Master of Science (MS)
Typically, computer viruses and other malware are detected by searching for a string of bits which is found in the virus or malware. Such a string can be viewed as a “fingerprint” of the virus. These “fingerprints” are not generally unique; however they can be used to make rapid malware scanning feasible. This fingerprint is often called a signature and the technique of detecting viruses using signatures is known as signaturebased detection . Today, virus writers often camouflage their viruses by using code obfuscation techniques in an effort to defeat signature-based detection schemes. So-called metamorphic viruses are viruses in which each instance has the same functionality but differs in its internal structure. Metamorphic viruses differ from polymorphic viruses in the method they use to hide their signature. While polymorphic viruses primarily rely on encryption for signature obfuscation, metamorphic viruses hide their signature via “mutating” their own code . The paper  provides a rigorous proof that metamorphic viruses can bypass any signature-based detection, provided the code obfuscation has been done carefully based on a set of specified rules. Specifically, according to , if dead code is added and the control flow is changed sufficiently by inserting jump statements, the virus cannot be detected. In this project we first developed a code obfuscation engine conforming to the rules in . We then used this engine to create metamorphic variants of a seed virus (created using the PS-MPK virus creation kit ) and demonstrated the validity of the assertion in  about metamorphic viruses and signature based detectors. In the second phase of this project we validated another theory advanced in , namely, that machine learning based methods¾specifically ones based on Hidden Markov Model (HMM) ¾can detect metamorphic viruses. In other words, we show that a collection of metamorphic viruses which are (provably) undetectable via signature detection techniques can nevertheless be detected using an HMM approach.
Venkatesan, Ashwini, "Code Obfuscation and Virus Detection" (2008). Master's Projects. 116.