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An intrusion detection system using ranked feature bagging

International Journal of Information Technology (Singapore) 2024年第2期16卷 1213-1219页

作者： Azhagiri, M. Rajesh, A. Karthik, S. Raja, K. Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering Jain University Bangalore India Department of IT College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India

An Intrusion Detection System monitors the network for any malicious attacks. It is an ideal tool for protecting extensive business networks from any kind of attack. In this paper, an Intrusion Detection System using Bagging and ranked features has been proposed. The sample datasets for training the ensemble of classifiers in the bagging approach are obtained by generating subsets of the features ranked according to their importance in the classification process. OneR algorithm has been used to rank the features. The decision tree has been used as the base classifier in the bagging approach. The proposed system was able to predict attacks with an accuracy of 99.71% with a minimal number of features and outperformed other similar approaches discussed in the literature. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Bagging CIC-IDS 2017 dataset Decision tree Ensemble Feature selection Intrusion detection system OneR feature selection

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Optimal Confidential Mechanisms in Smart City Healthcare

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computers, Materials & Continua 2022年第3期70卷 4883-4896页

作者： R.Gopi P.Muthusamy P.Suresh C.G.Gabriel Santhosh Kumar Irina V.Pustokhina Denis A.Pustokhin K.Shankar Department of Computing Science and Engineering Dhanalakshmi Srinivasan Engineering CollegePerambalur621212India School of Computing Science and Engineering Galgotias UniversityNCRDelhi203201India Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Electrical and Electronics Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Applications Alagappa UniversityKaraikudi630001India

Smart City Healthcare(SHC2)system is applied in monitoring the patient at home while it is also expected to react to their needs in a timely *** system also concedes the freedom of a *** is a part of this system and it helps in providing care to the *** healthcare devices are trustworthy since it almost certainly recognizes the potential intensifications at very early stage and alerts the patients and medical experts to such an extent that they are provided with immediate *** methodologies exhibit few shortcomings in terms of computational complexity,cost and data ***,the current research article examines SHC2 security through LightWeight Cipher(LWC)with Optimal S-Box model in PRESENT *** procedure aims at changing the sub bytes in which a single function is connected with several bytes’information to upgrade the security level through Swam *** key contribution of this research article is the development of a secure healthcare model for smart city using SHC2 security via LWC and Optimal S-Box *** study used a nonlinear layer and single 4-bit S box for round configuration after verifying SHC2 information,constrained by Mutual Authentication(MA).The security challenges,in healthcare information systems,emphasize the need for a methodology that immovably concretes the *** methodology should act practically,be an effective healthcare framework that depends on solidarity and adapts to the developing *** service providers integrated the IoT applications and medical services to offer individuals,a seamless technology-supported healthcare *** proposed SHC^(2) was implemented to demonstrate its security levels in terms of time and access *** model was tested under different parameters such as encryption time,decryption time,access time and response time inminimum ***,the level of the model and throughput were analyzed by maximum value i.e.,50Mbp

关键词： Smart city healthcare security block cipher LWC

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Advances on Quantum Microcombs 26

Advances on Quantum Microcombs

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Laser Resonators, Microresonators, and Beam Control XXVI 2024

作者： Liu, Fengyu Chembo, Yanne K. Department of Electrical and Computer Engineering 8279 Paint Branch Dr College ParkMD20742 United States

ISBN: (纸本)9781510670020

Quantum microcombs are nonclassical states of light generated when twin photons are excited via spontaneous four-wave mixing in a microresonator. In general, intrinsic losses are overlooked and the theoretical description of these systems implicitly assumes that the quantum microcombs are pure states. In this communication, we propose a density operator theoretical framework that is valid in the general case of a lossy resonator. © 2024 SPIE.

关键词： Photons

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Multi‐granularity re‐ranking for visible‐infrared person re‐identification

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 770-779页

作者： Yadi Wang Hongyun Zhang Duoqian Miao Witold Pedrycz Department of Computer Science and Technology Tongji UniversityShanghaiChina Key Laboratory of Embedded System and Service Computing Ministry of EducationShanghaiChina Department of Electrical and Computer Engineering University of AlbertaEdmontonAlbertaCanada System Research Institute Polish Academy of SciencesWarsawPoland

Visible‐infrared person re‐identification(VI‐ReID)is a supplementary task of single‐modality re‐identification,which makes up for the defect of conventional re‐identification under insufficient *** is more challenging than single‐modality ReID because,in addition to difficulties in pedestrian posture,camera shoot-ing angle and background change,there are also difficulties in the cross‐modality *** works only involve coarse‐grained global features in the re‐ranking calculation,which cannot effectively use fine‐grained ***,fine‐grained features are particularly important due to the lack of information in cross‐modality re‐*** this end,the Q‐center Multi‐granularity K‐reciprocal Re‐ranking Algorithm(termed QCMR)is proposed,including a Q‐nearest neighbour centre encoder(termed QNC)and a Multi‐granularity K‐reciprocal Encoder(termed MGK)for a more comprehensive feature *** converts the probe‐corresponding modality features into gallery corresponding modality features through modality transfer to narrow the modality *** takes a coarse‐grained mutual nearest neighbour as the dominant and combines a fine‐grained nearest neighbour as a supplement for similarity *** experiments on two widely used VI‐ReID benchmarks,SYSU‐MM01 and RegDB have shown that our method achieves state‐of‐the‐art ***,the mAP of SYSU‐MM01 is increased by 5.9%in all‐search mode.

