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Intrusion Detection System: The Use of neural network Packet Classification

Intrusion Detection System: The Use of Neural Network Packet...

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2020 International conference on Computational Science and Computational Intelligence, CSCI 2020

作者： Ruiz, Nery Tavera, Bryan Abuzneid, Abdel-Shakour University of Bridgeport Department of Computer Science and Engineering BridgeportCT United States

ISBN: (纸本)9781728176246

Despite recent advances in cloud processing power and network connectivity to handle massive network traffic, networks are still vulnerable to distributed Denial of Service (DDoS) attacks. With the recent proliferation of the Internet of Things (IoT), unsecured devices are fueling the ever-growing botnets, which allow creating larger malicious networks. Current mitigation techniques need to adapt to a new growing size of zero-day attacks to protect network services to consumers and block malicious connections. Deep learning enables machines to find the solution to many complex problems. This paper evaluates the performance of the Simple neural networks, Convolutional neural networks, and Recurrent neural networks in detecting DDoS attacks when trained with the CSE-CIC-IDS2018 Dataset. This research will discuss the presented datasets and the efficiency of the proposed networks. The trained data was obtained from a realistic dataset that holds different forms of intrinsic volume, protocol, and web-based attacks. © 2020 IEEE.

关键词： Denial-of-service attack

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Collar Data Synthesis and Its Application for Generating Virtual Collar 3D Novel View from a Single-View

Collar Data Synthesis and Its Application for Generating Vir...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Na Guo Pei-Wei Tsai Hongwang Xiao Chang Wu Wenjuan Duan Department of Computing Technologies Swinburne University of Technology Melbourne Australia School of Mechanical Electrical &Information Engineering Shandong University Weihai Weihai China Beijing Academy Artificial Intelligence Beijing China Department of Computing Technologies Beijing Institute of Technology Beijing China

In recent years, virtual reality, big data and cloud computing technologies have all made rapid development, and they are also more and more closely combined with the apparel industry. The apparel digital design and virtual fitting techniques are playing increasingly important roles in the field of apparel design, manufacturing and sales. The three-dimensional digital design and virtual fitting make the designers or the users have multi-view and more real impressions and experiences of the apparel. But 3D display is difficult to be popularized in apparel industry because of its high cost. The cost of two-dimensional display is low and easy to popularize. However, 2D system can only provide images of a specific perspective, lacking a multi-perspective experience. If the supplier is required to provide 2D images of several specific perspectives, the personalized needs of users cannot be satisfied. Therefore, how to make 2D system according to an object image and users' needs to provide the object images of other perspectives is an urgent problem to be solved. In this work, we propose a novel view synthesis pipeline based on an enhanced Pix2Pix neural network for a novel view (Pix2Pix-V). It consists of four parts: 1) a new method of generating the labeled virtual collar image data based on its 3D model, and the 2D images can be obtained on any perspective; 2) Pix2Pix-V neural network; 3) a Pix2Pix-V training workflow for predicting the novel view; 4) transfer learning is used in predicting similar views of virtual collars.

关键词： Industries Solid modeling Three-dimensional displays Costs Pipelines Transfer learning neural networks

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Deep neural network Architectures for Spectrum Sensing Using Signal processing Features

Deep Neural Network Architectures for Spectrum Sensing Using...

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IEEE distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Shreeram Suresh Chandra Akshay Upadhye Purushothaman Saravanan Sanjeev Gurugopinath R. Muralishankar PES University Bengaluru India School of Engineering and Technology CMR University Bengaluru India

ISBN: (纸本)9781665429528

In this work, we consider a performance comparison of deep learning-based approaches to the problem of spectrum sensing (SS) in cognitive radios. Towards this end, we use signal processing (SP) features such as energy, differential entropy, geometric power and p-norm. For the classification problem of SS, we employ deep neural network (NN) architectures such as multi-layer perceptron (MLP), convolutional NN, fully convolutional network, residual NN (ResNet), long short-term memory and temporal convolutional network. Through extensive experiments based on real-world captured datasets and Monte Carlo simulations, we show that MLP and ResNet architectures offer the best performance in terms of probability of detection, for a given predefined level of probability of false-alarm. Further, we show that NN architectures trained with a combined set of the SP features yield the best performance.

