Spectrum Usage Anomaly Detection from Sub-Sampled Data Stream via Deep Neural Network

被引:3
|
作者
Zhang H. [1 ]
Yang J. [2 ]
Chen J.T. [3 ]
Gao Y. [1 ,4 ]
机构
[1] Institute for Communication Systems, The University of Surrey, Guildford
[2] Mobile Communications Innovation Center, China Academy of Information and Communications Technology, Beijing
[3] Future Network Intelligence Institute (FNii) and School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen
[4] School of Computer Science, Fudan University, Shanghai
基金
英国工程与自然科学研究理事会; 中国国家自然科学基金;
关键词
anomaly detection; compressive sensing (CS); machine learning (ML); multicoset sampling;
D O I
10.23919/JCIN.2023.10087244
中图分类号
学科分类号
摘要
Anomaly detection is an essential part of any practical system in order to remedy any malfunction and accident early to create a secure and robust system. Malicious users and malfunctioning cognitive radio (CR) devices may cause severe interference to legitimate users. However, there are no effective methods to detect sponta-neous and irregular anomaly behaviors in sub-sampling data stream from wideband compressive spectrum sensing as an important function of a CR device. In this article, to detect anomaly utilization of spectrum from sub-sampled data stream, a multiple layer perceptron/feed-forward neural network (FFNN) based solution is proposed. The proposed solution would learn the pattern of legitimate and anomalous usages autonomously without expert’s knowledge. The proposed neural network (NN) framework has also shown benefits such as more than 80% faster detection speed and lower detection error rate. © 2023, Posts and Telecom Press Co Ltd. All rights reserved.
引用
收藏
页码:13 / 23
页数:10
相关论文
共 50 条
  • [31] Deep Neural Network Architecture for Anomaly Based Intrusion Detection System
    Behera, Sidharth
    Pradhan, Ayush
    Dash, Ratnakar
    2018 5TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING AND INTEGRATED NETWORKS (SPIN), 2018, : 270 - 274
  • [32] Signal reconstruction from sampled data using neural network
    Sudou, A
    Hartono, P
    Saegusa, R
    Hashimoto, S
    NEURAL NETWORKS FOR SIGNAL PROCESSING XII, PROCEEDINGS, 2002, : 707 - 715
  • [33] Graph neural network approach with spatial structure to anomaly detection of network data
    Hao Zhang
    Yun Zhou
    Huahu Xu
    Jiangang Shi
    Xinhua Lin
    Yiqin Gao
    Journal of Big Data, 12 (1)
  • [34] Unsupervised Anomaly Detection in Spatio-Temporal Stream Network Sensor Data
    Santos-Fernandez, Edgar
    Ver Hoef, Jay M.
    Peterson, Erin E.
    Mcgree, James
    Villa, Cesar A.
    Leigh, Catherine
    Turner, Ryan
    Roberts, Cameron
    Mengersen, Kerrie
    WATER RESOURCES RESEARCH, 2024, 60 (11)
  • [35] Marrying Graph Kernel with Deep Neural Network: A Case Study for Network Anomaly Detection
    Yao, Yepeng
    Su, Liya
    Zhang, Chen
    Lu, Zhigang
    Liu, Baoxu
    COMPUTATIONAL SCIENCE - ICCS 2019, PT II, 2019, 11537 : 102 - 115
  • [36] Deep Learning for Compressed Sensing-Based Blade Vibration Reconstruction From Sub-Sampled Tip-Timing Signals
    Chen, Zhongsheng
    Sheng, Hao
    Liao, Lianying
    Liu, Chengwu
    Xiong, Yeping
    IEEE ACCESS, 2023, 11 : 38251 - 38262
  • [37] A Stacking-Based Deep Neural Network Approach for Effective Network Anomaly Detection
    Nkenyereye, Lewis
    Tama, Bayu Adhi
    Lim, Sunghoon
    CMC-COMPUTERS MATERIALS & CONTINUA, 2021, 66 (02): : 2217 - 2227
  • [38] Accelerating deep neural network learning using data stream methodology
    Duda, Piotr
    Wojtulewicz, Mateusz
    Rutkowski, Leszek
    INFORMATION SCIENCES, 2024, 669
  • [39] Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes
    Sabokrou, Mohammad
    Fayyaz, Mohsen
    Fathy, Mahmood
    Moayed, Zahra
    Klette, Reinhard
    COMPUTER VISION AND IMAGE UNDERSTANDING, 2018, 172 : 88 - 97
  • [40] A novel anomaly detection method for multimodal WSN data flow via a dynamic graph neural network
    Zhang, Qinghao
    Ye, Miao
    Deng, Xiaofang
    CONNECTION SCIENCE, 2022, 34 (01) : 1609 - 1637