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International Conference on Military communications and Information Systems (ICMCIS)

作者： Johannes F. Loevenich Erik Adler Rémi Mercier Alexander Velazquez Roberto Rigolin F. Lopes Secure Communications & Information (SIX) Thales Deutschland Ditzingen Germany Department of Mathematics/Computer Science University of Osnabrück Osnabrück Germany Medical Informatics Heilbronn University of Applied Sciences Heilbronn Germany Computer Sciences and Networks (INFRES) Télécom Paris Paris France Information Technology Division U.S. Naval Research Laboratory Washington DC USA

ISBN: (数字)9798350373196

ISBN: (纸本)9798350373202

This paper extends the design of an autonomous cyber defence (ACD) agent to monitor and actuate within a protected core network segment. The goal is to take advantage of recent developments in AI models to define a hybrid architecture that combines deep reinforcement learning (DRL), large language models (LLMs), and rule-based models. The motivation comes from the fact that modern network segments within colored clouds are using software-defined controllers with the means to host ACD agents and other cybersecurity tools implementing hybrid AI models. For example, our ACD agent uses a DRL model and the chatbot uses an LLM to create an interface with human cybersecurity experts. The ACD agent was evaluated against two red agent strategies in a gym environment using a set of actions to defend services in the network (monitor, analyse, decoy, remove, and restore). Our chatbot was developed using retrieval augmented generation and a prompting agent to augment a pre-trained LLM with data from cybersecurity knowledge graphs. We performed a comparative analysis between a baseline implementation and our chatbot using generation/retrieval metrics. The results suggest that both ACD agent and chatbot can potentially enhance the defence of critical networks connected to untrusted infrastructure.

关键词： Military communication Measurement Prototypes Knowledge graphs Chatbots Reliability engineering Deep reinforcement learning

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Smart Resource Allocation Model via Artificial Intelligence in Software Defined 6G networks

Smart Resource Allocation Model via Artificial Intelligence ...

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IEEE International Conference on communications (ICC)

作者： Ali Nouruzi Atefeh Rezaei Ata Khalili Nader Mokari Mohammad Reza Javan Eduard A. Jorswieck Halim Yanikomeroglu Department of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran Technical University of Berlin Germany Friedrich-Alexander-University Erlangen-Nurnberg Germany Department of Electrical Engineering Shahrood University of Technology Shahrood Iran Institute of Communications Technology Technische Universität Braunschweig Braunschweig Germany Department of Systems and Computer Engineering Non-Terrestrial Networks (NTN) Lab Carleton University

In this paper, we design a new flexible smart software-defined radio access network (Soft-RAN) architecture with traffic awareness for sixth generation (6G) wireless networks. In particular, we consider a hierarchical resource allocation model for the proposed smart soft-RAN model where the software-defined network (SDN) controller is the first and foremost layer of the framework. This unit dynamically monitors the network to select a network operation type on the basis of distributed or centralized resource allocation procedures to intelligently perform decision-making. In this paper, our aim is to make the network more scalable and more flexible in terms of conflicting performance indicators such as achievable data rate, overhead, and complexity indicators. To this end, we introduce a new metric, i.e, throughput-overhead-complexity (TOC), for the proposed machine learning-based algorithm, which supports a trade-off between these performance indicators. In particular, the decision making based on TOC is solved via deep reinforcement learning (DRL) which determines an appropriate resource allocation policy. Furthermore, for the selected algorithm, we employ the soft actor-critic (SAC) method which is more accurate, scalable, and robust than other learning methods. Simulation results demonstrate that the proposed smart network achieves better performance in terms of TOC compared to fixed centralized or distributed resource management schemes that lack dynamism. Moreover, our proposed algorithm outperforms conventional learning methods employed in recent state-of-the-art network designs.

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An On-Board Hardware Implementation of AODV Routing Protocol in VANET: Design and Experimental Evaluation

An On-Board Hardware Implementation of AODV Routing Protocol...

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International Conference on Internet of Things: Systems, Management and Security

作者： Hassan Mistareehi Haythem Bany Salameh D. Manivannan Department of Computer Science University of Kentucky Lexington KY USA Department of Networks and Communications Engineering Al Ain University Al Ain UAE Telecommunication Engineering Department Yarmouk University Irbid Jordan

Vehicular Ad hoc networks (VANET) will likely play an essential role in the large-scale deployment of Intelligent Transportation Systems (ITS). On-Board Units (OBUs) in each vehicle collect information that can help drivers, such as alerts about accidents, traffic updates, etc. However, most proposed VANET schemes were based on simulation models, with only a few built hardware implementations. To our knowledge, there is no hardware implementation for the routing protocol in VANET. In this paper, we design and build a hardware implementation for OBU that uses the ad hoc On-Demand Distance Vector (AODV) routing protocol. Our implementation allows messages to disseminate to and from vehicles and roadside units (RSUs). The implemented OBU is efficient, simple, and at a low cost. Experimentation results show that the implemented method can transmit and receive messages between the source and destination in a timely manner.