关键词： Deep learning Monte Carlo methods Frequency modulation Convolution Computer architecture Very large scale integration Robot sensing systems

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A CASCADING INCREMENTAL TRAINING APPROACH FOR LARGE-SCALE distributed DATA BASED ON SUPPORT VECTOR MACHINE 17

A CASCADING INCREMENTAL TRAINING APPROACH FOR LARGE-SCALE DI...

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17th IEEE International Computer conference on Wavelet Active Media Technology and Information processing (ICCWAMTIP)

作者： Xu Yuanyuan Li Shucheng Li Fan Gu Xiaofeng Sun Rui Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu 610054 Peoples R China Sichuan Shengtian New Energy Dev Co Ltd 333 East Sect HuPan Rd Chengdu 610213 Peoples R China Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu 611731 Peoples R China

ISBN: (纸本)9780738142593

In this paper, we present a cascading incremental training approach for large-scale and unevenly distributed data based on Support Vector Machine. Instead of using training data directly, data's distribution and scale are concerned, so Synthetic Minority Oversampling Technique and Generative Adversarial networks are both used to synthesize new samples and add them to the data set to achieve a balanced sample number. Then, we propose a cascading model and divide samples into groups and apply the multi-layer convolutional neural network for pre-training to collect the embedding vectors of training samples. Finally, features extracted from the pre-training will be used for incremental training on Support Vector Machine. A multi-layer convolutional network and cluster are applied to maintain some key information and train features for classification. Additionally, uncertainty strategy is used to obtain potential features. In the comparative experiments with the above dataset, our proposed method performs the state-of-the-art competitors.

关键词： Incremental learning Classification Cascade model Convolutional network Clustering Support Vector Machine

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Edge Computing in Micro Data Centers for Firefighting in Residential Areas of Future Smart Cities

Edge Computing in Micro Data Centers for Firefighting in Res...

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2022 International conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022

作者： Gudepu, Venkateswarlu Pappu, Bhavani Javvadi, Tejasri Bassoli, Riccardo Fitzek, Frank H.P. Valcarenghi, Luca Devi, D.V.N. Kondepu, Koteswararao IIT Dharwad Department of Computer Science and Engineering Dharwad India RGUKT-Srikakulam Department of Computer Science and Engineering Srikakulam India RGUKT-Srikakulam Department of Electronics Communication Engineering Srikakulam India Technische Universitat Dresden Deutsche Telekom of Communication Networks Dresden Germany Telecommunications. Computer Engineering and Photonics Institute Scuola Superiore sant'Anna Pisa Italy RGUKT Nuzvid Department of Computer Science and Engineering Nuzvid India

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

5G standardization is going to reach its end, so research on 6G has started, driven by the scientific and industrial communities. 5G and especially 6G, are going to provide resources to enhance every aspect of human life via communication networks and computing. Among the different verticals, emergency services are one of the most important parts of making smart cities of the future a reality. In this context, firefighting is highly important for security and safety. However, firefighting requires ultra-reliable and low-latency communications since firefighters can be provided with advanced guidance during fire control with the employment of distributed sensors, robots, etc. Also, the use of machine learning algorithms is important for firefighters to analyze and make decisions based on the audiovisual and possibly tactile information they collect. Such a scenario cannot leverage the current cloud computing paradigm, which has significant latency issues. That is why it is important to study and design edge computing paradigms to address the goals of these scenarios and use cases since they can be capable of fulfilling latency and reliability requirements. In this situation, this work looks into how computing at Edge Micro Data Centers (EMDC) can be used to improve how fires are predicted and managed. We propose a novel three-stage architecture. The initial stage focuses on prediction and classification of the fire occurrence based on available sensor data at the EMDC, whereas the second stage deals with the fire occurrence confirmation using a convolutional neural network (CNN) classification model. After the fire occurrence has been confirmed, the final stage notifies the tenants and streams 360-degree monitoring video to the nearby fire station after processing at EMDC. The results showed that the proposed architecture can realize firefighting services with low latency. to the best of the authors' knowledge, this is the first work studying and experimentally evalu

关键词： network architecture

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A Retina-LGN-V1 Structure-like Spiking Neuron network for Image Feature Extraction 21

A Retina-LGN-V1 Structure-like Spiking Neuron Network for Im...

