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A Novel Semi-supervised Anomaly Detection Method for Network Intrusion Detection 22

A Novel Semi-supervised Anomaly Detection Method for Network...

引用

22nd IEEE international conference on Communication Technology, ICCT 2022

作者： Wang, Xinran Qiu, Xiaofeng School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665470674

Deep learning techniques have become an increasingly popular solution for network intrusion detection system (NIDS). Semi-supervised deep anomaly detection methods used in network intrusion detection always need a high proportion of labeled samples and do not have good robustness. To solve these problems, we propose ATSAD: a novel semi-supervised anomaly detection method which is an improvement of the state-of-the-art method Deep SAD (Deep Semi-Supervised Anomaly Detection). Our main idea is to make the model pay more attention to important features of input data. We use an attention network to make the idea come true. We define a new objective function so that the attention network and the mapping network of Deep SAD can be trained together. Our evaluations show ATSAD has the best performance and good robustness compared with other methods on the dataset CSE-CIC-IDS2018. © 2022 IEEE.

关键词： Anomaly detection

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When Adversarial Example Attacks Meet Vertical Federated learning 19

When Adversarial Example Attacks Meet Vertical Federated Lea...

引用

2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous Intelligence and Computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable Computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy Computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Meng, Dan Fu, Zhihui Kong, Chao Qi, Yue Cao, Guitao Oppo Research Institute Shenzhen China Anhui Polytechnic University School of Computer and Information Wuhu China East China Normal University Shanghai Key Laboratory of Trustworthy Computing Shanghai China MoE Engineering Research Center of SH/HW Co-design Technology and Application Ecnu Shanghai China

ISBN: (纸本)9798350346558

Federated learning (FL) is proposed to enable efficient machine learning while protecting the privacy of user data. In non-federated scenarios, machine learning models are vulnerable to adversarial attacks. The attacker can construct elaborate adversarial examples, which can greatly reduce the performance of the victim model. It is worth exploring how FL models differ from traditional models in face of complex adversarial attacks. We are also curious about the robustness of different FL modeling methods against adversarial attacks, providing suggestions for selecting suitable FL models in practical applications. Consequently, we perform adversarial example attacks on vertical federated learning and design experiments to study how vertical federated models perform under adversarial example attacks. The simulation results show that federated models are more robust than non-federated models. Besides, in vertical federation learning, decision trees and XGBoost are much more robust than logistic regression and neural networks. © 2022 IEEE.

关键词： Decision trees

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An Effective Preprocess for Deep learning Based Intrusion Detection 22

An Effective Preprocess for Deep Learning Based Intrusion De...

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22nd IEEE/ACIS international conference on Software engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Fall)

作者： Lin, Chia-Ju Chen, Ruey-Maw Natl Chin Yi Univ Technol Dept Comp Sci & Informat Engn Taichung Taiwan

ISBN: (纸本)9781665404037

The data preprocess directly affects the classification results in various applications. In the field of intrusion detection, less research raised the problems or solutions of unequal metrics in data attributes. This study proposes an effective data preprocessing method for network packets with unequal metrics in packet attributes. A standard deviation standardization was first applied to standardize each attribute of KDDCUP'99 dataset, followed by quantizing it to the range of 0 to 255 interval for afterward use of the image. Meanwhile, the Zigzag arrangement coding and IDCT (Inverse Discrete Cosine Transform) were then used to convert the quantized data into images. Experimental results demonstrate that a more than 94% recall rate of the overall intrusion detection classifier can be yielded by the proposed preprocess method even without a complicated network model. Meanwhile, intrusion detection performance can be guaranteed by using small-size images of packet attributes.

关键词： Intrusion Detection KDDCUP'99 data Preprocess Feature Extraction Standard Deviation Standardization

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VOCANO: A NOTE TRANSCRIPTION FRAMEWORK FOR SINGING VOICE IN POLYPHONIC MUSIC 22

VOCANO: A NOTE TRANSCRIPTION FRAMEWORK FOR SINGING VOICE IN ...

