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4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2023

作者： Pawar, Bhushan Prakash, Vijay Garg, Lalit Galdies, Charles Buttigieg, Sandra Calleja, Neville Department of Computer Information Systems Faculty of Information and Communication Technology University of Malta MSD Msida2080 Malta Department of Computer Science and Engineering Graphics Era Hill University Dehradun India Environmental Management and Plan Division Institute of Earth Systems University of Malta Msida Malta Faculty of Health Sciences University of Malta Msida Malta Faculty of Medicine and Surgery University of Malta Msida Malta

ISBN: (纸本)9789819994410

The rapid growth of urban areas, population, and pollution presents a significant environmental challenge. Addressing these challenges requires innovative analytical approaches and data sources. This study focuses on the three-scale phenomena of the environmental system, physical environment, and regional surroundings, considering their implications on a global network scale. Remote sensing data, with its easy accessibility, offers new toolkits and methods to improve the global environment. It provides self-consistent measurements of vital physical attributes and surpasses traditional data sources regarding accuracy, efficiency, and coverage of larger geographical areas. This study aims to identify and analyze appropriate satellite image analysis tools for healthcare. The study objectives include studying available tools, analyzing their features, categorizing tools into open-source and commercial categories, and providing recommendations. This study emphasizes feature extraction and image enhancement parameters, which are crucial for image analysis. Further, this study provides a functional overview of various commercial and open-source satellite image analysis tools and suggests suitable tools for healthcare applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Geographic information systems

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Energy Efficient Coyote optimizer-based Multipath Routing in MANET 2

Energy Efficient Coyote optimizer-based Multipath Routing in...

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2nd IEEE International Conference on Mobile networks and Wireless communications, ICMNWC 2022

作者： Venkatasubramanian, S. Hariprasath, S. Saranathan College of Engineering Department of Computer Science and Business Systems Trichy India Saranathan College of Engineering Department of Electronics and Communication Trichy India

ISBN: (纸本)9781665491112

MANETs use multi-hop wireless connections to connect nodes, and the topology often changes as nodes move around. Therefore, dynamic routing techniques are required. A multipath routing strategy all better services network factors, including delay, energy consumption, and network throughput in dynamic contexts, than using a single path routing system. Each protocol addresses a specific use case in a networking environment. This can be caused by various factors, such as random loss;delaying packet delivery due to this data loss would harm real-time applications. This study offers guidance on how to deal with the confluence of serious issues that plague MANETs. Among the Ad-hoc On-demand Multipath Distance Vector (AOMDV) routing protocol, this study proposes Coyote optimizer-based Multipath Routing to choose the most efficient route from the available options. The suggested optimizer has a novel algorithmic structure and techniques for balancing exploitation. When data packets are randomly lost, these protocols use an optimization procedure to identify well-organized routes with the highest fitness values, resulting in the least data traffic, shortest path, and residual energy possible. Here, a mechanism is introduced that allows the suggested model to use the TCP Congestion Control Improvement for Random Loss (TCP CERL) to improve the effective route. © 2022 IEEE.

关键词： Energy utilization

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Street Light Cum Garbage System Optimal Design Based on IOT 2

Street Light Cum Garbage System Optimal Design Based on IOT

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2nd International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2024

作者： Rajesh, G. Deepa, J. Ezilarasan, M.R. Syed Fiaz, A. Siva Rama Lingham, N. Jagannathan, J. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Information Technology Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Department of Software and Systems Engineering Vellore India

ISBN: (纸本)9798350328202

Street lights currently use more energy than other types of lighting because of an inefficient mechanism that makes the bulbs use a lot of electricity. The suggested approach uses various sensors on intelligent streetlights to keep an eye on and manage the lights. It has sensors for measuring temperature, brightness, and power that control dimming levels and keep track of status. One Zigbee network serves as the connection point for these lights. In the modification, a cloud- and IoT-based installation was made. Through Zigbee -based data transmission, sensor values are recorded on a distant server known as the Cloud. Additionally, it lowers electricity theft. We include a trash can notice in addition to this street light idea. When the garbage can is whole, a machine automatically notifies the business as people approach the sun, and the brightness of the street lights changes or is increased. © 2024 IEEE.

