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Hybrid Deep Learning based Attack Detection and Classification Model on IoT Environment

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Hybrid Deep Learning based Attack Detection and Classificati...

International Conference on Electronics, communication and Computing Technologies (ICECCT)

作者： Jaya Dipti Lal Shahnawaz Ayoub Prashant D Hakim S. Prabagar Vijay Kumar Dwivedi Mohit Tiwari Department of Electronics and Communication Engineering Shri G S Institute of Technology and Science Indore Madhya Pradesh India Department of Computer Science Shri Venkateshwara University Gujrala Amroha Uttar Pradesh India Department of Information Technology Thakur College of Science and Commerce Mumbai Maharashtra India Department of Information Science Engineering Networks and IoT Lab School of Computer Science and Engineering & Information Science Presidency University Bengaluru Karnataka India Department of Mathematics Vishwavidyalaya Engineering College Ambikapur Chhattisgarh India Department of Computer Science and Engineering Bharati Vidyapeeth's College of Engineering Delhi India

The Internet of Things (IoT) is becoming an active research area because of its largescale challenges and implementation. But security is the major concern while seeing the dramatic expansion in its applications and size. It is challenging to independently put security mechanism in all the IoT devices and upgrade it according to newer threats. Furthermore, machine learning (ML) techniques could better apply the massive quantity of data produced by IoT devices. Thus, several Deep Learning (DL) based algorithms were introduced for detecting attacks in IoT. Therefore, this study develops a galactic swarm optimization with Deep Learning based Attack Detection and Classification (GSODL-ADC) Model in IoT Environment. The presented GSODL-ADC technique concentrates on the identification of attacks in the IoT environment. The presented GSODL-ADC technique utilizes deep autoencoder (DAE) as a classifier model which properly recognizes the attacks in the IoT environment. Followed by this, the GSO approach is utilized for the optimum hyperparameter adjustments of the DAE model, which leads to enhanced attack detection efficacy. The experimental evaluation of the GSODL-ADC algorithm is tested against benchmark dataset. The obtained experimental values signify the betterment of the GSODL-ADC technique for attack recognition purposes.

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The Effect of Device Redundancy in Timeliness of Information

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The Effect of Device Redundancy in Timeliness of Information

IEEE International Conference on communications (ICC)

作者： Kang Lang Zhengchuan Chen Nikolaos Pappas Howard H. Yang Yunjian Jia Tony Q. S. Quek School of Microelectronics and Communication Engineering Chongqing University Chongqing China State Key Laboratory of Integrated Services Networks Xidian University Xi'an China Department of Computer and Information Science Linköping University Linköping Sweden Zhejiang University/University of Illinois at Urbana-Champaign Institute Zhejiang University Haining China Information System Technology and Design Pillar Singapore University of Technology and Design Singapore

Emerging interaction-based Internet of Things (IoT) applications have stringent demand for timeliness, imposing critical challenges to the design of status update system. Using redundant devices to update the status of the same process is a promising way to improve timeliness, but this approach can result in out of order update arrivals, making it difficult to analyze timeliness. To that end, the present paper conducts a theoretical study toward the Age of Information (AoI) of a multi-queue status update system where multiple sensors observe one physical process and update a common monitor. Based on the stochastic hybrid systems method, the average AoI of the considered system is derived in closed form. The theoretical results are consistent with the simulation results, verifying the correctness of the theoretical analysis. It is shown that the logarithm of the average AoI is linearly decreasing with the logarithm of the number of sensors.

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Smith-Purcell radiation from time grating

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arXiv 2023年

作者： Zhu, Juan-Feng Nussupbekov, Ayan Zhou, Wenjie Song, Zicheng Wang, Xuchen Zhang, Zi-Wen Du, Chao-Hai Bai, Ping Png, Ching Eng Qiu, Cheng-Wei Wu, Lin 8 Somapah Road 487372 Singapore 1 Fusionopolis Way No. 16-16 Connexis 138632 Singapore Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 117583 Singapore Center for Composite Materials and Structures School of Astronautics Harbin Institute of Technology Harbin150080 China Institute of Nanotechnology Karlsruhe Institute of Technology Karlsruhe Germany Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China

Smith-Purcell radiation (SPR) occurs when an electron skims above a spatial grating, but the fixed momentum compensation from the static grating imposes limitations on the emission wavelength. It has been discovered that a temporally periodic system can provide energy compensation to generate light emissions in free space. Here, we introduce temporal SPR (t-SPR) emerging from a time grating and propose a generalized t-SPR dispersion equation to predict the relationship between radiation frequency, direction, electron velocity, modulation period, and harmonic orders. Compared to conventional SPR, t-SPR can: 1) Provide a versatile platform for manipulating SPR emission through temporal modulation (e.g., period, amplitude, wave shape). 2) Exhibit strong robustness to the electron-grating separation, alleviating the constraints associated with extreme electron near-field excitation. 3) Introduce additional energy channels through temporal modulation, enhancing and amplifying emission. Copyright © 2023, The Authors. All rights reserved.