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Proceedings of the 2021 5th International conference on Video and Image processing

作者： Sheng Ji Qijun Gu Ye Yuan Peng Zhao Tao Fang Hong Huo Xin Niu Department of Automation_School of Electronic Information and Electrical Engineering_Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University China College of Computer Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology China

ISBN: (纸本)9781450385893

SNNs have achieved great attention in recent years as they contain neurons more like those in the brain and use spikes to encode and transmit information efficiently among neurons with lower energy consumption. A Retina-LGN-V1 structure-like spiking neuron network (RLVSL-SNN) is proposed in this paper. It is inspired by the structure of mammalian primary visual pathway, and simulates different biological structures of Retina, LGN and V1. Noise reduction circuit and light adaptation circuit are also simulated for enhancing the robustness of its extracted features. RLVSL-SNN is a bio-plausible neuron network as it has firing rates of neurons in each layer that are similar to those of biological experiments. Besides, a full-connected SNN (FC SNN) is implemented following RLVSL-SNN for classification to evaluate the extracted features. The additive spiking timing dependent plasticity (STDP) learning rules and the ANN-to-SNN conversion method are utilized to train RLVSL-SNN and FC SNN, respectively. The experiments on MNIST dataset have verified that RLVSL-SNN is comparable to AlexNet for classification by features from spikes.

关键词： Additive Spiking Timing Dependent Plasticity Learning Primary Visual Pathway Image Classification Spiking neural network

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ADDRESSING THE POLYSEMY PROBLEM IN LANGUAGE MODELING WITH ATTENTIONAL MULTI-SENSE EMBEDDINGS

ADDRESSING THE POLYSEMY PROBLEM IN LANGUAGE MODELING WITH AT...

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IEEE International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Ma, Rao Jin, Lesheng Liu, Qi Chen, Lu Yu, Kai Shanghai Jiao Tong Univ Dept Comp Sci & Engn SpeechLab MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9781509066315

neural network language models have gained considerable popularity due to their promising performance. distributed word embeddings are utilized to represent semantic information. However, each word is associated with a single vector in the embedding layer, disabling the model from capturing the meanings of polysemous words. In this work, we address this problem by assigning multiple fine-grained sense embeddings to each word in the embedding layers. The proposed model discriminates among different senses of a word with attention mechanism in an unsupervised manner. Experiments demonstrate the benefits of our approach in language modeling and ASR rescoring. Investigations are also made on standard word similarity tasks. The results indicate that our proposed method is efficient in modeling polysemy and therefore obtains better word representations.

关键词： language modeling multi-sense embeddings polysemy attention models distributed representation

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47th International conference on Very Large Data Bases, VLDB 2021

47th International Conference on Very Large Data Bases, VLDB...

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47th International conference on Very Large Data Bases, VLDB 2021

The proceedings contain 305 papers. The special focus in this conference is on Very Large Data Bases. The topics include: Scaling attributed network embedding to massive graphs;Jointly optimizing preprocessing and inference for DNN-based visual analytics;Real-time distance-based outlier detection in data streams;Improving execution efficiency of just-in-time compilation based query processing on GPUs;Aggregated deletion propagation for counting conjunctive query answers;Multi-modal transportation recommendation with unified route representation learning;Toward a better understanding and evaluation of tree structures on flash SSDs;Answering multi-dimensional range queries under local differential privacy;Capturing and querying fine-grained provenance of preprocessing pipelines in data science;Towards an efficient weighted random walk domination;Maximizing social welfare in a competitive diffusion model;Quality of sentiment analysis tools;Efficient streaming subgraph Isomorphism with graph neural networks;Hierarchical core maintenance on large dynamic graphs;Optimizing an in-memory database system for AI-powered on-line decision augmentation using persistent memory;Improving information extraction from visually rich documents using visual span representations;Optimization of threshold functions over streams;Building enclave-native storage engines for practical encrypted databases;Randomized error removal for online spread estimation in data streaming;Collective influence maximization for multiple competing products with an awareness-to-influence model;Shortest paths and centrality in uncertain networks;Adaptive data augmentation for supervised learning over missing data;distributed numerical and machine learning computations via two-phase execution of aggregated join trees;Symmetric continuous subgraph matching with bidirectional dynamic programming;Fast augmentation algorithms for network kernel density visualization;Frequency estimation under local differential priv

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47th International conference on Very Large Data Bases, VLDB 2021

47th International Conference on Very Large Data Bases, VLDB...

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47th International conference on Very Large Data Bases, VLDB 2021