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This paper introduces the computer security domain of anomaly detection and formulates it as a machine learning task on temporal sequence data. In this domain, the goal is to develop a model or profile of the normal working state of a system user and to detect anomalous conditions as long-term deviations from the expected behavior patterns. We introduce two approaches to this problem: one employing instance-based learning (IBL) and the other using hidden Markov models (HMMs). Though not suitable for a comprehensive security solution, both approaches achieve anomaly identification performance sufficient for a low-level focus of attention detector in a multitier security system. Further, we evaluate model scaling techniques for the two approaches: two clustering techniques for the IBL approach and variation of the number of hidden states for the HMM approach. We find that over both model classes and a wide range of model scales, there is no significant difference in performance at recognizing the profiled user. We take this invariance as evidence that, in this security domain, limited memory models (e.g., fixed-length instances or low-order Markov models) can learn only part of the user identity information in which we're interested and that substantially different models will be necessary if dramatic improvements in user-based anomaly detection are to be achieved. 相似文献
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Sergey M. Plis Vamsi K. Potluru Terran Lane Vince D. Calhoun 《Journal of Signal Processing Systems》2011,64(3):351-359
A novel direct digital frequency synthesizer (DDFS) based on a parabolic polynomial with an offset is proposed in this paper.
A 16-segment parabolic polynomial interpolation is adopted to replace the traditional ROM-based phase-to-amplitude conversion
methods. Besides, the proposed parabolic polynomial interpolation is realized in a multiplier-less structure such that the
speed can be significantly improved. This work is manufactured by a standard 0.13 μm CMOS cell-based technology. The maximum
clock rate is 161 MHz, the core area is 0.33 mm2, and the spurious free dynamic range (SDRF) is 117 dBc by physical measurements on silicon. 相似文献
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Graph-based malware detection using dynamic analysis 总被引:1,自引:0,他引:1
Blake Anderson Daniel Quist Joshua Neil Curtis Storlie Terran Lane 《Journal in Computer Virology》2011,7(4):247-258
We introduce a novel malware detection algorithm based on the analysis of graphs constructed from dynamically collected instruction
traces of the target executable. These graphs represent Markov chains, where the vertices are the instructions and the transition
probabilities are estimated by the data contained in the trace. We use a combination of graph kernels to create a similarity
matrix between the instruction trace graphs. The resulting graph kernel measures similarity between graphs on both local and
global levels. Finally, the similarity matrix is sent to a support vector machine to perform classification. Our method is
particularly appealing because we do not base our classifications on the raw n-gram data, but rather use our data representation to perform classification in graph space. We demonstrate the performance
of our algorithm on two classification problems: benign software versus malware, and the Netbull virus with different packers
versus other classes of viruses. Our results show a statistically significant improvement over signature-based and other machine
learning-based detection methods. 相似文献
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Matthew R. Lakin Amanda Minnich Terran Lane Darko Stefanovic 《Journal of the Royal Society Interface》2014,11(101)
Learning and adaptive behaviour are fundamental biological processes. A key goal in the field of bioengineering is to develop biochemical circuit architectures with the ability to adapt to dynamic chemical environments. Here, we present a novel design for a biomolecular circuit capable of supervised learning of linear functions, using a model based on chemical reactions catalysed by DNAzymes. To achieve this, we propose a novel mechanism of maintaining and modifying internal state in biochemical systems, thereby advancing the state of the art in biomolecular circuit architecture. We use simulations to demonstrate that the circuit is capable of learning behaviour and assess its asymptotic learning performance, scalability and robustness to noise. Such circuits show great potential for building autonomous in vivo nanomedical devices. While such a biochemical system can tell us a great deal about the fundamentals of learning in living systems and may have broad applications in biomedicine (e.g. autonomous and adaptive drugs), it also offers some intriguing challenges and surprising behaviours from a machine learning perspective. 相似文献
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