关键词： computer vision recognition

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A Digital Dual 4-Step Switching for an Isolated 3-Phase AC-DC Matrix Converters in Industrial computing Applications

A Digital Dual 4-Step Switching for an Isolated 3-Phase AC-D...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Huang, Peng-Hao Raval, Vishwam Garza-Arias, Enrique Enjeti, Prasad Texas A&m University Department of Electrical & Computer Engineering College StationTX77845 United States

ISBN: (纸本)9798350376067

This paper demonstrates a dead-time free digital dual 4-step switching mechanism implemented on a Silicon-Carbide based industrial isolated 3-phase AC-DC matrix converter, designed for powering industrial computing and artificial intelligence (AI) data center loads. The dual 4-step switching mechanism divides the entire matrix converter into independent high-side and low-side systems. Two systems employ different switching patterns to prevent short circuits between different phase sources and to ensure a continuous inductive current path through the semiconductor. By implementing the proposed dual 4-step switching mechanism, the adverse effects of dead-time caused by high switching frequencies are mitigated, thereby reducing output distortion, and ringing at the switching node. The proposed digital dual 4-step switching is simulated with a 3-phase 230V-480V input to 48V DC output converter. The hardware logic is implemented and evaluated using a Zybo Z7-10 FPGA board. © 2024 IEEE.

关键词： Matrix converters

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Ontology-Based Reinforcement Learning for Semantic Offloading in MEC-Enabled 6G IoT Networks

Ontology-Based Reinforcement Learning for Semantic Offloadin...

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Gyamfi, Eric Ansere, James Adu Golpayegani, Fatemeh School of Computer Science University College Dublin Ireland Sunyani Technical University Department of Electrical Engineering Ghana

ISBN: (纸本)9798350364910

The proliferation of ultra-low-latency and faster convergence applications in sixth-generation (6G) networks necessitates mobile edge computing (MEC) for offloading computationally intensive tasks from user devices to the network edge. However, traditional offloading approaches ignore the semantic aspects of tasks and resources. This paper proposes a novel ontology-based reinforcement learning (ORL) approach for optimal semantic offloading decisions in evolving dynamic MEC-enabled 6G networks, considering network uncertainty and time variation conditions. The proposed ORL approach integrates a dynamic learning mechanism that adjusts and captures semantics task resources, enabling adaptive real-time offloading decisions to satisfy task processing requirements and user demands. The numerical results demonstrate significant improvements in latency reduction, responsiveness, and adaptability compared to traditional offloading approaches. © 2024 IEEE.

关键词： Adversarial machine learning

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Hybrid lightweight encryption for IoT: integrating chaotic key generators with Feistel and substitution-permutation networks for secure 3DV transmission

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Multimedia Tools and Applications 2025年 1-47页

作者： El-Shafai, Walid Mesrega, Ahmed K. Ahmed, Hossam Eldin H. Elbahnasawy, Nirmeen A. El-Samie, Fathi E. Abd Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Deptartment of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

The Internet of Things (IoT) stands as a revolutionary leap in digital connectivity, envisioning a future network connecting billions of devices, seamlessly. Amidst the myriad benefits, there arises an intricate web of challenges, prominently centered around potential threats and data security implications. Recent cryptography techniques, such as DNA-based cryptography, 3D chaos-based cryptography, and optical cryptography, face challenges including large encryption times, high energy consumption, and suboptimal rather than optimal performance. Particularly, the burden of long encryption cycles strains the energy resources of typical low-power and compact IoT devices. These challenges render the devices vulnerable to unauthorized breaches, despite large storage capacities. The hallmark of the IoT ecosystem, characterized by its low-power compact devices, is the burgeoning volume of data they generate. This escalating data influx, while necessitating expansive storage, remains vulnerable to unauthorized access and breaches. Historically, encryption algorithms, with their multifaceted architectures, have been the bulwark against such intrusions. However, their inherently-complex nature, entailing multiple encryption cycles, strains the limited energy reserves of typical IoT devices. In response to this intricate dilemma, we present a hybrid lightweight encryption strategy. Our algorithm innovatively leverages both one-dimensional (1D) and two-dimensional (2D) chaotic key generators. Furthermore, it amalgamates a classical encryption philosophy, harmonizing the strengths of Feistel and substitution-permutation networks. The centerpiece of our strategy is achieving effective encryption in merely three rounds, tailored expressly for compressed Three-Dimensional Video (3DV) frames, ensuring their unwavering integrity. Our workflow commences with the H.264/MVC compression algorithm, setting the stage for the subsequent encryption phase. Through rigorous MATLAB simulations,

关键词： Image coding

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Mask, Hairnet, and Handwash Detection System for Food Manufacturing Health and Safety Monitoring 15

Mask, Hairnet, and Handwash Detection System for Food Manufa...

