The proceedings contain 21 papers. The topics discussed include: a tale of three bio-inspiredcomputational approaches;probabilistic graphs using coupled random variables;evaluating data distribution and drift vulnera...
ISBN:
(纸本)9781628410563
The proceedings contain 21 papers. The topics discussed include: a tale of three bio-inspiredcomputational approaches;probabilistic graphs using coupled random variables;evaluating data distribution and drift vulnerabilities of machine learning algorithms in secure and adversarial environments;energy-efficient STDP-based learning circuits with memristor synapses;towards leakage resiliency: memristor-based AES design for differential power attack mitigation;hardware-based artificial neural networks for size, weight, and power constrained platforms;a reinforcement learning trained fuzzy neural network controller for maintaining wireless communication connections in multi-robot systems;a novel pipeline based FPGA implementation of a genetic algorithm;and applying hardware-based machine learning to signature-based network intrusion detection.
The proceedings contain 11 papers. The topics discussed include: optimization of background subtraction for image enhancement;statistical recognition of 3D objects using integral imaging;spatial context for moving veh...
ISBN:
(纸本)9780819495426
The proceedings contain 11 papers. The topics discussed include: optimization of background subtraction for image enhancement;statistical recognition of 3D objects using integral imaging;spatial context for moving vehicle detection in wide area motion imagery with multiple kernel learning;fusing video and text data by integrating appearance and behavior similarity;trusted computation through biologically inspired processes;a developmental approach to learning causal models for cyber security;computational intelligence and neuromorphic computing potential for cybersecurity applications;a pipelined FPGA implementation of an encryption algorithm based on genetic algorithm;vehicle tracking and analysis within a city;applying manifold learning to vehicle classification using vibrometry signatures;and electro-optical seasonal weather and gender data collection.
A modified version of the intelligent water drop algorithm for performing planning for air and ground robots based on telemetry provided by satellites has been created. The IWD algorithm works by simulating the flow o...
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ISBN:
(纸本)9781628410563
A modified version of the intelligent water drop algorithm for performing planning for air and ground robots based on telemetry provided by satellites has been created. The IWD algorithm works by simulating the flow of water drops in a stream-network, dynamically adapting drop and network characteristics. This paper presents the base IWD algorithm, a simplified version of the algorithm (SIWD) and a derivative of this simplified version that has been adapted and applied to planning air and ground robot paths based upon orbital (for aerial) and aerial (for ground) imagery. An analysis of the performance of the algorithm is presented.
What is presented here is a sequence of evolving concepts for network intrusion detection. These concepts start with neuromorphic structures for XOR-based signature matching and conclude with computationally based net...
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ISBN:
(纸本)9781628410563
What is presented here is a sequence of evolving concepts for network intrusion detection. These concepts start with neuromorphic structures for XOR-based signature matching and conclude with computationally based network intrusion detection system with an autonomous structuring algorithm. There is evidence that neuromorphic computation for network intrusion detection is fractal in nature under certain conditions. Specifically, the neural structure can take fractal form when simple neural structuring is autonomous. A neural structure is fractal by definition when its fractal dimension exceeds the synaptic matrix dimension. The authors introduce the use of fractal dimension of the neuromorphic structure as a factor in the autonomous restructuring feedback loop.
The advent of nanoscale metal-insulator-metal (MIM) structures with memristive properties has given birth to a new generation of hardware neural networks based on CMOS/memristor integration (CMHNNs). The advantage of ...
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ISBN:
(纸本)9781628410563
The advent of nanoscale metal-insulator-metal (MIM) structures with memristive properties has given birth to a new generation of hardware neural networks based on CMOS/memristor integration (CMHNNs). The advantage of the CMHNN paradigm compared to a pure CMOS approach lies in the multi-faceted functionality of memristive devices: They can efficiently store neural network configurations (weights and activation function parameters) via non-volatile, quasi-analog resistance states. They also provide high-density interconnects between neurons when integrated into 2-D and 3-D crossbar architectures. In this work, we explore the combination of CMHNN classifiers with manifold learning to reduce the dimensionality of CMHNN inputs. This allows the size of the CMHNN to be reduced significantly (by approximate to 9 7 %). We tested the proposed system using the Caltech101 database and were able to achieve classification accuracies within approximate to 1.5 % of those produced by a traditional support vector machine.
