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Graph Attention network Towards Detection and Mitigation of distributed Denial of Service Attack to IoT Servers

Graph Attention Network Towards Detection and Mitigation of ...

引用

International conference on Inventive Computation Technologies (ICICT)

作者： M Abirami K Vanitha Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Internet of Things is a revolutionizing as next generation technology which offers real time objects or things to be connected to the internet to provide effective communication among them. Despite of multiple advantages of the Internet of things, particular environment undergoes several threats like data breaches and lack of the resource availability due to its heterogeneous nature. Especially denial of service attack which leads to lack of the resource availability is serious concern in the IoT networks and servers. Botnet has been used to launch the DDOS attacks. Hence it becomes mandatory to detect and prevent the DDOS attack propagation to IoT server through IoT network. Traditionally many intrusion detection systems have been developed to the DDOS attacks using machine learning and deep learning architectures. Those architecture does not performs well for the diversity and evolving patterns of the attacks. In this paper, a graph attention network is designed to detect and mitigate the evolving attacks patterns of the DDOS attacks through Botnet on measuring the state changes of the network traffic. Initially traffic data composed of the several attributes for normal traffic and attack traffic are preprocessed using missing value imputation and normalization approaches. Preprocessed traffic data is transformed to graph structure data. Those graph structured data is employed to graph attention network which composed of attention mechanism and convolution neural network for processing the graph transformed data. In Graph transformed data, attribute of the network traffic data is considered as node and its association between the attributes of neighboring node is considered as edge. Especially graph attention mechanism assigns different weights to the attributes represented as nodes and its association represented as edges on processing graph data through adjacency matrix. Further it efficiently captures both local and global association among the traffic informat

关键词： Attention mechanisms Prevention and mitigation Telecommunication traffic Denial-of-service attack Feature extraction Robustness Imputation Performance analysis Servers Internet of Things

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Optimizing Resource Allocation in Pipeline Parallelism for distributed DNN Training

Optimizing Resource Allocation in Pipeline Parallelism for D...

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International conference on Parallel and distributed Systems (ICPADS)

作者： Yubin Duan Jie Wu Temple University USA

Deep neural network (DNN) models have been widely deployed in a variety of applications. Driven by privacy concerns and great improvement in the computational power of mobile devices, the idea of training machine learning models on mobile devices has become more and more important. Directly applying parallel training frameworks designed for data center networks to train DNN models on mobile devices may not achieve the ideal performance, since mobile devices usually have multiple types of computation resources such as ASIC, neural engine, and FPGA. Moreover, the communication time is not negligible when training on mobile devices. With the objective of minimizing DNN training time, we propose to extend the pipeline parallelism, which can hide the communication time behind computation for DNN training by integrating the resource allocation. Fine-tuning the ratio of resources allocated to forward and backward propagation can improve resource utilization. We focus on homogeneous workers and theoretically analyze the ideal cases where resources are linearly separable. We also discuss the model partition and resource allocation for a more realistic case. Additionally, we investigate the heterogeneous worker case. Trace-based simulation results show that our scheme can efficiently reduce the time cost of a training iteration.

关键词： Training Performance evaluation Privacy Computational modeling Simulation Pipelines Parallel processing

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Multi-perspective Enhancement Of Text Semantic Matching 23

Multi-perspective Enhancement Of Text Semantic Matching

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Proceedings of the 2023 7th International conference on Natural Language processing and Information Retrieval

作者： Hongchun Yu Wei Pan School of Computer Science China West Normal University China and Nanchong Key Laboratory of IoT Awareness and Distributed Computing China

ISBN: (纸本)9798400709227

With the widespread adoption of social media, text matching tasks have gained a prominent role in the field of natural language processing. However, traditional text matching methods often overly rely on shallow features and do not adequately consider the contextual semantic information within the text. With the rise of deep neural networks, deep learning methods have ushered in new directions in the field of text matching, enabling us to better understand and process semantic information within text. We introduce a framework that combines various models and techniques to more accurately assess semantic similarity between Chinese texts. Within this framework, we integrate the contextual understanding capability of BERT with neural network architectures such as Bidirectional Long Short-Term Memory (BiLSTM), Convolutional neural networks (CNN), and self-attention mechanisms. Through the fusion of multi-perspective information, we are better equipped to handle semantic ambiguities and polysemy in text matching tasks, ultimately improving matching performance. Extensive experimental evaluations were conducted on publicly available Chinese datasets, BQ and LCQMC. The results demonstrate that our multi-perspective textual semantic matching approach has significantly outperformed existing methods in terms of performance.

关键词： BERT multi-perspective semantic matching text analysis

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Optimizing UAV Base Station Positioning through Quantum-Inspired Solution Workflow

Optimizing UAV Base Station Positioning through Quantum-Insp...

