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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 Cyber Sensing

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)9781628410341

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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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

关键词： Cyber Security Network Intrusion Detection hardware machine learning

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Design of Intelligent EEG System for Human Emotion Recognition with Convolutional Neural Network 1

Design of Intelligent EEG System for Human Emotion Recogniti...

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1st IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)

作者： Wang, Kai-Yen Ho, Yun-Lung Huang, Yu-De Fang, Wai-Chi Natl Chiao Tung Univ Dept Elect Engn Hsinchu Taiwan Natl Chiao Tung Univ Inst Elect Hsinchu Taiwan Pervas Artificial Intelligence Res PAIR Labs Taipei Taiwan

ISBN: (纸本)9781538678848

Emotions play a significant role in the field of affective computing and Human-Computer Interfaces(HCI). In this paper, we propose an intelligent human emotion detection system based on EEG features with a multi-channel fused processing. We also proposed an advanced convolutional neural network that was implemented in VLSI hardware design. This hardware design can accelerate both the training and classification processes and meet real-time system requirements for fast emotion detection. The performance of this design was validated using DEAP [1] database with datasets from 32 subjects, the mean classification accuracy achieved is 83.88%.

关键词： Emotion Recognition Convolutional Neural Network Deep learning hardware machine learning Real-time EEG System Affective Computing

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A Customized Convolutional Neural Network Design Using Improved Softmax Layer for Real-time Human Emotion Recognition 1

A Customized Convolutional Neural Network Design Using Impro...

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1st IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)

作者： Wang, Kai-Yen Huang, Yu-De Ho, Yun-Lung Fang, Wai-Chi Natl Chiao Tung Univ Hsinchu Dept Elect Engn Hsinchu Taiwan Natl Chiao Tung Univ Hsinchu Inst Elect Hsinchu Taiwan Pervas Artificial Intelligence Res PAIR Labs Hsinchu Taiwan

ISBN: (纸本)9781538678848

This paper proposes an improved softmax layer algorithm and hardware implementation, which is applicable to an effective convolutional neural network of EEG-based real-time human emotion recognition. Compared with the general softmax layer, this hardware design adds threshold layers to accelerate the training speed and replace the Euler's base value with a dynamic base value to improve the network accuracy. This work also shows a hardware-friendly way to implement batch normalization layer on chip. Using the EEG emotion DEAP[7] database, the maximum and mean classification accuracy were achieved as 96.03% and 83.88% respectively. In this work, the usage of improved softmax layer can save up to 15% of training model convergence time and also increase by 3 to 5% the average accuracy.

关键词： Improved Softmax Layer Threshold Layer Batch Normalization Layer Convolutional Neural Network Deep learning hardware machine learning

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