关键词： Costs Vehicular ad hoc networks Hardware Routing protocols Security Internet of Things Floods

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Analog parallel processor for broadband multifunctional integrated system based on silicon photonic platform

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Light(Science & Applications) 2025年第3期14卷 746-756页

作者： Na Qian Defu Zhou Haowen Shu Ming Zhang Xingjun Wang Daoxin Dai Xiao Deng Weiwen Zou State Key Laboratory of Advanced Optical Communication Systems and Networks Intelligent Microwave Lightwave Integration Innovation Center(imLic)Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghaiChina State Key Laboratory of Advanced Optical Communications System and Networks Department of ElectronicsSchool of Electronics Engineering and Computer SciencePeking UniversityBejingChina State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and EngineeringInternational Research Center for Advanced PhotonicsZhejiang UniversityHangzhouChina

Sharing the hardware platform between diverse information systems to establish full cooperation among different functionalities has attracted substantial ***,broadband multifunctional integrated systems with large operating frequency ranges are challenging due to the bandwidth and computing speed restrictions of electronic ***,we report an analog parallel processor(APP)based on the silicon photonic platform that directly discretizes and parallelizes the broadband signal in the analog *** APP first discretizes the signal with the optical frequency comb and then adopts optical dynamic phase interference to reassign the analog signal into 2N parallel *** photonic analog parallelism,data rate and data volume in each sequence are simultaneously compressed,which mitigates the requirement on each parallel computing ***,the fusion of the outputs from each computing core is equivalent to directly processing broadband *** the proof-of-concept experiment,two-channel analog parallel processing of broadband radar signals and high-speed communication signals is implemented on the single photonic integrated *** bandwidth of broadband radar signal is 6 GHz and the range resolution of 2.69 cm is *** wireless communication rate of 8 Gbit/s is also *** the bandwidth and speed limitations of the single-computing core along with further exploring the multichannel potential of this architecture,we anticipate that the proposed APP will accelerate the development of powerful optoelectronic processors as critical support for applications such as satellite networks and intelligent driving.

关键词： analog parallel processor sharing hardware platform multifunctional integrated systems establish full cooperation different functionalities analog parallel processor app based silicon photonic platform electronic circuitryherewe broadband signal

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Opinion Mining of Politics and Inflation using Roman Urdu Dataset

Opinion Mining of Politics and Inflation using Roman Urdu Da...

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Human-Centered Cognitive Systems (HCCS)

作者： Zunaira Shafqat Muddesar Iqbal Waqas Haider Bangyal Dhafer Almakhles Department of Computer Science University of Sialkot Sialkot Pakistan Division of Computer Science and Informatics School of Engineering London Southbank University United Kindgdom (UK) Department of Computer Science Kohsar University Murrree Murree Pakistan Communications and Networks Engineering Department Renewable Energy Laboratory College of Engineering Prince Sultan University Riyadh Saudi Arabia

In today's world, data volume is immense and is expanding at an exponential rate. A leading factor of this is the data shared over social media by millions of people daily in the form of text, audio, video, and images. Opinion mining is a branch of text mining that seeks to extract public opinion about an event or particular topic on any online platform like review sites, Twitter, Facebook, etc. Most of the work in opinion mining is done in the English language and very little is done in Roman Urdu, which is a dominant social media language in Pakistan and India. Inflation and politics are amongst the most talked-about and discussed topics on social media in Pakistan. In this study, an Inflation and Politics-based Roman Urdu dataset is prepared that is extracted from the Roman Urdu dataset available at Kaggle. Various operations (with text processing, without text preprocessing, with attribute selection, without attribute selection) were performed on the data over machine learning algorithms of Naive Bayes, Bayes Net, KStar, Decision Tree, and Random Forest and determined which algorithm gave the best accuracy on the training data. WEKA (Version 3.8.4) was used to generate opinion mining regarding politics and inflation in Pakistan using the Roman Urdu dataset.

关键词： Text mining Sentiment analysis Machine learning algorithms Social networking (online) Blogs Training data Decision trees

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IRS-Enhanced Anti-Jamming Precoding Against DISCO Physical Layer Jamming Attacks

IRS-Enhanced Anti-Jamming Precoding Against DISCO Physical L...

