We describe a novel approach to monitoring high level behaviors using concepts from AI planning. Our goal is to understand what a program is doing based on its system call trace. This ability is particularly important for detecting malware. We approach this problem by building an abstract model of the operating system using the STRIPS planning language, casting system calls as planning operators. Given a system call trace, we simulate the corresponding operators on our model and by observing the properties of the state reached, we learn about the nature of the original program and its behavior. Thus, unlike most statistical detection methods that focus on syntactic features, our approach is semantic in nature. Therefore, it is more robust against obfuscation techniques used by malware that change the outward appearance of the trace but not its effect. We demonstrate the efficacy of our approach by evaluating it on actual system call traces.
@cite_0 is considered the seminal work which pushed forward research on methods and representations of operating system process monitoring based on system calls. @cite_13 provides an early comparison of machine learning methods for modeling process behavior. @cite_2 introduces the model of execution graph, and behavior similarity-measure based on the execution graph. @cite_22 combines multiple models into an ensemble to improve anomaly detection. @cite_26 applies continuous time Bayesian network (CTBN) to system call processes to account for time-dependent features and address high variability of system call streams over time. @cite_4 applies a deep LSTM-based architecture to sequences of individual system calls, treating system calls as a language model.
@cite_26 @cite_4 @cite_22 @cite_0 @cite_2 @cite_13
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Intrusion detection systems (IDSs) are used to detect traces of malicious activities targeted against the network and its resources. Anomaly-based IDSs build models of the expected behavior of applications by analyzing events that are generated during the applications' normal operation. Once these models have been established, subsequent events are analyzed to identify deviations, on the assumption that anomalies represent evidence of an attack. Host-based anomaly detection systems often rely on system call sequences to characterize the normal behavior of applications. Recently, it has been shown how these systems can be evaded by launching attacks that execute legitimate system call sequences. The evasion is possible because existing techniques do not take into account all available features of system calls. In particular, system call arguments are not considered. We propose two primary improvements upon existing host-based anomaly detectors. First, we apply multiple detection models to system call arguments. Multiple models allow the arguments of each system call invocation to be evaluated from several different perspectives. Second, we introduce a sophisticated method of combining the anomaly scores from each model into an overall aggregate score. The combined anomaly score determines whether an event is part of an attack. Individual anomaly scores are often contradicting and, therefore, a simple weighted sum cannot deliver reliable results. To address this problem, we propose a technique that uses Bayesian networks to perform system call classification. We show that the analysis of system call arguments and the use of Bayesian classification improves detection accuracy and resilien Many host-based anomaly detection systems monitor a process by observing the system calls it makes, and comparing these calls to a model of behavior for the program that the process should be executing. In this paper we introduce a new model of system call behavior, called an execution graph. The execution graph is the first such model that both requires no static analysis of the program source or binary, and conforms to the control flow graph of the program. When used as the model in an anomaly detection system monitoring system calls, it offers two strong properties: (i) it accepts only system call sequences that are consistent with the control flow graph of the program; (ii) it is maximal given a set of training data, meaning that any extensions to the execution graph could permit some intrusions to go undetected. In this paper, we formalize and prove these claims. We additionally evaluate the performance of our anomaly detection technique. With the tremendous amount of research publications, recommending relevant papers to researchers to fulfill their information need becomes a significant problem. The major challenge to be tackled by our work is that given a target paper, how to effectively recommend a set of relevant papers from an existing citation network. In this paper, we propose a novel method to address the problem by incorporating various citation relations for a proper set of papers, which are more relevant but with a very limited size. The proposed method has two unique properties. Firstly, a metric called Local Relation Strength is defined to measure the dependency between cited and citing papers. Secondly, a model called Global Relation Strength is proposed to capture the relevance between two papers in the whole citation graph. We evaluate our proposed model on a real-world publication dataset and conduct an extensive comparison with the state-of-the-art baseline methods. The experimental results demonstrate that our method can have a promising improvement over the state-of-the-art techniques. Purpose  The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations.Design methodology approach  ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets  a citation network based on an early paper on webometrics, and a selfcitation network based on the 19 most cited papers in the Journal of Documentation  using citation data taken from the Web of Knowledge database.Findings  ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited ... Scholars are increasingly citing electronic web references which are not preserved in libraries or full text archives. WebCite is a new standard for citing web references. To webcite a document involves archiving the cited Web page through www.webcitation.org and citing the WebCite permalink instead of (or in addition to) the unstable live Web page. This journal has amended its instructions for authors accordingly, asking authors to archive cited Web pages before submitting a manuscript. Almost 200 other journals are already using the system. We discuss the rationale for WebCite, its technology, and how scholars, editors, and publishers can benefit from the service. Citing scholars initiate an archiving process of all cited Web references, ideally before they submit a manuscript. Authors of online documents and websites which are expected to be cited by others can ensure that their work is permanently available by creating an archived copy using WebCite and providing the citation information including the WebCite link on their Web document(s). Editors should ask their authors to cache all cited Web addresses (Uniform Resource Locators, or URLs) prospectively before submitting their manuscripts to their journal. Editors and publishers should also instruct their copyeditors to cache cited Web material if the author has not done so already. Finally, WebCite can process publisher submitted citing articles (submitted for example as eXtensible Markup Language [XML] documents) to automatically archive all cited Web pages shortly before or on publication. Finally, WebCite can act as a focussed crawler, caching retrospectively references of already published articles. Copyright issues Intrusion detection systems (IDSs) fall into two high-level categories: network-based systems (NIDS) that monitor network behaviors, and host-based systems (HIDS) that monitor system calls. In this work, we present a general technique for both systems. We use anomaly detection, which identifies patterns not conforming to a historic norm. In both types of systems, the rates of change vary dramatically over time (due to burstiness) and over components (due to service difference). To efficiently model such systems, we use continuous time Bayesian networks (CTBNs) and avoid specifying a fixed update interval common to discrete-time models. We build generative models from the normal training data, and abnormal behaviors are flagged based on their likelihood under this norm. For NIDS, we construct a hierarchical CTBN model for the network packet traces and use Rao-Blackwellized particle filtering to learn the parameters. We illustrate the power of our method through experiments on detecting real worms and identifying hosts on two publicly available network traces, the MAWI dataset and the LBNL dataset. For HIDS, we develop a novel learning method to deal with the finite resolution of system log file time stamps, without losing the benefits of our continuous time model. We demonstrate the method by detecting intrusions in the DARPA 1998 BSM dataset.