A neural-network learning theory and a polynomial time RBF algorithm

Roy, Asim; Govil, Sandeep; Miranda, R.

doi:10.1109/72.641453

Cited by 74 publications

(25 citation statements)

References 27 publications

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“…The computational expense of model-based clustering approaches varies between approaches, but is often higher order [19]. A notable exception to this is Kohonen's Self-Organizing Map (SOM) approach-an unsupervised neural network approach [20].…”

Section: Algorithm Selectionmentioning

confidence: 99%

Detecting threatening insiders with lightweight media forensics

Garfinkel¹,

Beebe

Liu

et al. 2013

2013 IEEE International Conference on Technologies for Homeland Security (HST)

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Abstract-This research uses machine learning and outlier analysis to detect potentially hostile insiders through the automated analysis of stored data on cell phones, laptops, and desktop computers belonging to members of an organization. Whereas other systems look for specific signatures associated with hostile insider activity, our system is based on the creation of a "storage profile" for each user and then an automated analysis of all the storage profiles in the organization, with the purpose of finding storage outliers. Our hypothesis is that malicious insiders will have specific data and concentrations of data that differ from their colleagues and coworkers. By exploiting these differences, we can identify potentially hostile insiders.Our system is based on a combination of existing open source computer forensic tools and datamining algorithms. We modify these tools to perform a "lightweight" analysis based on statistical sampling over time. In this, our approach is both efficient and privacy sensitive. As a result, we can detect not just individuals that differ from their co-workers, but also insiders that differ from their historic norms. Accordingly, we should be able to detect insiders that have been "turned" by events or outside organizations. We should also be able to detect insider accounts that have been taken over by outsiders.Our project, now in its first year, is a three-year project funded by the Department of Homeland Security, Science and Technology Directorate, Cyber Security Division. In this paper we describe the underlying approach and demonstrate how the storage profile is created and collected using specially modified open source tools. We also present the results of running these tools on a 500GB corpus of simulated insider threat data created by the Naval Postgraduate School in 2008 under grant from the National Science Foundation.

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Section: Algorithm Selectionmentioning

confidence: 99%

Detecting threatening insiders with lightweight media forensics

Garfinkel¹,

Beebe

Liu

et al. 2013

2013 IEEE International Conference on Technologies for Homeland Security (HST)

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show abstract

“…Engineers are perpetually confronted with a tradeoff between generalization and robustness. While adding a multitude of neurons increases the system's fault tolerance, there is a risk of overfitting if we do not attempt to generalize [109]. Implementing neurons in hardware is generally quite expensive, so it is imperative that cost-effectiveness be considered, trying to obtain the smallest possible network.…”

Section: Conclusion: Toward Novel Bio-inspired Hardware Systemsmentioning

confidence: 99%

A phylogenetic, ontogenetic, and epigenetic view of bio-inspired hardware systems

Sipper

Sánchez

Mange

et al. 1997

IEEE Trans. Evol. Computat.

175

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Abstract-If one considers life on Earth since its very beginning, three levels of organization can be distinguished: the phylogenetic level concerns the temporal evolution of the genetic programs within individuals and species, the ontogenetic level concerns the developmental process of a single multicellular organism, and the epigenetic level concerns the learning processes during an individual organism's lifetime. In analogy to nature, the space of bio-inspired hardware systems can be partitioned along these three axes, phylogeny, ontogeny, and epigenesis, giving rise to the POE model. This paper is an exposition and examination of bio-inspired systems within the POE framework, with our goals being: 1) to present an overview of current-day research, 2) to demonstrate that the POE model can be used to classify bioinspired systems, and 3) to identify possible directions for future research, derived from a POE outlook. We first discuss each of the three axes separately, considering the systems created to date and plotting directions for continued progress along the axis in question. We end our exposition by a discussion of possible research directions, involving the construction of bio-inspired systems that are situated along two, and ultimately all three axes. This could give rise to novel systems endowed with evolutionary, reproductive, regenerative, and learning capabilities.

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“…The basic ant colony algorithm also has been put forward to train the artificial neural network (ANN). Though it can overcome the fault of the BP to some extent [6] , the convergence speed is still not fast.…”

Section: Introductionmentioning

confidence: 99%

A novel approach of hybrid genetic optimization with elite ant colony: Application in the modulation recognition

Liu

Wang

Lang

2011

Wuhan Univ. J. Nat. Sci.

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A novel approach (HGO-EAC) for hybrid genetic optimization (GO) with elite ant colony (EAC) is proposed for the automatic modulation recognition of communication signals, through which we improve the basic ant colony algorithms by referencing elite strategy and present a new fusion strategy for genetic optimization and elite ant colony. This approach is used to train the neural networks as the classifier for modulation. Simulation results indicate good performance on an additive white Gaussian noise (AWGN) channel, with recognition rate reaching to 70% especially for CW even at signal-to-noise ratios as low as 5 dB. This approach can achieve a high recognition rate for the typical modulations such as CW, 4ASK, 4FSK, BPSK, and QAM16. Test result shows that it has better performance than BP algorithm and basic ant colony algorithms by achieving faster training and stronger robustness.

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A neural-network learning theory and a polynomial time RBF algorithm

Cited by 74 publications

References 27 publications

Detecting threatening insiders with lightweight media forensics

Detecting threatening insiders with lightweight media forensics

A phylogenetic, ontogenetic, and epigenetic view of bio-inspired hardware systems

A novel approach of hybrid genetic optimization with elite ant colony: Application in the modulation recognition

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