2,105 research outputs found
Why Just Boogie? Translating Between Intermediate Verification Languages
The verification systems Boogie and Why3 use their respective intermediate
languages to generate verification conditions from high-level programs. Since
the two systems support different back-end provers (such as Z3 and Alt-Ergo)
and are used to encode different high-level languages (such as C# and Java),
being able to translate between their intermediate languages would provide a
way to reuse one system's features to verify programs meant for the other. This
paper describes a translation of Boogie into WhyML (Why3's intermediate
language) that preserves semantics, verifiability, and program structure to a
large degree. We implemented the translation as a tool and applied it to 194
Boogie-verified programs of various sources and sizes; Why3 verified 83% of the
translated programs with the same outcome as Boogie. These results indicate
that the translation is often effective and practically applicable
Classification of Polymorphic Virus Based on Integrated Features
Standard virus classification relies on the use of virus function, which is a small number of bytes written in assembly language. The addressable problem with current malware intrusion detection and prevention system is having difficulties in detecting unknown and multipath polymorphic computer virus solely based on either static or dynamic features. Thus, this paper presents an effective and efficient polymorphic classification technique based on integrated features. The integrated feature is selected based on Information Gain (IG) rank value between static and dynamic features. Then, all datasets are tested on Naïve Bayes and Random Forest classifiers. We extracted 49 features from 700 polymorphic computer virus samples from Netherland Net Lab and VXHeaven, which includes benign and polymorphic virus function. We spilt the dataset based on 60:40 split ratio sizes for training and testing respectively. Our proposed integrated features manage to achieve 98.9% of accuracy value
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