By Heng Yin

Malicious software program (i.e., malware) has turn into a serious possibility to interconnected computers for many years and has prompted billions of bucks damages every year. a wide quantity of recent malware samples are came across day-by-day. Even worse, malware is quickly evolving changing into extra subtle and evasive to strike opposed to present malware research and protection structures.

Automatic Malware research presents a virtualized malware research framework that addresses universal demanding situations in malware research. with regard to this new research framework, a chain of study thoughts for computerized malware research is constructed. those recommendations catch intrinsic features of malware, and are like minded for facing new malware samples and assault mechanisms.

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Additional resources for Automatic Malware Analysis: An Emulator Based Approach

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From the above discussion, we specify the following policies: (1) text, password, FTP, UDP and ICMP inputs cannot be accessed by the samples; (2) URL, HTTP, HTTPS and document inputs cannot be leaked by the samples; (3) directory inputs cannot be accessed excessively by the samples. type ∈ {file, network}) → Violate(v, “No Leakage! 3) In addition to manually specifying the policies, it is possible to automatically generate policies by using machine learning techniques. First, we can gather a representative collection of malware and benign samples as our training set.

A malicious program C attempts to change a memory location L of the operating system, to implant a hook H. When a certain event happens, the operating system will load the hook H, and then starts to execute malicious code F in program C. We refer to the address of F as hook entry, and L as hook site. H. Yin and D. 1007/978-1-4614-5523-3 5, © The Author(s) 2013 43 44 5 Hooking Behavior Analysis Fig. ServiceTableBase \ [*(PULONG)((PUCHAR)_function+1)] void HookSyscalls() { ... OldZwOpenKey = SYSTEMSERVICE(ZwOpenKey); SYSTEMSERVICE(ZwOpenKey) = NewZwOpenKey; ...

This is related to previous work that performs forensic analysis based on information flows. For example, some systems track the flow of information between operating system processes to perform intrusion analysis [16], intrusion recovery [12], and malware removal [15]. However, these systems typically monitor the system call interface and thus, are not as comprehensive and do not provide the same level of precision as our technique. Another limitation of previous systems is that it is often not possible to precisely track data while it is processed by a program.

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