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They were added to the test database if at least one of the scanners classified them as malware. Java Code Features extraction To be able to extract the features, the files should be prepared properly. To this end, BASH shell scripts have been developed and auxiliary scripts that organize files. Scripts gets the. For this purpose, the dex2jar tool is used.
It enables: reading and writing to a. Table 1.
Methods, commands and classes from which methods where extracted belonging to the feature vector from java code. The external library used for the extraction of features is JD-core-java - a package decompiling Java decompiler called Java Decompiler Java Decompiler authors did not provide a tool in the form of a library that can be used inside the code, only a decompiler as a plug-in for selected programming environments or graphics tool.
Necessary to obtain the application code from the. To focus on the analysis of features selected by all three selection methods, in Table 2 there are features present in the top characteristics for each selection method and their participation in malware applications and secure applications.
All features from Table 2 are much more common in malware than in secure applications.
Table 2. Percentage of occurrences of features derived from java code, which are in the top features chosen by 3 selection algorithms in the malware and safe application sets. Feature Percentage in malware Percentage in safe apps startService 0. Operations on strings are used to avoid detection by dynamically creating URLs, providing parameters to the reflection mechanism API, or to hide Linux commands.
In addition, the Context. Therefore, this is a great opportunity to save a dangerous string of characters, e. The Context. In the dataset of this work, it occurred about times more often in malware. The getSystem method. The Context class service, appearing in malicious applications 9 times more often than in safe applications, allows access to given system services.
Without examining the parameters of this method, it is difficult to conclude what specifically access was requested for. However, among the system services that this method gives access to, there are those that can be potentially dangerous: window visibility management, network connections, Wi-Fi connectivity, HTTP download process, location data.
Thanks to this method, on the one hand, the application can receive harmful packages payload , on the other hand, send sensitive data about the user to the external server. Intent: setPackage and putExtra methods, used mostly in malware in comparison with secure applications, may have their justification in intentional intentions.
Intentional intentions are those that do not indicate a specific component that the intention can pick up. Therefore, it is possible that the intention will be received by another application. The threat occurs when a secure application uses implicit intent, does not specify which component can perform the action, and then such an intention intercepts the malware. Then he will be able to send the application in response to inaccurate data or send information about the success of the operation at the moment when the operation did not take place.
The result of intention can be saved using the putExtra function. The setPackage function is used to determine which components can receive the intention. Perhaps such a large presence in the malware serves to specify exactly which component should be responsible for the actions in order to have full control over the course of malicious activity, so that no other application could accidentally intercept the expected event.
It is noted that only two patterns listed at all appear in the code of the tested applications - and these are the startService and schedule methods. They are used to activate the service accordingly and scheduling the service.
Statistical tests were performed on the characteristics of Table 2 using the Mann-Whitney U-test. The confidence level at 0. For each of the features in Table 2, the null hypothesis of median equality was rejected and an alternative hypothesis with a larger median in the malware population was adopted than in the population of safe applications.
Three methods of feature selection where be tested. Then, for each classifier, its selected parameters will be tested, and with the adopted determined parameters, the classification will be determined depending on the number of features taken into account.
Next, the most common features in malware will be listed. For testing each classifier, fold cross validation will be used, repeated 10 times. The stages are as follows: First is selection of features, second is characteristics of malware, third is classifiers and their parameters see Table 3 , fourth is summary of the best results and time data for classifiers and last each of the 5 classifiers will be tested for selected parameters see second column of Table 3.
Parameters tested for classifiers. Random Forest First test case included changing of the maximum depth of the tree with assumptions: number of iterations: , number of features: Table 4 shows that after reaching the maximum depth of 50, the percentage of correctly classified instances was stable - and in the range of maximum depth from 80 to and equal to infinity even identical.
Some of the other indicators also remained at the same level from a depth greater than or equal to TP, FN and F-measure. Interestingly, among these research cases, the lowest time was recorded for a maximum depth of Measures TN and FP had the best result for a depth of Table 4. Results of the examination of the influence of the maximum depth for a random forest on the percentage of correctly classified instances, the root of mean square error and the time of learning and testing.
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