引用

22nd international conference on Music Information Retrieval, ISMIR 2021

作者： Hsu, Jui-Yang Su, Li Department of Electrical Engineering National Taiwan University Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan

ISBN: (纸本)9781732729902

High variability of singing voice and insufficiency of note event annotation present a huge bottleneck in singing voice transcription (SVT). In this paper, we present VOCANO, an open-source VOCAl NOte transcription framework built upon robust neural networks with multi-task and semi-supervised learning. Based on a state-of-the-art SVT method, we further consider virtual adversarial training (VAT), a semi-supervised learning (SSL) method for SVT on both clean and accompanied singing voice data, the latter being pre-processed using the singing voice separation (SVS) technique. The proposed framework outperforms the state of the arts on public benchmarks over a wide variety of evaluation metrics. The effects of the types of training models and the sizes of the unlabeled datasets on the performance of SVT are also discussed. © 2021 Proceedings of the 22nd international conference on Music Information Retrieval, ISMIR 2021. All Rights Reserved.

关键词： Music

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Enhancing Training Efficiency for Cloud-Edge Collaboration in the Industrial Internet of Things: A Transmission-Centric Approach

Enhancing Training Efficiency for Cloud-Edge Collaboration i...

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IEEE international conference on Industrial Informatics (IndIN)

作者： Jia Zhang Tao Zheng Binjie Lu Huan Yin Kyi Thar Mikael Gidlund Mohsen Guizani School of Electronic and Information Engineering Beijng Jiaotong University China Department of Computer and Electrical Engineering Mid Sweden University Sweden Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence(MBZUAI) Abu Dhabi UAE

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

With the development of intelligent edge computing (IEC) in industrial IoT (IIoT), there is a growing number of service providers trying to leverage computing resources in the cloud and at the edge to meet the users‘ demand for low latency and high reliability in diversified applications. This evolving landscape necessitates innovative approaches to manage and process the vast amounts of data generated by IIoT devices. Among these approaches, distributed learning frameworks, such as federated learning (FL), have emerged as popular solutions. However, compared to computing, communication remains the primary bottleneck that constrains the speed of federated model training. Most of the previous solutions have focused on reducing communication overhead. Differently, we propose a transmission-centric approach by designing an efficient communication archi-tecture for FL with cloud-edge collaboration, specifically aimed at enhancing communication capabilities through multi-path transmission. We deploy this FL system in a real environment and conduct extensive testing. The results demonstrate that the new approach can significantly reduce communication time in FL setting, thereby enhancing model aggregation efficiency and shortening the overall training duration. Compared to conventional single-path transmission, the proposed solution improves training efficiency by up to 26.4%.

关键词： Training Cloud computing Scheduling algorithms Collaboration Computer architecture Reliability Low latency communication Informatics Industrial Internet of Things Testing

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learning Nonlinear Models of Dynamic Systems

Learning Nonlinear Models of Dynamic Systems

引用

international Symposium on intelligent Systems and Informatics (SISY)

作者： József Bokor Zoltán Szabó HUN-REN Institute for Computer Science and Control SZTAKI Budapest Hungary Research Center of Vehicle Industry (SZE-JKK) Széchenyi István University Gyor Hungary

ISBN: (数字)9798350385601

ISBN: (纸本)9798350385618

Modeling dynamic systems is an important part in analysing and control of various systems arising either in health sciences or in the engineering word. Recent approaches to learn models from data are the so-called kernel-based methods and SVMs. There are strong relations to the theory of reproducing kernel Hilbert space (RKHS), to principal component analysis and canonic correlation analysis known previously from statistics. In recent form their use was extended from statistics to obtain models for dynamic systems. First we summarise the basics for the reproducing kernel based Hilbert space (RKHS) and the support vector machine (SVM) approaches. Following this it will be shown how some frequently used nonlinear models can be obtained by using these concepts. In the last part we discuss the structure estimation problem, i.e. how to determine the (least) number of features (observables) to describe the nonlinear system with a sparse representation.

关键词： Support vector machines Analytical models Sparse approximation Statistical learning data models Hilbert space Eigenvalues and eigenfunctions Kernel Dynamical systems Principal component analysis

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Convolutional Neural Network for Performance Monitoring and Modulation Formation Recognition in Communication System

Convolutional Neural Network for Performance Monitoring and ...

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international conference on Communication Technology (ICCT)

作者： Lifang Zhang Ting Zhang Danshi Wang Wenbin Chen The Intelligent Network Innovation Center of Chinaunicom China Unicom Beijing China State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing China

In this paper, a deep learning-based method is proposed for system damage identification and modulation format recognition in satellite communication system. Four typical damages are studied, including atmospheric channel impairments and hardware imperfections. In addition, total of ten modulation formats are generated, from low-order phase modulation to complex quadrature amplitude modulation. All the data are converted into image data and processed from the perspective of image recognition using convolutional neural network without feature engineering.