Due to supply chain threats it is no longer a reasonable assumption that traditional protections alone will provide sufficient security for enterprise systems. The proposed cognitive trust model architecture extends t...
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ISBN:
(纸本)9780819495426
Due to supply chain threats it is no longer a reasonable assumption that traditional protections alone will provide sufficient security for enterprise systems. The proposed cognitive trust model architecture extends the state-of-the-art in enterprise anti-exploitation technologies by providing collective immunity through backup and cross-checking, proactive health monitoring and adaptive/autonomic threat response, and network resource diversity.
An experimental study of a neural network modeled by an adaptive Lotka-Volterra system follows. With totally inhibitory connections, this system can be embedded in a simple classification network. This network is able...
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ISBN:
(纸本)9781628416107
An experimental study of a neural network modeled by an adaptive Lotka-Volterra system follows. With totally inhibitory connections, this system can be embedded in a simple classification network. This network is able to classify and monitor its inputs in a spontaneous nonlinear fashion without prior training. We describe a framework for leveraging this behavior through an example involving breast cancer diagnosis.
In today's highly mobile, networked, and interconnected internet world, the flow and volume of information is overwhelming and continuously increasing. Therefore, it is believed that the next frontier in technolog...
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ISBN:
(纸本)9780819495426
In today's highly mobile, networked, and interconnected internet world, the flow and volume of information is overwhelming and continuously increasing. Therefore, it is believed that the next frontier in technological evolution and development will rely in our ability to develop intelligent systems that can help us process, analyze, and make-sense of information autonomously just as a well-trained and educated human expert. In computational intelligence, neuromorphic computing promises to allow for the development of computing systems able to imitate natural neurobiological processes and form the foundation for intelligent system architectures.
Multilayer Perceptron Networks with random hidden layers are very efficient at automatic feature extraction and offer significant performance improvements in the training process. They essentially employ large collect...
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ISBN:
(纸本)9781510600911
Multilayer Perceptron Networks with random hidden layers are very efficient at automatic feature extraction and offer significant performance improvements in the training process. They essentially employ large collection of fixed, random features, and are expedient for form-factor constrained embedded platforms. In this work, a reconfigurable and scalable architecture is proposed for the MLPs with random hidden layers with a customized building block based on CORDIC algorithm. The proposed architecture also exploits fixed point operations for area efficiency. The design is validated for classification on two different datasets. An accuracy of approximate to 90% for MNIST dataset and 75% for gender classification on LFW dataset was observed. The hardware has 299 speed-up over the corresponding software realization.
Honeypot application is a source of valuable data about attacks on the network. We run several SIP honeypots in various computer networks, which are separated geographically and logically. Each honeypot runs on public...
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ISBN:
(纸本)9781510600911
Honeypot application is a source of valuable data about attacks on the network. We run several SIP honeypots in various computer networks, which are separated geographically and logically. Each honeypot runs on public IP address and uses standard SIP PBX ports. All information gathered via honeypot is periodically sent to the centralized server. This server classifies all attack data by neural network algorithm. The paper describes optimizations of a neural network classifier, which lower the classification error. The article contains the comparison of two neural network algorithm used for the classification of validation data. The first is the original implementation of the neural network described in recent work;the second neural network uses further optimizations like input normalization or cross-entropy cost function. We also use other implementations of neural networks and machine learning classification algorithms. The comparison test their capabilities on validation data to find the optimal classifier. The article result shows promise for further development of an accurate SIP attack classification engine.
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