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15th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2023

作者： Pepito, Collien Princess C. Aleluya, Earl Ryan M. Alagon, Francis Jann A. Clar, Steve E. Abayan, Jared Jan A. Salaan, Carl John O. Bahinting, Maria Fe P. College of Computer Studies Department of Computer Applications Philippines College of Engineering Department of Computer Engineering and Mechatronics Philippines Department of Electrical Engineering College of Engineering Philippines Graduate School of Engineering Mindanao State University-Iligan Institute of Technology Iligan City9200 Philippines

ISBN: (纸本)9798350381177

Food safety is a top concern within food manufacturing facilities, with strict measures in place to ensure the quality and integrity of products. The existing food safety measures typically rely on manual inspections and human vigilance to ensure compliance with safety protocols. These measures, although essential, are prone to human error and limitations in terms of real-time monitoring. Moreover, the global pandemic has underscored the importance of adherence to health and safety guidelines, including the wearing of masks, hairnets, and proper handwashing practices to ensure the well-being of workers and the integrity of food production processes. This research introduces the development of a mask, hairnet, and handwash detection system employing pre-trained deep learning models, specifically YOLOv5 and YOLOv8 (You Only Look Once). Each model was evaluated on various metrics to assess its accuracy and overall performance. The YOLOv8 model showcases outstanding performance in mask and hairnet detection, along with handwash detection. For mask and hairnet detection, it achieved a precision, recall, and F1 score of 100%, and mAP of 99.40%. While handwash detection attained a precision, recall, and F1 score of 100%, and mAP of 92.70%. To provide a comparative analysis between the YOLO models used in the study, the YOLOv5 model was also trained using the same dataset as the YOLOv8 and was evaluated accordingly. Moreover, this study aims to impart significant contributions, particularly in the real-time application of health and safety monitoring within food manufacturing settings. © 2023 IEEE.

关键词： Object detection

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Enhanced Object Detection and Classification via Multi-Method Fusion

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computers, Materials & Continua 2024年第5期79卷 3315-3331页

作者： Muhammad Waqas Ahmed Nouf Abdullah Almujally Abdulwahab Alazeb Asaad Algarni Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Engineering Tech University of Korea237 Sangidaehak-roSiheung-siGyeonggi-do15073South Korea

Advances in machine vision systems have revolutionized applications such as autonomous driving,robotic navigation,and augmented *** substantial progress,challenges persist,including dynamic backgrounds,occlusion,and limited labeled *** address these challenges,we introduce a comprehensive methodology toenhance image classification and object detection *** proposed approach involves the integration ofmultiple methods in a complementary *** process commences with the application of Gaussian filters tomitigate the impact of noise *** images are then processed for segmentation using Fuzzy C-Meanssegmentation in parallel with saliency mapping techniques to find the most prominent *** Binary RobustIndependent Elementary Features(BRIEF)characteristics are then extracted fromdata derived fromsaliency mapsand segmented *** precise object separation,Oriented FAST and Rotated BRIEF(ORB)algorithms *** Algorithms(GAs)are used to optimize Random Forest classifier parameters which lead toimproved *** method stands out due to its comprehensive approach,adeptly addressing challengessuch as changing backdrops,occlusion,and limited labeled data concurrently.A significant enhancement hasbeen achieved by integrating Genetic Algorithms(GAs)to precisely optimize *** minor adjustmentnot only boosts the uniqueness of our system but also amplifies its overall *** proposed methodologyhas demonstrated notable classification accuracies of 90.9%and 89.0%on the challenging Corel-1k and MSRCdatasets,***,detection accuracies of 87.2%and 86.6%have been *** ourmethod performed well in both datasets it may face difficulties in real-world data especially where datasets havehighly complex *** these limitations,GAintegration for parameter optimization shows a notablestrength in enhancing the overall adaptability and performance of our system.

关键词： BRIEF features saliency map fuzzy c-means object detection object recognition

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Preface to the Proceedings of the 8th Smart Data Smart Cities (SDSC) conference ISPRS Annals

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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2024年第4/W4-2024期10卷 1-4页

作者： Pantazis, Dimos N. Karanikolas, Nikitas N. Vassilakopoulos, Michael Coors, Volker Voyiatzis, Ioannis Ellul, Claire Department of Surveying and Geoinformatics Engineering Research laboratory S.O.C.R.A.T.E.S. University of West Attica Greece Department of Informatics and Computer Engineering University of West Attica Greece Department of Electrical and Computer Engineering University of Thessaly Greece Germany Department of Civil Environmental & Geomatic Engineering University College London United Kingdom

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