While network worms carry various payloads and may utilize any available exploits, they all have one common component -the propagation engine. Moreover, it is important to note that the number of conceptually distinct propagation engines employed by existing network worms is quite limited.This paper presents a novel signature-based approach for detecting attacks perpetrated by network worms as a manifestation of a semantic functionality performed by one of the few known propagation engines. We propose a novel methodology to recognize any semantic functionality in the system call domain through utilizing Colored Petri Nets. In this application, Petri Nets embody behavior-based signatures of the propagation engine functionalities. These signatures are indicative of the shell code activity in the first stage of the worm proliferation.We developed, tested and evaluated a Propagation Engine Detector (PED) system that detects activity of the worm shell code executed by a process during an attack. Moreover, PED is able to recognize the type of propagation engine employed by the attacking worm.

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Abstract. Over the past years, we have experienced an increase in the quantity and complexity of malware binaries. This change has been fueled by the introduction of malware generation tools and reuse of different malcode modules. Recent malware appears to be highly modular and less functionally typified. A side-effect of this "composition" of components across different malware types, a growing number of new malware samples cannot be explicitly assigned to traditional classes defined by Anti-Virus (AV) vendors. Indeed, by nature, clustering techniques capture dominant behavior that could be a manifestation of only one of the malware component failing to reveal malware similarities that depend on other, less dominant components and other evolutionary traits. In this paper, we introduce a novel malware behavioral commonality analysis scheme that takes into consideration component-wise grouping, called behavioral mapping. Our effort attempts to shed light to malware behavioral relationships and go beyond simply clustering the malware into a family. To this end, we implemented a method for identifying soft clusters and reveal shared malware components and traits. Using our method, we demonstrate that a malware sample can belong to several groups (clusters), implying sharing of its respective components with other samples from the groups. We performed experiments with a large corpus of real-world malware data-sets and identified that we can successfully highlight malware component relationships across the existing AV malware families and variants.

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