text: This paper presents a novel deep learning based method for automatic malware signature generation and classification. The method uses a deep belief network (DBN), implemented with a deep stack of denoising autoencoders, generating an invariant compact representation of the malware behavior. While conventional signature and token based methods for malware detection do not detect a majority of new variants for existing malware, the results presented in this paper show that signatures generated by the DBN allow for an accurate classification of new malware variants. Using a dataset containing hundreds of variants for several major malware families, our method achieves 98.6 classification accuracy using the signatures generated by the DBN. The presented method is completely agnostic to the type of malware behavior that is logged (e.g., API calls and their parameters, registry entries, websites and ports accessed, etc.), and can use any raw input from a sandbox to successfully train the deep neural network which is used to generate malware signatures.
  @cite_9: This paper presents a statistical model of the malware detection problem. Where Chess and White (An undetectable computer virus. In: Virus Bulletin Conference, 2000) just partially addressed this issue and gave only existence results, we give here constructive results of undetectable malware. We show that any existing detection techniques can be modelled by one or more statistical tests. Consequently, the concepts of false positive and non detection are precisely defined. The concept of test simulability is then presented and enables us to gives constructive results how undetectable malware could be developped by an attacker. Practical applications of this statistical model are proposed. Finally, we give a statistical variant of Cohen’s undecidability results of virus detection.
  @cite_14: This research proposes a novel automatic method (termed Auto-Sign) for extracting unique signatures of malware executables to be used by high-speed malware filtering devices based on deep-packet inspection and operating in real-time. Contrary to extant string and token-based signature generation methods, we implemented Auto-Sign an automatic signature generation method that can be used on large-size malware by disregarding signature candidates which appear in benign executables. Results from experimental evaluation of the proposed method suggest that picking a collection of executables which closely represents commonly used code, plays a key role in achieving highly specific signatures which yield low false positives.
summary: The majority of anti-virus programs reply on analyzing the executable file to determine whether it is a malware. As Filiol and Josse @cite_9 establish, most current anti-virus programs do not detects variant of malware. They propose a method for automatic signature generation by analyzing the executable's code and substrings, and measure statistical distribution of code across variants of malware. Their experiments were performed on short (small sized) malware such as Nimda, Code Red Code Red II, MS Blaster, Sober, Netsky and Beagle. This method is less accurate when applied to larger malware. Most real world malware are large, containing many modules and sub-modules, and so a statistical analysis would not be sufficient to accurately classify them. @cite_14 generates a list of signatures for a malware by splitting its executable to segments of equal sizes. For each segment a signature is generated, and the list of signatures is subsequently ranked. This method is more resilient to small modifications in the executable, but a malware can evade this method by encrypting the executable (which is a simple and popular method for many malware programs), and thus evading any method which inspects the executable file for signature comparison.