关键词： Light emitting diodes

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Goal-Oriented Multiple Access Connectivity for Networked Intelligent systems

arXiv

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arXiv 2024年

作者： Agheli, Pouya Pappas, Nikolaos Kountouris, Marios Communication Systems Department EURECOM Sophia-Antipolis France Department of Computer Science and Artificial Intelligence University of Granada Spain Department of Computer and Information Science Linköping University Sweden

We design a self-decision goal-oriented multiple access scheme, where sensing agents observe a common event and individually decide to communicate the event’s attributes as updates to the monitoring agents, to satisfy a certain goal. Decisions are based on the usefulness of updates, generated under uniform, change- and semantics-aware acquisition, as well as statistics and updates of other agents. We obtain optimal activation probabilities and threshold criteria for decision-making under all schemes, maximizing a grade of effectiveness metric. Alongside studying the effect of different parameters on effectiveness, our simulation results show that the self-decision scheme may attain at least 92% of optimal performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Decision making

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Experimental evaluation of MU-MIMO in TDD environment for 5G NR using exploiting channel reciprocity

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International Journal of Information Technology (Singapore) 2024年第7期16卷 4651-4666页

作者： Kumar, Rajesh Sinwar, Deepak Singh, Vijander Department of Computer and Communication Engineering Manipal University Jaipur Rajasthan Jaipur 303007 India Department of IoT and Intelligent Systems Manipal University Jaipur Rajasthan Jaipur 303007 India Department of ICT and Natural Sciences Faculty of Information Technology and Electrical Engineering NTNU - Norwegian University of Science and Technology Ålesund Norway

5G New Radio (NR) operates in two frequency ranges viz. Frequency Range 1 (sub-6 GHz band) and Frequency Range 2 (millimeter wave communication). It utilizes Time Division Duplex (TDD) communication, allowing the same frequency band for uplink and downlink transmission using sounding reference signals. These signals help estimate channel conditions for scheduling downlink traffic. Multi-User Multiple Input Multiple Output (MU-MIMO) technology enables simultaneous data transmission to multiple users using multiple antennas. This study examines MU-MIMO performance in TDD by analyzing variables like radio conditions, modulation schemes, code rates, precoding algorithms, and sub-carrier spacing. Simulation results show that Zero Forcing (ZF) precoding outperforms Block Diagonalization (BD) and Regularized Zero Forcing (RZF) in good radio conditions. However, in bad radio conditions, RZF performs better than ZF. Choosing higher code rates in 16 and 64-QAM and lower code rates in 256-QAM from the MCS table improves throughput. This indicates that MCS and user selection are crucial for higher throughput in TDD-based MU-MIMO for 5G NR. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： 5G new radio Beamforming MU-MIMO Precoding Sounding reference signal Time division duplex

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Analog Self-Interference Cancellation By Means of a Synchronised Signal Injection

Analog Self-Interference Cancellation By Means of a Synchron...

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European Conference on Antennas and Propagation, EuCAP

作者： Sarmad Ozan Geoffrey Hilton Tommaso Cappello Mark Beach Communication Systems & Networks Research Group University of Bristol United Kingdom Department of Electrical and Computer Engineering Villanova University United States

Full-duplex communications have the potential to double the spectral efficiency. Self-interference hinders the realization of full-duplex communications, thus significant suppression needs to be achieved to enable simultaneous transmit and receive. This paper presents a digitally-assisted analog self-interference cancellation by pre-coding a cancellation waveform on an auxiliary transmitter using a modified copy of the transmitted signal. Over 30 dB self-interference cancellation has been achieved over a bandwidth of 20 MHz.