关键词： learning systems Image recognition Quadrature amplitude modulation Phase modulation Hardware Convolutional neural networks Monitoring

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Comparing Ranking learning Algorithms for Information Retrieval Systems 24th

Comparing Ranking Learning Algorithms for Information Retri...

引用

24th international conference on intelligent data engineering and automated learning, IDEAL 2023

作者： Zilles, J. Borges, E.N. Lucca, G. Marco-Detchart, C. Berri, Rafael A. Dimuro, G.P. Centro de Ciências Computacionais Universidade Federal do Rio Grande Av. Itália km 08 Campus Carreiros Rio Grande96201-900 Brazil Programa de Pós-Graduação em Engenharia Eletrônica e Computação Universidade Católica de Pelotas Gonçalves Chaves Pelotas96015-560 Brazil Camino de Vera s/n Valencia46022 Spain

ISBN: (纸本)9783031482311

The growth of machine learning applications in various fields has enabled the advancement of Information Retrieval systems. As a result of this evolution, it has become possible to solve the well-known document classification problem. In the beginning, document positions within a result were given from a score, where each document receives an assigned value based on the terms used in the input query. The use of machine learning in this field is known as learning to Rank, which allows the classification of documents to better meet user search requirements, taking into account aspects such as document preference, importance, and relevance. This paper presents a comparison of different algorithms for ranking documents using machine learning. It is observed that RankSVM presents relatively satisfactory results in smaller datasets, while algorithms that use Gradient Boosting obtain better results for larger datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Machine learning

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intelligent Resource Allocation for RIS-Assisted Cell-Free Massive MIMO Systems

Intelligent Resource Allocation for RIS-Assisted Cell-Free M...

引用

Consumer Communications and Networking conference, CCNC IEEE

作者： Gloria Mollah Majumder Haider Danda B. Rawat Imtiaz Ahmed Dept. of Electrical Engineering & Computer Science Howard University Washington DC USA

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Cell-free massive multiple-input multiple-output (mMIMO) systems distribute a large number of access points (APs) over a wide geographic region, improving coverage, capacity, and user experiences notably. Similarly, reconfigurable intelligent surface (RIS) allows a significant leap forward in signal quality and energy efficiency by adding additional flexibility and intelligent control over the propagation environment of wireless systems. However, optimal resource allocation in RIS-integrated cell-free mMIMO systems faces substantial challenges due to the presence of a large number of APs and passive reflecting elements (PREs) at RIS while simultaneously providing network coverage to aerial and ground users. To efficiently navigate the high-dimensional optimization space, this paper proposes a novel framework for optimal resource allocation in RIS-assisted cell-free mMIMO systems employing deep learning (DL) techniques. In particular, we train a deep neural network offline to optimize the transmit powers at APs and the phase shift of PREs at RIS. The trained model is tested online to perform real-time optimization with low computational complexity. Simulation results demonstrate the effectiveness of the proposed data-driven resource allocation scheme while comparing it with the state-of-the-art baseline scheme.

关键词： Navigation Simulation Wireless networks Artificial neural networks Massive MIMO Reconfigurable intelligent surfaces Real-time systems Resource management Optimization intelligent control

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Information Geometry of the Locally Most Powerful Test 22

Information Geometry of the Locally Most Powerful Test

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22nd IEEE international conference on Communication Technology, ICCT 2022

作者： Cheng, Yongqiang Hua, Xiaoqiang Wu, Hao Wang, Hongqiang College of Electronic Science and Engineering National University of Defense Technology Changsha China College of Meteorology and Oceanography National University of Defense Technology Changsha China

ISBN: (纸本)9781665470674

The locally most powerful (LMP) test for weak signal detection is studied from a information-geometric perspective. In such a framework, the LMP test is identified as the norm of natural whitened gradient on the statistical manifold consisting of a family of parametric probability distributions, which indicates that the LMP test pursues the steepest learning directions from the null hypothesis to the empirical distribution of the observed data on the manifold. A concrete geometrical interpretation of the LMP test in the theory of information geometry is presented, which leads to an immediate extension of the LMP test to a vector valued parameter case. Example of multi-component sinusoidal signal detection under low SNR conditions confirms a practical importance of the extended test. © 2022 IEEE.

关键词： Signal detection

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