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IEEE International Conference on communications (ICC)

作者： Huan Huang Hongliang Zhang Yi Cai Yunjing Zhang A. Lee Swindlehurst Zhu Han Electronic Information School Soochow University Suzhou China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing China Department of Electrical and Computer Engineering University of Houston Houston USA Center for Pervasive Communications and Computing University of California Irvine USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Illegitimate intelligent reflective surfaces (IRSs) can pose significant physical layer security risks on multi-user multiple-input single-output (MU-MISO) systems. Recently, a DISCO approach has been proposed an illegitimate IRS with random and time-varying reflection coefficients, referred to as a “disco” IRS (DIRS). Such DIRS can attack MU-MISO systems without relying on either jamming power or channel state information (CSI), and classical anti-jamming techniques are in-effective for the DIRS-based fully-passive jammers (DIRS-based FPJs). In this paper, we propose an IRS-enhanced anti-jamming precoder against DIRS-based FPJs that requires only statistical rather than instantaneous CSI of the DIRS-jammed channels. Specifically, a legitimate IRS is introduced to reduce the strength of the DIRS-based jamming relative to the transmit signals at a legitimate user (LU). In addition, the active beamforming at the legitimate access point (AP) is designed to maximize the signal-to-jamming-plus-noise ratios (SJNRs). Numerical results are presented to evaluate the effectiveness of the proposed IRS-enhanced anti-jamming precoder against DIRS-based FPJs.

关键词： Array signal processing Precoding Computational modeling Interference MISO communication Physical layer security Reflection coefficient

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Fully integrated hybrid microwave photonic receiver

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Photonics Research 2022年第6期10卷 1472-1483页

作者： JIACHEN LI SIGANG YANG HONGWEI CHEN XINGJUN WANG MINGHUA CHEN WEIWEN ZOU Beijing National Research Center for Information Science and Technology(BNRist) Department of Electronic EngineeringTsinghua UniversityBeijing 100084China State Key Laboratory of Advanced Optical Communications System and Networks Department of ElectronicsSchool of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China State Key Laboratory of Advanced Optical Communication Systems and Networks Intelligent Microwave Lightwave Integration Innovation Center(imLic)Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghai 200240China

Microwave photonic receivers are a promising candidate in breaking the bandwidth limitation of traditional radio-frequency(RF)*** further balance the performance superiority with the requirements regarding size,weight,and power consumption(SWa P),the implementation of integrated microwave photonic microsystems has been considered an upgrade ***,up to now,to the best of our knowledge,chip-scale fully integrated microwave photonic receivers have not been reported due to the limitation of material *** this paper,we report a fully integrated hybrid microwave photonic receiver(FIH-MWPR)obtained by comprising the indium phosphide(In P)laser chip and the monolithic silicon-on-insulator(SOI)photonic circuit into the same substrate based on the low-coupling-loss micro-optics *** from the integration of all optoelectronic components,the packaged FIH-MWPR exhibits a compact volume of 6 cmand low power consumption of 1.2 *** FIH-MWPR supports a wide operation bandwidth from 2 to 18 ***,its RF-link performance to down-convert the RF signals to the intermediate frequency is experimentally characterized by measuring the link gain,the noise figure,and the spurious-free dynamic range metrics across the whole operation frequency ***,we have utilized it as a de-chirp receiver to process the broadband linear frequencymodulated(LFM)radar echo signals at different frequency bands(S-,C-,X-,and Ku-bands)and successfully demonstrated its high-resolution-ranging *** the best of our knowledge,this is the first realization of a chip-scale broadband fully integrated microwave photonic receiver,which is expected to be an important step in demonstrating the feasibility of all-integrated microwave photonic microsystems oriented to miniaturized application scenarios.

关键词： method microwave bands

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Comparison of ITU models performance in predicting Malaysia′s tropical Rainfall Rate and Rain Attenuation at 26GHz mm-wave propagation

TechRxiv

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TechRxiv 2021年

作者： Nissirat, Liyth A. Alsamawi, Aida Shayea, Ibraheem Azmi, Marwan Ergen, Mustafa Rahman, Tharek Abd The Department of Computer Networks and Communications College of Computer Science and Information Technology King Faisal University Al-Hasa31982 Saudi Arabia Turkey Malaysia