关键词： Electrons

Adjacent Context Coordination Network for Salient Object Detection in Optical Remote Sensing Images

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arXiv 2022年

作者： Li, Gongyang Liu, Zhi Zeng, Dan Lin, Weisi Ling, Haibin Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China School of Communication and Information Engineering Shanghai University Shanghai200444 China Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai University Shanghai200444 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The Department of Computer Science Stony Brook University Stony BrookNY11794 United States

Salient object detection (SOD) in optical remote sensing images (RSIs), or RSI-SOD, is an emerging topic in understanding optical RSIs. However, due to the difference between optical RSIs and natural scene images (NSIs), directly applying NSI-SOD methods to optical RSIs fails to achieve satisfactory results. In this paper, we propose a novel Adjacent Context Coordination Network (ACCoNet) to explore the coordination of adjacent features in an encoder-decoder architecture for RSI-SOD. Specifically, ACCoNet consists of three parts: an encoder, Adjacent Context Coordination Modules (ACCoMs), and a decoder. As the key component of ACCoNet, ACCoM activates the salient regions of output features of the encoder and transmits them to the decoder. ACCoM contains a local branch and two adjacent branches to coordinate the multilevel features simultaneously. The local branch highlights the salient regions in an adaptive way, while the adjacent branches introduce global information of adjacent levels to enhance salient regions. Additionally, to extend the capabilities of the classic decoder block (i.e., several cascaded convolutional layers), we extend it with two bifurcations and propose a Bifurcation-Aggregation Block to capture the contextual information in the decoder. Extensive experiments on two benchmark datasets demonstrate that the proposed ACCoNet outperforms 22 state-of-the-art methods under nine evaluation metrics, and runs up to 81 fps on a single NVIDIA Titan X GPU. The code and results of our method are available at https://***/MathLee/ACCoNet. © 2022, CC BY-NC-ND.

关键词： Signal encoding

DeepMA: End-to-end Deep Multiple Access for Wireless Image Transmission in Semantic communication

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arXiv 2023年

作者： Zhang, Wenyu Bai, Kaiyuan Zeadally, Sherali Zhang, Haijun Shao, Hua Ma, Hui Leung, Victor C.M. The School of Intelligence Science and Technology The Institute of Artificial Intelligence University of Science and Technology Beijing Beijing100083 China The China Telecom Research Institute Beijing100096 China The College of Communication and Information University of Kentucky LexingtonKY40506 United States Institute of Artificial Intelligence Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology Beijing Beijing100083 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

Semantic communication is a new paradigm that exploits deep learning models to enable end-to-end communications processes, and recent studies have shown that it can achieve better noise resiliency compared with traditional communication schemes in a low signal-to-noise (SNR) regime. To achieve multiple access in semantic communication, we propose a deep learning-based multiple access (DeepMA) method by training semantic communication models with the abilities of joint source-channel coding (JSCC) and orthogonal signal modulation. DeepMA is achieved by a DeepMA network (DMANet), which is comprised of several independent encoder-decoder pairs (EDPs), and the DeepMA encoders can encode the input data as mutually orthogonal semantic symbol vectors (SSVs) such that the DeepMA decoders can recover their own target data from a received mixed SSV (MSSV) superposed by multiple SSV components transmitted from different encoders. We describe frameworks of DeepMA in wireless device-to-device (D2D), downlink, and uplink channel multiplexing scenarios, along with the training algorithm. We evaluate the performance of the proposed DeepMA in wireless image transmission tasks and compare its performance with the attention module-based deep JSCC (ADJSCC) method and conventional communication schemes using better portable graphics (BPG) and Low-density parity-check code (LDPC). The results obtained show that the proposed DeepMA can achieve effective, flexible, and privacy-preserving channel multiplexing process, and demonstrate that our proposed DeepMA approach can yield comparable bandwidth efficiency compared with conventional multiple access schemes. © 2023, CC BY.

关键词： Signal to noise ratio

Photonic-based fully integrated microwave instantaneous frequency measurement

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Photonic-based fully integrated microwave instantaneous freq...