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Association rule learning for threat analysis using traffic analysis and packet filtering approach

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International Journal of Information Technology (Singapore) 2023年第6期15卷 3245-3255页

作者： Rawat, Romil Chakrawarti, Rajesh Kumar Raj, A. Samson Arun Mani, Gayathri Chidambarathanu, K. Bhardwaj, Ramakant Computer and Communication Technology Department University of Extremadura Badajoz Spain Department of Computer Science and Engineering Sushila Devi Bansal College (SDBC) Bansal Group of Institutions (BGI) Indore India Karunya Institute of Technology and Sciences Karunya Nagar Tamil Nadu Coimbatore India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Science and Business Systems R.M.K. Engineering College Chennai India Department of Mathematics Amity University West Bengal Kolkata India

The Mirai botnet source code, which was distributed on the dark web, is still changing as malware designers modify it to produce more sophisticated varieties of the botnet—the Satori—based on the malware source code. An botnet that is tied to the Internet of Things, infested with malware, and administered by offenders creates a backdoor for cybercriminals to carry out destructive operations throughout the system. The work provides association rule learning, which reveals design characteristics and warning indications of hazards using data (a data stream) collected for darknet vulnerability research. The negative effects of threat-originating computers associated with malware scripts are investigated by looking at trends in IoT-linked applications (like destination port, TCP window lengths, and kind of service).This paper provides an study of an attack (both before and after) with the first source scripts development of a conventional IoT malware, the Satori, as well as an uncommon insight into the viral processes of an attack campaign. Trials demonstrate that the suggested system is efficient in anticipating and tracking new virus tendencies in cyberspace, demonstrating that it is a viable way for streamlining and accelerating the discovery of new illicit patterns in online activities. Satori-based mathematical modelling of destination ports, ToS, and TCP window size is provided with NICT/16 DN sensor dataset that is employed in the research. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Association rule learning Cyber-attack Dark web IoT Malware

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On the Passive Fingerprinting Attack for IoT Device Classification

On the Passive Fingerprinting Attack for IoT Device Classifi...

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IEEE International Symposium on Telecommunication Technologies (ISTT)

作者： Tabina Kamal Fazirulhisyam Hashim Mohd. Fadlee A. Rasid Faisul Arif Ahmad Department of Computer and Communication Systems Engineering Wireless and Photonics Networks Research Centre (WiPNET) Universiti Putra Malaysia Serdang Selangor Malaysia

ISBN: (数字)9798350392074

ISBN: (纸本)9798350392081

The rapid growth of the Internet of Things (IoT), expected to exceed 29 billion devices by 2027, presents a significant security challenge. Device fingerprinting, which identifies devices through unique network traffic patterns, is a valuable security tool but can be exploited by attackers for undetectable reconnaissance. This paper explores the feasibility of passive traffic fingerprinting attacks from an attacker's perspective, focusing on a small-scale testbed with four devices from an undisclosed single vendor. Using minimal resources and open-source tools, traffic patterns were analyzed, confirming unique device behaviors. A Random Forest (RF) classifier was developed, demonstrating high precision with instances where 100 % classification accuracy can be achieved in a specific experimental scenario using just two features: Source Port and Destination Port. Key aspects of the model included feature selection, hyperparameter tuning, and a tree depth optimization of 20. Notably, forward feature selection proved more effective than Principal Component Analysis (PCA). These preliminary results underscore the vulnerabilities of single-vendor IoT ecosystems and highlight the simplicity and replicability of this low-cost attack methodology. The ease with which attackers can implement this approach underscores the urgent need for robust defenses as IoT devices proliferate across various sectors.

关键词： Telecommunication traffic Fingerprint recognition Traffic control Reconnaissance Feature extraction Internet of Things Tuning Replicability Random forests Principal component analysis

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A hybrid machine learning approach for analysis of stegomalware

International Journal of Industrial Engineering and Operatio...