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International conference on distributed Computing in Sensor Systems (DCOSS)

作者： M Saravanan Viswanath Pathmanaban Ericsson Research Ericsson India Global Services Pvt. Ltd India Department of Information Science and Technology Anna University Chennai India

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

UAV base stations play a pivotal role in providing wireless connectivity where terrestrial networks face limitations. User Equipment (UE) relies on these UAV-based solutions for enhanced data rates and coverage. This novel approach incorporates Spiking neural networks (SNNs) within Reinforcement Learning (RL) frameworks, a pioneering exploration in this field. The principal aims are to optimize data rates for User Equipment (UE) while minimizing latency, expanding coverage, and improving network performance, all within the limitations of the restricted number of Unmanned Aerial Vehicle (UAV) base stations. Employing a quantum-inspired clustering technique, Q-Means, we optimize UE allocation to UAV base stations, significantly reducing computational complexity. SNNs, capable of event-based information processing, offer more efficient computations and resilience in noisy environments. This innovative synergy of SNNs with RL is instrumental in optimizing UAV placement, addressing the burgeoning demand for improved wireless connectivity in a variety of settings. The utilization of non-orthogonal multiple access (NOMA) further underscores the crucial role of UAV base stations in meeting evolving connectivity challenges.

关键词： Wireless communication Base stations NOMA Reinforcement learning Spiking neural networks Autonomous aerial vehicles Computational efficiency

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Multi-Head Self-Attention Enhanced Convolutional neural network for Driver Fatigue Detection using EEG Signals

Multi-Head Self-Attention Enhanced Convolutional Neural Netw...

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

作者： Kai Gao Wenhao Jia Yuxiang Zhou Ronghua Du College of Automotive and Mechanical Engineering Changsha University of Science &Technology Changsha China

Extended periods or intensive sessions of driving can lead to driver fatigue, affecting not just their reaction time and decision-making capacity, but also resulting in diminished concentration and potential episodes of drowsiness or even falling asleep. In response to this challenge, we have developed a convolutional neural network model incorporating a multi-head self-attention mechanism. To quantify the relationship between electroencephalogram (EEG) signals and driver fatigue, we commence by collecting raw EEG data from 40 channels in the brain using an EEG device. Subsequently, we apply filtering to the raw signals to eliminate physiological disturbances like electrocardiogram signals and non-physiological interferences such as power frequency noise. Thereafter, we employ continuous wavelet transform to extract $\alpha$ and $\beta$ signals from the EEG data, along with features like spectral entropy, sample entropy, and fuzzy entropy from distinct subbands. Finally, we employ a convolutional neural network featuring a multi-head self-attention mechanism for classification. Additionally, we compare the performance of this model against existing classification methods, validating its efficacy. Our results demonstrate that this model can serve as an effective means for detecting driver fatigue, achieving an average accuracy of 98.3% across both testing and validation datasets.

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Anomaly Detection with Graph Attention network for Multimodal IoT Data Monitoring

Anomaly Detection with Graph Attention Network for Multimoda...

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International conference on Parallel and distributed Systems (ICPADS)

作者： Haoyu Li Yi Gao Wei Dong College of Computer Science Zhejiang University China

The popularity of the wireless network and the embedded system has promoted the widespread application of the Internet of Things (IoT) monitoring system which detects anomalies for discovering emergency events to avoid losses by deploying various sensors. In the face of anomaly detection algorithms for multi-dimensional data in the IoT monitoring system, the currently popular method is to implement a model based on the graph neural network (GNN). Graph attention network (GAT), a mutant of GNN, is widely used because of the attention mechanism in capturing multi-dimensional nonlinear features between nodes. However, the GAT’s weakness in processing complex temporal features leads to the fact that anomalies with distinctive temporal features are hard to detect. In this paper, we propose Time-Series Graph Attention network (TSGAT), an innovative GAT-based model for anomaly detection. Given the simplified attention mechanism of the GAT and its weakness in extracting temporal pattern features, we embed the time-series layer to enhance the ability to process time-series data. In addition, we designed the loss function and anomaly scoring mechanism based on data reconstruction features, multidimensional features, and temporal features. TSGAT achieves the improvement of 7%-8% F1 score on two classical public datasets for IoT monitoring: SWaT and WADI, compared with the state-of-the-art GNN-based anomaly detection model.

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MIMO Based Uncertainty-aware Learning-to-Rank Query Optimizer

MIMO Based Uncertainty-aware Learning-to-Rank Query Optimize...