2021 Asia communications and Photonics Conference, ACP 2021

作者： Tao, Yuansheng Tao, Zihan Shu, Haowen Jin, Ming Wang, Xingjun State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronics School of Electronics Engineering and Computer Science Peking University Beijing100871 China Frontier Science Center for Nano-optoelectronics Peking University Beijing100871 China Peking University Yangtze Delta Institute of Optoelectronics Nantong226010 China Peng Cheng Laboratory Shenzhen518055 China

We demonstrate the first photonic-based fully integrated microwave instantaneous frequency measurement system, showing a wideband measurement range of 2-34 GHz and an ultralow estimation error of 10.85 MHz. © Opt... 详细信息

ISBN: (纸本)9781957171005

关键词： Frequency estimation

Performance Analysis of LEO Satellite-Based IoT networks in the Presence of Interference

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arXiv 2022年

作者： Dwivedi, Ayush Kumar Chaudhari, Sachin Varshney, Neeraj Varshney, Pramod K. The Signal Processing and Communication Research Center International Institute of Information Technology Hyderabad500032 India The Wireless Networks Division National Institute of Standards and Technology GaithersburgMD20899 United States The Department of Electrical Engineering and Computer Science Syracuse University SyracuseNY13244 United States

This paper presents a star-of-star topology for internet-of-things (IoT) networks using mega low-Earth-orbit constellations. The proposed topology enables IoT users to broadcast their sensed data to multiple satellites simultaneously over a shared channel, which is then relayed to the ground station (GS) using amplify-and-forward relaying. The GS coherently combines the signals from multiple satellites using maximal ratio combining. To analyze the performance of the proposed topology in the presence of interference, a comprehensive outage probability (OP) analysis is performed, assuming imperfect channel state information at the GS. The paper employs stochastic geometry to model the random locations of satellites, making the analysis general and independent of any specific constellation. Furthermore, the paper examines successive interference cancellation (SIC) and capture model (CM)-based decoding schemes at the GS to mitigate interference. The average OP for the CM-based scheme and the OP of the best user for the SIC scheme are derived analytically. The paper also presents simplified expressions for the OP under a high signal-to-noise ratio (SNR) assumption, which are utilized to optimize the system parameters for achieving a target OP. The simulation results are consistent with the analytical expressions and provide insights into the impact of various system parameters, such as mask angle, altitude, number of satellites, and decoding order. The findings of this study demonstrate that the proposed topology can effectively leverage the benefits of multiple satellites to achieve the desired OP and enable burst transmissions without coordination among IoT users, making it an attractive choice for satellite-based IoT networks. Copyright © 2022, The Authors. All rights reserved.

关键词： Topology

PAM-4 Optical Transmission Beyond 224 Gbps Based on an Ultrahigh-Bandwidth Slow-light Silicon Modulator

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PAM-4 Optical Transmission Beyond 224 Gbps Based on an Ultra...

Conference on Lasers and Electro-Optics (CLEO)

作者： Changhao Han Jun Qin Qipeng Yang Zhao Zheng Haowen Shu Yunhao Zhang Yichen Wu Yu Sun Junde Lu Yan Zhou Zhangfeng Ge Lei Wang Zhixue He Shaohua Yu Weiwei Hu Chao Peng John E. Bowers Xingjun Wang State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing China Department of Electrical and Computer Engineering University of California Santa Barbara CA US Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing China Frontiers Science Center for Nano-optoelectronics Peking University Beijing China Peng Cheng Laboratory Shenzhen China Peking University Yangtze Delta Institute of Optoelectronics Nantong China

We experimentally demonstrate PAM-4 optical transmission beyond 224 Gbps based on an ultrahigh-bandwidth slow-light silicon modulator in C-band with the combination of the artificial neural network equalizers.

ISBN: (纸本)9798350369311

关键词： Equalizers C-band Optical computing Artificial neural networks Electro-optic modulators Optical fiber networks Silicon photonics Ultrafast optics Optical modulation Passband

100 Gb/s NRZ OOK signal regeneration on a silicon chip

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100 Gb/s NRZ OOK signal regeneration on a silicon chip

Conference on Lasers and Electro-Optics (CLEO)

作者： Hua Shun Wen Jia Bin Cui Heng Zhou Yin Fang Chen Ya Jin Bo Rui Xu Kun Peng Zhai Jia Zheng Sun Yuan Yuan Guo Yan Ran Wu Wen Chen Wei Chen Xin Wang Ning Hua Zhu Guo-Wei Lu Gui Jun Ji Dennis Chi Zhou Yiu-Kwok Cheng Da-Quan Yang Ming Li State Key Laboratory on Integrated Optoelectronics Institute of Semiconductors Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China State Key Laboratory of Information Photonics and Optical Communications School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Optical Fiber Sensing and Communication Networks School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Division of Computer Engineering The University of Aizu Fukushima Japan Advanced Fiber Resources (Zhuhai) Ltd. Zhuhai China

All-optical regeneration of 100 Gb/s NRZ OOK signal with 3.6 dB extinction ratio and 0.5 dB quality factor improvements is experimentally demonstrated using a silicon waveguide with reverse-biased p-i-n junction for t... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Q-factor Knowledge engineering PIN photodiodes Waveguide junctions Extinction ratio Silicon Optical signal processing