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13th International Conference on Dependable systems, Services and Technologies, DESSERT 2023

作者： Lysenko, Sergii Vorobiov, Volodymyr Bokhonko, Oleksandr Gaj, Piotr Savenko, Oleg Woloszyn, Jacek Khmelnitsky National University Department of Computer Engineering and Information Systems Khmelnitsky Ukraine Silesian University of Technology Gliwice Poland Khmelnitsky National University Department of Computer Engineering and System Programming Khmelnitsky Ukraine Kazimierz Pulaski University of Technology and Humanities in Radom Department of Informatics and Teleinformatics Radom Poland

ISBN: (纸本)9798350396119

The paper focuses on the problem of technical social engineering attacks that encompass the manipulation of individuals to reveal sensitive information, execute actions, or breach security systems. These exploits frequently capitalize on human psychology, trust, and a lack of vigilance to attain unauthorized entry to networks, systems, or data. In contrast to traditional social engineering tactics that center on psychological manipulation, technical social engineering attacks employ technological means and strategies to manipulate and beguile individuals. The paper presents an attempt to detect social engineering attacks. The approach utilized four machine learning algorithms (decision tree, random forest, K-nearest neighbor, and extreme gradient boosting). The analysis is centered on data collected from network hosts, which may serve as indicators of a potential social engineering attack. The empirical results demonstrated high detection accuracy. © 2023 IEEE.

关键词： Cybersecurity

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Watchtower Selection in Off-Blockchain PCN Using Peterson Leader-Election Algorithm 21

Watchtower Selection in Off-Blockchain PCN Using Peterson Le...

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21st International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2022

作者： Amin, Md Faizul Ibne Watanobe, Yutaka Rahman, Md. Mostafizer Kabir, Raihan Graduate Department of Computer and Information System The University of Aizu Fukushima AizuWakamatsu Japan Department of Computer Science and Engineering Dhaka University of Engineering & Technology Gazipur Bangladesh

ISBN: (纸本)9781643683164

Despite the incredible adoption of cryptocurrencies, blockchain-based cryptocurrencies have likewise raised some concerns. The scalability problem is the major one among them. An off-blockchain payment channel network (PCN) has been introduced to solve this issue. PCN can fundamentally reduce blockchain scalability by constructing a number of payment channels between the nodes and without committing every single transaction to the blockchain. But as a matter of fact, there has an unwanted assumption in PCN that channel participants must remain online and follow blockchain updates, for the synchronization with blockchain to protect the channel against deception. To mitigate this issue 'Watchtower' concept has been proposed. Watchtower is a watching service and always stays online that a channel participant can hire it by offering incentives for monitoring the channel and checking blockchain updates consistently to prevent fraud on behalf of the hiring party. However, watchtower may be more beneficial by cooperating with the cheating counterparty and neglecting to perform the watching service properly. The efficiency drawback can occur for that. In this work, we have been motivated by this issue and tried to find out an effective and reliable watchtower for the channel watching service from multiple watchtower nodes or candidates in the PCN. In particular, we have been approached by using the distributed Peterson Leader-Election Algorithm to find the best watchtower among multiple of them where the more successfully performed work node or candidate will be selected for the channel monitoring job. We also have provided a detailed step-by-step process of the algorithm including experiments and illustrations for employing watchtower among multiple of them. © 2022 The authors and IOS Press. All rights reserved.

关键词： Blockchain

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Adaptive Confidence Threshold for ByteTrack in Multi-Object Tracking 12

Adaptive Confidence Threshold for ByteTrack in Multi-Object ...

