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检索条件"任意字段=Conference on Machine Intelligence and Bio-inspired Computation - Theory and Applications VIII"

共 45 条记录，以下是1-10 订阅

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machine intelligence and bio-inspired computation: theory and applications viii

Machine Intelligence and Bio-inspired Computation: Theory an...

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machine intelligence and bio-inspired computation: theory and applications viii

ISBN: (纸本)9781628410563

The proceedings contain 21 papers. The topics discussed include: a tale of three bio-inspired computational 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.

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AHaH computing with thermodynamic RAM: bridging the technology stack

AHaH computing with thermodynamic RAM: bridging the technolo...

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conference on machine intelligence and bio-inspired computation - theory and applications viii

作者： Nugent, Alex M Alexander Nugent Consulting Santa Fe NM 87505 USA

We introduce the motivations behind AHaH computing, an emerging new form of adaptive computing with many applications in machine learning. We then present a technology stack or specification describing the multiple le... 详细信息

ISBN: (纸本)9781628410563

关键词： computing neural adaptive NPU machine learning memristor plasticity electronics

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Intelligent Water Drops for Aerospace and Defense applications

Intelligent Water Drops for Aerospace and Defense Applicatio...

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conference on machine intelligence and bio-inspired computation - theory and applications viii

作者： Straub, Jeremy Univ N Dakota Dept Comp Sci Grand Forks ND 58202 USA

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.

关键词： Intelligent water drop path planning swarm intelligence UAV planning aircraft planning ground robot planning performance analysis

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Neuromorphic Computing applications for Network Intrusion Detection Systems

Neuromorphic Computing Applications for Network Intrusion De...

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conference on machine intelligence and bio-inspired computation - theory and applications viii

作者： Garcia, Raymond C. Pino, Robinson E. ICF Int Fairfax VA 22031 USA

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.

关键词： Intrusion Detection Neuromorphic Security Fractal Autonomous Neural Structuring Cloud Computing

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Heterogeneous CMOS/Memristor Hardware Neural Networks for Real-Time Target Classification

Heterogeneous CMOS/Memristor Hardware Neural Networks for Re...

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conference on machine intelligence and bio-inspired computation - theory and applications viii

作者： Merkel, Cory Kudithipudi, Dhireesha Ptucha, Ray Rochester Inst Technol NanoComp Res Lab Rochester NY 14623 USA Rochester Inst Technol Machine Intelligence Lab Rochester NY USA

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.

关键词： Memristor Neural Network Object Classification Dimensionality Reduction

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Applying hardware-based machine learning to signature-based network intrusion detection

Applying hardware-based machine learning to signature-based ...

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conference on machine intelligence and bio-inspired computation - theory and applications viii

作者： Payer, Garrett McCormick, Chris Harang, Richard ICF Int 9300 Lee Hwy Fairfax VA 22031 USA CogniMem Technol Folsom CA 95630 USA

ISBN: (纸本)9781628410563

We present a proof-of-concept of a lightweight and low-power network intrusion detection system (NIDS) using a commercially available neural network chip. Such a system is well-suited to the increasing deployment of low-power devices with ubiquitous internet connectivity. Our proposal makes use of previous work on extracting a feature vector from network packets using a histogram of hashed n-grams. The commercially available CogniMem CM1K device implements a version of the Restricted Coulomb Energy neural network classifier, which was used to classify the resulting feature vectors at high speed and low power. In this paper, we describe our feature extraction technique for network packets and the classification algorithm used by the CM1K chip, and present initial classification results on a fabricated test set. Despite the generality of the RCE algorithm and our 'plug-in' approach to the classification task, with no fine-tuning of the hardware to our problem domain, we obtain surprisingly good classification results even on highly imbalanced and restricted training sets.

关键词： Cyber Security Network Intrusion Detection Hardware machine Learning

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Evaluating data distribution and drift vulnerabilities of machine learning algorithms in secure and adversarial environments

Evaluating data distribution and drift vulnerabilities of ma...

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conference on machine intelligence and bio-inspired computation - theory and applications viii

作者： Nelson, Kevin Corbin, George Blowers, Misty BAE Syst 581 Phoenix Dr Rome NY 13441 USA Air Force Res Lab 525 Brooks Rd Rome NY 13441 USA

ISBN: (纸本)9781628410563

machine learning is continuing to gain popularity due to its ability to solve problems that are difficult to model using conventional computer programming logic. Much of the current and past work has focused on algorithm development, data processing, and optimization. Lately, a subset of research has emerged which explores issues related to security. This research is gaining traction as systems employing these methods are being applied to both secure and adversarial environments. One of machine learning's biggest benefits, its data-driven versus logic-driven approach, is also a weakness if the data on which the models rely are corrupted. Adversaries could maliciously influence systems which address drift and data distribution changes using re-training and online learning. Our work is focused on exploring the resilience of various machine learning algorithms to these data-driven attacks. In this paper, we present our initial findings using Monte Carlo simulations, and statistical analysis, to explore the maximal achievable shift to a classification model, as well as the required amount of control over the data.

关键词： Adversarial machine Learning Intrusion Detection Monte Carlo Hidden Markov Models

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Trusted computation through biologically inspired processes

Trusted computation through biologically inspired processes

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conference on machine intelligence and bio-inspired computation - theory and applications VII

作者： Anderson, Gustave W. MacAulay Brown Inc MacB Roanoke VA USA

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.

关键词： Cloud MapReduce Trust Redundancy Security Information Assurance Information Security Cybersecurity

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Experimental analysis of a Lotka-Volterra neural network for classification 2

Experimental analysis of a Lotka-Volterra neural network for...

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conference on Next-Generation Robotics II and machine intelligence and bio-inspired computation - theory and applications IX

作者： Sukhu, Christopher L. Stanton, Joseph Aylesworth, Marc BAE Syst Intelligence & Secur Rome NY 13441 USA

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.

关键词： Lotka-Volterra neural network classification

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The Application of Top-Down Abstraction Learning Using Prediction as a Supervisory Signal to Cyber Security

The Application of Top-Down Abstraction Learning Using Predi...

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conference on machine intelligence and bio-inspired computation - theory and applications viii

作者： Mugan, Jonathan Khalili, Aram 21CT Inc Austin TX 78730 USA

ISBN: (纸本)9781628410563

Current computer systems are dumb automatons, and their blind execution of instructions makes them open to attack. Their inability to reason means that they don't consider the larger, constantly changing context outside their immediate inputs. Their nearsightedness is particularly dangerous because, in our complex systems, it is difficult to prevent all exploitable situations. Additionally, the lack of autonomous oversight of our systems means they are unable to fight through attacks. Keeping adversaries completely out of systems may be an unreasonable expectation, and our systems need to adapt to attacks and other disruptions to achieve their objectives. What is needed is an autonomous controller within the computer system that can sense the state of the system and reason about that state. In this paper, we present Self-Awareness Through Predictive Abstraction Modeling (SATPAM). SATPAM uses prediction to learn abstractions that allow it to recognize the right events at the right level of detail. These abstractions allow SATPAM to break the world into small, relatively independent, pieces that allow employment of existing reasoning methods. SATPAM goes beyond classification-based machine learning and statistical anomaly detection to be able to reason about the system, and SATPAM's knowledge representation and reasoning is more like that of a human. For example, humans intuitively know that the color of a car is not relevant to any mechanical problem, and SATPAM provides a plausible method whereby a machine can acquire such reasoning patterns. In this paper, we present the initial experimental results using SATPAM.

关键词： machine learning automation automation assurance autonomic computing verification and validation cyber resilience causal models

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