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International Journal of Industrial Engineering and Operations Management 2023年第2期5卷 104-117页

作者： Kadebu, Prudence Shoniwa, Robert T.R. Zvarevashe, Kudakwashe Mukwazvure, Addlight Mapanga, Innocent Thusabantu, Nyasha Fadzai Gotora, Tatenda Trust Department of Computer Engineering University of Zimbabwe Harare Zimbabwe Zimbabwe Information and Communication Technology Harare Zimbabwe Victoria Institute of Technology Adelaide Australia A Division of Zimbabwe Institution of Engineers Zimbabwe Information and Communication Technology Harare Zimbabwe Department of Academics Harare Institute of Technology Belvedere Zimbabwe Department of Computer Science Midlands State University Gweru Zimbabwe Department of Information Systems Women's University in Africa Harare Zimbabwe

Purpose: Given how smart today’s malware authors have become through employing highly sophisticated techniques, it is only logical that methods be developed to combat the most potent threats, particularly where the malware is stealthy and makes indicators of compromise (IOC) difficult to detect. After the analysis is completed, the output can be employed to detect and then counteract the attack. The goal of this work is to propose a machine learning approach to improve malware detection by combining the strengths of both supervised and unsupervised machine learning techniques. This study is essential as malware has certainly become ubiquitous as cyber-criminals use it to attack systems in cyberspace. Malware analysis is required to reveal hidden IOC, to comprehend the attacker’s goal and the severity of the damage and to find vulnerabilities within the system. Design/methodology/approach: This research proposes a hybrid approach for dynamic and static malware analysis that combines unsupervised and supervised machine learning algorithms and goes on to show how Malware exploiting steganography can be exposed. Findings: The tactics used by malware developers to circumvent detection are becoming more advanced with steganography becoming a popular technique applied in obfuscation to evade mechanisms for detection. Malware analysis continues to call for continuous improvement of existing techniques. State-of-the-art approaches applying machine learning have become increasingly popular with highly promising results. Originality/value: Cyber security researchers globally are grappling with devising innovative strategies to identify and defend against the threat of extremely sophisticated malware attacks on key infrastructure containing sensitive data. The process of detecting the presence of malware requires expertise in malware analysis. Applying intelligent methods to this process can aid practitioners in identifying malware’s behaviour and features. This is especially e

关键词： Machine learning Malware analysis Malware detection Steganography Stegomalware

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Colliding Bodies Optimization with Machine Learning Based Parkinson’s Disease Diagnosis

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computer systems science & Engineering 2023年第3期44卷 2195-2207页

作者： Ashit Kumar Dutta Nazik M.A.Zakari Yasser Albagory Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Nursing College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Parkinson’s disease(PD)is one of the primary vital degenerative diseases that affect the Central Nervous System among elderly *** affect their quality of life drastically and millions of seniors are diagnosed with PD every year *** models have been presented earlier to detect the PD using various types of measurement data like speech,gait patterns,*** identification of PD is important owing to the fact that the patient can offer important details which helps in slowing down the progress of *** recently-emerging Deep Learning(DL)models can leverage the past data to detect and classify *** this motivation,the current study develops a novel Colliding Bodies Optimization Algorithm with Optimal Kernel Extreme Learning Machine(CBO-OKELM)for diagnosis and classification of *** goal of the proposed CBO-OKELM technique is to identify whether PD exists or ***-OKELM technique involves the design of Colliding Bodies Optimization-based Feature Selection(CBO-FS)technique for optimal subset of *** addition,Water Strider Algorithm(WSA)with Kernel Extreme Learning Machine(KELM)model is also developed for the classification of *** algorithm is used to elect the optimal set of fea-tures whereas WSA is utilized for parameter tuning of KELM model which alto-gether helps in accomplishing the maximum PD diagnostic *** experimental analysis was conducted for CBO-OKELM technique against four benchmark datasets and the model portrayed better performance such as 95.68%,96.34%,92.49%,and 92.36%on Speech PD,Voice PD,Hand PD Mean-der,and Hand PD Spiral datasets respectively.

关键词： Parkinson’s disease colliding bodies optimization algorithm feature selection metaheuristics classification kelm model

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