Rootkits constitute a significant threat to modern computing and information systems. Since their first appearance in the early 1990's they have steadily evolved, adapting to ever-improving security measures. The main feature rootkits have in common is the ability to hide their malicious presence and activities from the operating system and its legitimate users. In this paper we systematically analyze process hiding techniques routinely used by rootkit malware. We summarize the characteristics of different approaches and discuss their advantages and limitations. Furthermore, we assess detection and prevention techniques introduced in operating systems in response to the threat of hidden malware. The results of our assessments show that defenders still struggle to keep up with rootkit authors. At the same time we see a shift towards powerful VM-based techniques that will continue to evolve over the coming years.
Anomaly detection systems need to consider a lot of information when scanning for anomalies. One example is the context of the process in which an anomaly might occur, because anomalies for one process might not be anomalies for a different one. Therefore data -such as system events -need to be assigned to the program they originate from. This paper investigates whether it is possible to infer from a list of system events the program whose behavior caused the occurrence of these system events. To that end, we model transition probabilities between nonequivalent events and apply the k-nearest neighbors algorithm. This system is evaluated on nonmalicious, real-world data using four different evaluation scores. Our results suggest that the approach proposed in this paper is capable of correctly inferring program names from system events.
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