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International conference on Parallel and distributed Systems (ICPADS)

作者： Jincan Xiong Rui Xi Xue Chen Zihan Yan Junxin Zhu Mengshu Hou SCSE UESTC Chengdu China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

The query optimizer is a critical component within the database management system(DBMS), tasked with translating user-input SQL queries into efficient execution plans. Traditional query optimizers rely on statistical data that may become outdated, resulting in inaccuracies leading to suboptimal execution plans and consequent declines in query performance. In response, learning-based optimizers have emerged, utilizing historical data to improve plan selection quality. However, these approaches often struggle to handle dynamic workloads and fail to consistently achieve robust optimization outcomes. This paper introduces MIMO-Lero, an uncertainty-aware learning-based query optimizer designed for seamless integration into existing DBMS platforms without requiring extensive modifications. Inspired by neural network uncertainty principles, we design a Multi-in Multi-out (MIMO) architecture-based plan ranker. This ranker predicts both the relative ranking order of plans and their associated uncertainty levels, signifying the confidence in the order prediction. Furthermore, we propose two novel selection strategies that incorporate uncertainty to enhance the robustness of query optimization. By implementing MIMO-Lero and evaluating its performance using PostgreSQL, experimental results on diverse datasets showcase significant improvements in robust query optimization while maintaining high prediction accuracy.

关键词： Uncertainty Accuracy Databases Query processing neural networks Robustness Optimization

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A Retina-LGN-V1 Structure-like Spiking Neuron network for Image Feature Extraction 5

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

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5th International conference on Video and Image processing (ICVIP)

作者： Ji, Sheng Gu, Qijun Yuan, Ye Zhao, Peng Fang, Tao Huo, Hong Niu, Xin Shanghai Jiao Tong Univ Key Lab Syst Control & Informat Proc Minist Educ Dept AutomatSch Elect Informat & Elect Engn Shanghai Peoples R China Natl Univ Def Technol Sci & Technol Parallel & Distributed Lab Coll Comp Changsha Peoples R 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： Spiking neural network Primary Visual Pathway Image Classification Additive Spiking Timing Dependent Plasticity Learning

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DF-SE-ResNet: Orange Leaf Disease Classification Method Based on Improved Attention Mechanism and FPN Fusion

DF-SE-ResNet: Orange Leaf Disease Classification Method Base...

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Image processing, Computer Vision and Machine Learning (ICICML), International conference on

作者： Zheng Xuan Zhou Jin Xia Shang Kunming University of Science and Technology Yunnan China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

The present study introduces a deep learning-based CNN model, DF-SE-ResNet, designed to enhance the classification precision of orange leaf diseases. This paper employs a feature extraction network that integrates an optimized ResNet50 with FPN (Feature Pyramid networks) and incorporates an improved attention mechanism module, enhancing the network's ability to extract features from small and dense lesion patches while retaining more semantic information from their surroundings, thus laying a solid foundation for accurate subsequent identification. To address the challenge of capturing lesion patches with random distribution, a region feature weighting fusion method based on permutation invariance is introduced after the feature extraction network, which improves the ability to precisely capture lesion patches and significantly enhances lesion features, effectively resolving the issue of randomly distributed lesion patches. Finally, to mitigate the impact of uneven sample distribution across classes in the dataset, an inverse frequency cross-entropy loss function is adopted in place of the standard cross-entropy loss function, alleviating the model's bias towards the majority classes. In the experimental phase, the DF-SE-ResNet achieved an accuracy rate of 95.4%, demonstrating superior performance compared to other deep learning models.

关键词： Location awareness Attention mechanisms Accuracy Semantics Feature extraction Solids Lesions Standards Diseases Residual neural networks

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HeSA: Heterogeneous Systolic Array Architecture for Compact CNNs Hardware Accelerators

HeSA: Heterogeneous Systolic Array Architecture for Compact ...

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Design, Automation and Test in Europe conference and Exhibition (DATE)

作者： Xu, Rui Ma, Sheng Wang, Yaohua Guo, Yang Natl Univ Def Technol Inst Microelect Changsha Peoples R China Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha Peoples R China

ISBN: (纸本)9783981926354

Compact convolutional neural networks have become a hot research topic. However, we find that the hardware accelerator with systolic arrays processing compact models is extremely performance-inefficient, especially when processing depthwise convolutional layers in the networks. To make systolic arrays efficient for compact convolutional neural networks, we propose the heterogeneous systolic array (HeSA) architecture. It introduces heterogeneous processing elements that support multiple modes of dataflow, which can further exploit the reuse data chance of depthwise convolutional layers and without changing the architecture of the naive systolic array. By increasing the utilization rate of processing elements in the array, HeSA improves the performance, throughput, and energy efficiency compared to the standard baseline. Based on our evaluation with typical workloads, HeSA improves the utilization rate of the computing resource in depthwise convolutional layers by 4.5x-5.5x and acquires 1.5-2.2x total performance speedup compared to the standard systolic array architecture. HeSA also improves the on-chip data reuse chance and saves over 20% of energy consumption. Meanwhile, the area of HeSA is basically unchanged compared to the baseline due to its simple design.

关键词： hardware accelerator architecture convolutional neural network depthwise separable convolution systolic array

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