type,Data,Flag,Extra, and Component of the 37 different revised Intentsin GooglePlay. Ther.h.s illustrates the attributes of the 36revise Intentsin MalGenome.Note that in the table, ‘–’ means that the relative value is captured but not modified, and ‘√’ indicates that the revised value is successfully acquired. As shown in Table 1, ICC values of all the 73revisedIntentsare successfully captured which are unable to be obtained by all the existing ICC analysis tools
In this part, we illustrate the target components of different Intentsin GooglePlay and MalGenome matched by ICCA. The results are compared with the target components matched with IC3. https://codeshoppy.com/php-projects-titles-topics.html All the Intents are classified into three categories: explicit, implicit and reused ones. We record the numbers of Intents in different categories and numbers of the matched target components.The results on GooglePlay and MalGenomeare presented in Ta-ble 2. The first column are the sets of data; the second one shows the categories of Intents; the third one illustrates the numbers of the involved Intents in the relative category. The right-most two columns present the numbers of the target components identified by IC3 and ICCA, respectively. As shown in the experiment, both IC3 and ICCA can identify most of the explicitly defined target components of In-tents (94.8% inGooglePlay and 98.7% inMalGenome). For implicitones, a small part (1.3% inGooglePlay and 0.7% inMalGenome) of them are acquired by ICCA whereas null of them can be obtained by IC3. The success rate of ICCA is low since implicit Intents are frequently used to launch target components in other apps which cannot be acquired without the runtime environment. Thus, our re-sult is reasonable. For the reused Intents, ICCA can acquire almost all the target components while null of them are acquired by IC3. In this experiment, we compare the results of ICCA in ICC analysis only with the newest ICC analysis tool IC3 as it is an improvement of Epicc. To be best of our knowledge, IC3 and Epicc are the only ICC analysis tools publicly available