However, due to space constraints such basic commands aren't discussed in this section see Appendix B for a full listing and summarization of all commands. For the second byte that command 2 expects, we initially send it a zero. Saving embedded binary to disk. Embedded binary execution. Note that the process monitor doesn't show any arguments. If we disassemble the embedded machO binary we uncover the code that reads in the parameter from stdin via getchar.
As the following disassembly shows, the binary uses this value as an index into a table of function pointers. In order words, this value is a 'command' selector. Argument processing in embedded binary. Examining command 2's response. File type identification. Command 2's result parameter value: However, we still didn't know the meaning of the second parameter. Command 8 also causes the malware to call into the embedded machO binary. Triage showed these to be a single byte followed by two four-byte integer values. However, the purpose of the command and these parameters, at this point, remained unknown.
The data returned by this command is either the command number 8 or a zero, depending on whether the command succeeds or fails. Thus, unlike command 2 which revealed its purpose by the data it returned i. Interestingly, the mouse sniffer lit up. For example, passing in 0, 0, 0 for the three command-specific parameters generated the following mouse event:. Captured mouse event parameter values: So tasking the malware via command 8, and then specifying zero, moves the mouse to the x,y screen location provided by the two remaining parameters.
It is easy to see that the 1 parameter instructs the malware, via the mouse command 8 , to move and then left-click the mouse. Passing in a 2 seems to generate the same event left mouse click. Captured mouse event drag. Selected text via command 8. Let's now take a look at command Specifically, it expects a single byte followed by a variable length string:. To determine the purpose of this command, we can simply task the malware to execute it once it reconnects, by sending it a We start by specifying a 0 for the first byte the command expects and then the string ' foo '.
Closely watching our monitoring utilities we notice this triggers an event on just one, the file monitor:. If we re-task the malware with the same command and initial parameter 0 , but this time provide a path to a file that exists on the infected system, again we see the stat64 , but this time the malware responds with a 1, as can be seen in Figure Thus we can conclude that command 12, when passed an initial parameter 'subcommand' of 0, will check for the existence of a file and return a boolean value representing the result of this check.
If we send the malware a 12 to execute the file command, followed by a 1, and then a path to a file, the file monitor shows the file being deleted via an unlink:. Command 12 parameter value: Command 12 process event parameter value: Next up are commands 16 and These command expect to receive an extra byte a subcommand? This value, along with the command value 16 or 17 , is passed into the ' V ' subroutine. Via our monitoring utilities we can see that, when the malware is tasked to execute either command 16 or command 17, the following events are recorded:.
So commands 16 and 17 can be used to send key presses to the active forefront window. In other words the malware affords an attacker the ability to type remotely on an infected host. From an attacker's point of view, this capability may be useful to interact with system dialogs or other UI components on the infected system. Of course, the attacker could also say 'hi' to the infected user:. Captured keyboard events 'hi'. The final command we'll look at in this section is command Via tcpdump we can observe the malware responding to our request to connect to the host specified virusbulletin.
Malware's response to our request. Somewhat interestingly, the malware will immediately close the connection even if it's successful. Malware analysis is a time-consuming and often strenuous process. And while traditional analysis techniques such as static analysis and debugging can reveal the full functionality of a malware specimen, there may be a better way.
In conjunction with various monitoring utilities, via this server we were able simply to task the malware in order to coerce it into revealing its entire capabilities. Besides basic capabilities such as executing commands via system and interacting with files on an infected system, we uncovered the fact that the malware supports more advanced commands rarely if ever? For example, being able to simulate mouse and keyboard events, perhaps to interact with system dialogs or alerts from security products, truly gives a remote attacker unprecedented control over an infected Mac.
A Systems Approach. Reads a specified number of bytes from a process, returning them to the caller. Sends data to the command-and-control server.
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Reads data from the command-and-control server. Once this buffer's size is equal to the requested number of bytes to read, the loop exits and the bytes are returned to the caller. In other words, this subroutine is invoked to read a chunk of variable-length data such as a string , with a prefixed length. Perl documents state that the ' V ' format specifier represents 'an unsigned long bit in "VAX" little-endian order' [ 12 ].
Thus in this subroutine, the ' unpack ' will convert a string from the server into a host-byte integer. By means of various helper functions, writes out an embedded machO binary, executes it, and writes a passed in argument to the process's stdin.
Then reads in all bytes, returning them to the caller. Converts a string into a string that's prefixed with its size. According to Perl documentation [ 12 ], the ' V ' packing template specifies 'an unsigned long bit in "VAX" little-endian order', while the 'a' specifies 'a string with arbitrary binary data, will be null padded' [ 12 ].
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Damodaran et al. Suarez-Tangil et al. Yerima et al. Saracino et al. OS X malware has also been on the increase [ 24 ], but there is limited published research in OS X malware analysis and detection. For example, a small number of researchers have developed OS X malware and Rootkit detection techniques, and malware detectors by tracing suspicious activities in memory like unwanted access, read, write and execute [ 25 , 26 , 27 ]. However, applying machine learning to detect OS X malware is limited to the Walkup approach [ 28 ], which utilized Information Gain IG to select effective features for supervised classification of OS X malware.
Hence, development of machine learning techniques for OS X malware detection is the gap that this paper seeks to contribute to. As part of this research, we collected malware samples from [ 29 , 30 , 31 ]. These samples were collected between Jan and June thus OS version which can run them are in following order: OS X Duplicated samples were detected by performing a SHA hash comparison and removed from the datasets.