This paper investigates the performance of different ITU models in predicting Malaysia′s tropical rainfall rates and rain attenuation. This investigation is considered at 26GHz mm-wave frequency, and three datasets collected at different regions in Malaysia with emphasize on the latest dataset in Skudai in 2012. The data collected has rainfall rate and rain attenuation measurements for a continuous year at 1 spm sampling rate. Nonlinear regression is used to estimate best fit parameters for comparison with ITU-R p.837-1 with rain zone data, ITU-R p.837-6 as method 1 and ITU-R p.837-7 as method 2. Results show that ITU method 2 outperforms all the models with RMSE in the range of 12.72 mm/hr and 3.51 dB/km for rainfall rate and rain attenuation respectively. Conversely, absolute error at 0.01% of time showed superiority of ITU-R p.837-1 tropical recommendation with absolute error in the range of 0.39 mm/hr and 4.16 dB/km for rainfall rate and rain attenuation respectively. All models showed significant deviation from the measurements and the best fit at lower than 0.01% of time, with minimum deviation observed for of ITU method 2 in that range. Worst month parameters estimation using ITU method2 is in good match to the best fit estimation. © 2021, CC BY.

关键词： Millimeter waves

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An AI‐Enabled Hybrid Lightweight Authentication Model for Digital Healthcare Using Industrial Internet of Things Cyber‐Physical Systems

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Sensors 2022年第4期22卷

作者： Almaiah, Mohammed Amin Hajjej, Fahima Ali, Aitizaz Pasha, Muhammad Fermi Almomani, Omar Department of Computer Networks and Communications College of Computer Sciences and Information Technology King Faisal University Al‐Ahsa31982 Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia School of Information Technology Monash University Sunway City Subang Jaya47500 Malaysia Computer Network and Information Systems Department The World Islamic Sciences and Education University Amman11947 Jordan

In the era of smart healthcare, Internet of Medical Things (IoMT)‐ based Cyber‐Physical Systems (CPS) play an important role, while accessing, monitoring, assessing, and prescribing patients ubiquitously. Efficient authentication and secure data transmission are the influential impediments of these networks that need to be addressed to maintain credence among clients, healthcare specialists, pharmacologists, and other associated entities. To address the authentication and data privacy issues in smart healthcare, in this paper we propose a lightweight hybrid deep learning protocol to achieve security and privacy. To achieve better results, we enabled the decentralized authentication of legitimate patient wearable devices to minimize computation cost, authentication time, and communication overheads with the help of an ML technique to predicate and forward the authentication attributes of patient wearable devices to the next concerned trusted authority, when it is shifted from region to another region. Simulation up-shots of the ML scheme exhibited extraordinary security features with the cost‐effective validation of legal patient wearable devices accompanied by worthwhile communication function-alities compared with previous work. However, the application of IoT‐based medical devices and managing such a broad, sophisticated medical IoT system on standard Single Cloud platforms (CP) would be extremely tough. We propose a scalable FC with a blockchain‐based architecture for a 5G‐enabled IoMT platform. To work on an FC architecture with flowing effects, low over-heads, and secure storage (SS), this research proposes a secured blockchain‐based fogBMIoMT communication mechanism. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： Cryptography

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COVID-19 prediction using machine learning based on the patient's vital signs: A case for Saudi Arabia

COVID-19 prediction using machine learning based on the pati...

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International Conference on Computational Intelligence and Communication networks (CICN)

作者： Nehad M. Ibrahim Kawther S. Alqudaihi Abdullah M. Almuhaideb Irfan Ullah Khan S.O. Olatunji Aadam Fahd A. Alhaidari Nida Aslam Mohammed S. Alshahrani Department of Computer Science College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Department of Networks and Communications College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Department of Computer Engineering College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Department of Emergency Medicine College of Medicine Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia

ISBN: (纸本)9781665487726

The world faces a rapidly spreading of COVID19 globally, for several countries around the world mitigating the consequences and spread of the pandemic remains a top priority. Researchers work to find a smart and rational solution to limit the spread of this epidemic and its repercussions. The goal of this research is to produce an early and accurate COVID-19 prediction, as well as a comparative analysis of the performance of several machine learning (ML) models based on patient vital signs, dataset balancing, and feature selection. The cases dataset was provided by King Fahad Hospital University in Al-Khobar, Saudi Arabia. The current study used the WEKA 3.8.5 and Python programming language (SKLEARN) to decide which method generated the highest level of accuracy while using fewer features. Random forest with grid search (RF with grid search), Artificial Neural networks (ANN), Support Vector Machine (SVM), Random Forest (RF), J48, XGB Classifier, and XGB Classifier with grid search were the techniques that were compared. The highest level of accuracy obtained with seven features was 84% achieved with the RF using grid search technique, while ANN, SVM, RF, J48, XGB Classifier, and XGB Classifier with grid search obtained 82.85%, 79%, 82.93%, 82.5%,82.21%, and 83.4% accuracy, respectively.

关键词： COVID-19 Pandemics Hospitals Support vector machine classification Artificial neural networks Forestry Predictive models

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