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Thanks to the ELF Object Format, we have successfully automated theprocedure of switching the set of user global variables when switchingthread contexts. Executable and Linkable Format (ELF) is a commonstandard file format for Object Files in Unix-like operating systems.ELF maintains a Global Offset Table (GOT) for globals so it is possibleto switch GOT contents at thread context-switch by the runtime system.
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Another approach is to do the changes described in the previous schemeautomatically. It means that we can use a tool to transform the sourcecode to move global or static variables in an object and pass themaround. This approach is portable across systems and compilers and mayalso improve locality and hence cache utilization. It also does not havethe context-switch overhead of swapping globals. We have multiple toolsfor automating these transformations for different languages. Currently,there is a tool called Photran [3] for refactoring Fortran codesthat can do this transformation. It is Eclipse-based and works byconstructing Abstract Syntax Trees (ASTs) of the program. We also have atool built on top of the ROSE compiler [4] that works for C/C++ andFortran programs that is available upon request. It emits patches forall files containing global variables which can then be applied to thesource code.
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Ref. [20] used a heuristic approach for detecting malware by analyzing windows binary files of obfuscated executables. They have come up with a framework that first generates a risk score by statically analyzing the windows PE (See Appendix B) file for 8 characteristics (abnormal ordinals, Nonstd_name, In_code, TLSection, DLL_no_export, Flagged Section Name, Low function Call, Other_badPEformat). This framework assigns weight and risk score to each characteristic. The risk score is assigned based on experience and comparison between malware and benign files. A total of 2014 windows files were used in experiments.
A hybrid approach is also being used for taking benefit from the amalgam of malware detection methods. Ref. [23] focused on availing the advantages of all techniques for malware detection due to which the implemented framework by [23] is hybrid. They presented a framework that works on the detection methodology involving API calls extracted from the suspected file by running it in a VM environment. Then a graph is built using the information of API calls and operating system resources being utilized. Graph nodes represent API calls and operating system resources, and edges represent the reference between nodes. Then the constructed graph is minimized. Finally, to find a match between two graphs, the Graph Edit Distance algorithm is used, and to make use of this algorithm cost matrix is utilized.
Static Analysis: It refers to the phenomena of analyzing a file without executing it to keep the process of analysis safe. This approach includes the extraction of low-level information such as CFGs (Control Flow Graphs), DFGs (Data Flow Graphs), and system call analysis. Different tools can aid in static analysis such as IDAPro for disassembling the file. The static analysis gets failed when malware is obfuscated as it cannot penetrate through the packed samples as explained by [18]. 350c69d7ab
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