Similar to previous datasets such as those of Masud et al. To start with how the dataset collected, we first presented an overall definition of each MacOS X application in Fig. As it can be seen if you extract each OS X application bundle you would usually encounter a directory, named Contents. This directory also consists files and some component as follows [ 33 ]:. This directory is main part of each application bundle and contains several directory and files which is introduce as follows:. This fill consist the configuration information for the application.
The Mac Operating System relies on the presence of info. Consists the applications executable code file Mach-O. Usually, this directory comes with only a binary file with the applications main entry point and constantly linked code. Consists all resource files of the application i. Consists all private shared library of the application and the framework which used by executable code. Consists all loadable files and libraries which extend application features and capabilities. Header contains common information about the binary such as byte order magic number , CPU type, and number of load commands.
Load Commands section contains information about the logical structure of an executable file and data stored in the virtual memory such as symbol table and dynamic symbol table. Load Commands contains information about logical structure of an executable file and data stored in the virtual memory such as symbol table, dynamic symbol table, etc.
Segments is the biggest part of each Mach-O files which contains application code and data. Mach-O HD: Mach-O LC: Mach-O SG: This file provides the raw data of three Mach-O file sections i. Utilizing Euclidean distance for computing distance between each missing value i. Feature selection techniques are used to find the most relevant attributes for tion. At this stage, the three common feature selection technique Information Gain, Chi-Square and Principal Component analysis for malware detection based on code inspection Shabtai et al.
Information Gain IG [ 43 ] is a technique used to evaluate attributes to find an optimum separation in classification, based on mutual dependencies of labels and attributes.
Automated Malware Analysis
Chi-square measures the lack of independence between attributes [ 44 ]. We also used PCA as a feature selection mechanism to select the most informative features for classification.
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After the feature selection methods were used to calculate the relevant scores, features with the highest scores will be considered. Features obtained values from ranker search method to select appropriate feature. The main classification task of the proposed methodology is developed using SVM. The machine learning algorithm in [ 46 ] separates data into N-dimensions with different categories in each hyperplane. Then, the dimension with the largest margin will be used for classification.
Detecting Malware in Mac OS X Environments
SVM is the used to maximize the margin between given classes and obtain best classification result. The boundary of margin function is defined by support vectors data samples.
This margin is calculated from candidate support vectors which are those nearest to the optimized margin the largest margin that separated two types of data see Fig. Kernel functions map training data into higher dimensions to find a separating hyper plane with a maximum margin [ 47 ]. Using the library-weighting measure, we created two new features, namely: Due to data normalization and well-separated features shown in Fig.
While accuracy is increased in all cases and we have received much higher accuracy i. In Addition, the complexity of classification technique had reduction due to two new added features lib-w-b, lib-w-m. For instance J48 classification complexity before adding the two new features was 65 nodes and 35 leaves but after providing the new features reduced to 55 nodes and 33 leaves receptively.
Cuckoo Sandbox - Automated Malware Analysis
As shown in Fig. Statistical analysis of the library calls revealed that applications that call audio and video related libraries AudioToolbox and CoreGraphics are mostly benign while most malicious apps more frequently call system libraries i. In this paper, we developed four OS X malware datasets and a novel measure based on library calls for classification of OS X malware and benign application. Moreover, using Decision Tree- J48 we obtained Moreover the synthetic datasets are generated using SMOTE technique and assessed them by same supervised algorithm.
This experiment is conducted to show effect of number of sample size on detection accuracy. Our results indicate that increasing sample size may increase detection accuracy but adversely affect the false alarm rate. OS X malware detection and analysis utilising dynamic analysis techniques is a potential future work of this research. Extending classification using other techniques such as Fuzzy classification, applying deep learning for OS X malware detectionm and using a combination of our suggested features for OSX malware detection are interesting future works of this study.
We thank VirusTotal for providing us a private API key to access their data for constructing our dataset. Skip to main content Skip to sections. Advertisement Hide. Download PDF. Intelligent OS X malware threat detection with code inspection. Open Access. First Online: The increasing Mac OS X market size second after Microsoft Windows [ 6 ] and its fast adoption rate motivate cyber threat actors to shift their focus to developing OS X malware.
Open image in new window. Accuracy ACC: Consists all non-critical resources which not extend the application capabilities. Dominance of benign samples in the collected dataset was due to obtain desirable results in False Alarm rate by training the classifier with more goodware and detect anomalies from them just like real world benchmark dataset on anomaly detection which provided in [ 35 , 36 , 37 ]. We then extracted the Mach-O binaries of all malware and beningware samples in the respective datasets manually.
Mach-O binaries are the executable portion of an OS X application [ 38 ] and consist of three sections as follows see also Fig. Similar to many other malware machine learning datasets, our datasets include several features with missing values; thus, we utilized K-Nearest Neighbor KNN imputation technique [ 40 ] for estimation of missing values. The imputation technique is performed in two steps, as follows: Table 1 OS X dataset features. Black Hat News. App Sec. Threat Intelligence. Kelly Jackson Higgins News. Connect Directly. Email This. White Papers. Integrated Threat Response.
Threat Intelligence Playbook: Making sense of indicators. A Profile of Today's Security Posture. The Risk Management Struggle. MelBrandle , User Rank: Hackers are growing in numbers and they know exactly which platforms are vulnerable enough to become their next target to hit. As much as we would like to update our systems at work and at home, we can never keep up with technology especially amidst our busy schedules. This makes us easy targets for hackers as they usually aim for the older versions of operating systems to hack into.
However, usualy for personal usage, it is not much of a concern. Large corporations with so much data to share are usually the main target. Kelly Jackson Higgins , User Rank: I know they are hoping to release it soon, but I'm not clear it will be next week. SchemaCzar , User Rank: This sounds like a great tool and a great asset. I hope it becomes publicly available soon!