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12th IEEE International Conference on Control, Automation and Information Sciences, ICCAIS 2023

作者： Ma, Linh Van Hussain, Muhammad Ishfaq Park, JongHyun Kim, Jeongbae Jeon, Moongu Gwangju Institute of Science and Technology School of Electrical Engineering and Computer Science Korea Republic of Pusan National University Department of Mechanical-IT Convergence System Engineering Busan Korea Republic of

ISBN: (纸本)9798350328783

We investigate the application of ByteTrack in the realm of multiple object tracking. ByteTrack, a simple tracking algorithm, enables the simultaneous tracking of multiple objects by strategically incorporating detections with a low confidence threshold. Conventionally, objects are initially associated with high confidence threshold detections. When the association between objects and detections becomes ambiguous, ByteTrack extends the association to lower confidence threshold detections. One notable drawback of the existing ByteTrack approach is its reliance on a fixed threshold to differentiate between high and low-confidence detections. In response to this limitation, we introduce a novel and adaptive approach. Our proposed method entails a dynamic adjustment of the confidence threshold, leveraging insights derived from overall detections. Through experimentation, we demonstrate the effectiveness of our adaptive confidence threshold technique while maintaining running time compared to ByteTrack. © 2023 IEEE.

关键词： Object detection

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Advancements in Wireless Networks: Transition from 5G to Beyond 2

Advancements in Wireless Networks: Transition from 5G to Bey...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Thangavel, Gunasekaran Malarvizhi, C. Banu, M. Ameena Anthonisamy, Arun Hemalatha, M. Manikandan, Raja University of Technology and Applied Sciences Department of Electronics and Communication Engineering Muscat Oman Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Chembarambakkam India NPR College of Engineering and Technology Department of Electronics and Communication Engineering Natham India Panimalar Engineering College Department of Computer Science and Business System Tamil Nadu Chennai600123 India Nandha Arts and Science College Department of Information Technology Tamil Nadu Erode638052 India K. Ramakrishnan College of Technology Department of ECE Tamil Nadu Trichy India

ISBN: (纸本)9798331534950

Earlier generations of mobile technology experienced exponential growth in mobile customers, disruptive ecosystems like Internet of Things applications, and massive channel capacity needed to connect trillions of devices. 5G networks are a vital solution. 5G aims to provide 10 Gbps to a single user, as well as quality of service, increased spectrum efficiency, ultra-reliable communication, extended device battery life, massive machine-type communication (mMTC), and a 1000-fold capacity gain. Due to the heavy traffic volume and high resource consumption of 5G applications like augmented reality (AR), virtual reality (VR), and wireless body area networks, there are major barriers to channel capacity and potential service disruptions for other users. New strategies are needed to increase spectral efficiency and channel capacity. This paper explores many major new wireless technologies that aim to increase spectral efficiency and channel capacity to meet growing user demands and improve network performance. This paper also examines these technologies' benefits and research challenges. Massive Multi-Input Multi-Output (mMIMO) technology's effectiveness and potential distinguish it for 5G and Beyond 5G (B5G) networks, offering a variety of benefits and characteristics. This article can also assist researchers who are conducting in-depth studies, as well as wireless network operators who aim to streamline the development of 5G and B5G networks. © 2024 IEEE.

关键词： 5G mobile communication systems

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Advanced Data Segmentation and Encryption Approach using Fog Computing

Advanced Data Segmentation and Encryption Approach using Fog...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Singh, Kamred Udham Varshney, Neeraj Kumar, Pushpendra Sharma, Pradeep Kumar Mishra, Umang Deep Singh, Teekam Graphic Era Hill University School of Computing Dehradun India Gla University Department of Computer Engineering & Applications UP Mathura India Scanno System Greater Noida India Hcl Technology UP India Unique Institute of Management & Technology Muradnagar India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350382723

Data security in conventional applications was only available at the application layer. Within the scope of the intended research, the safety of the merchandise is ensured. The deficiencies that are present in the security systems that are already in place have led to the realisation that a new security system is required. Considering that authentication is not present, the likelihood of decryption should be reduced. Establishing a decentralised security system is essential in order to protect against threats coming from a variety of digital networks. The new encryption standard is said to improve multilayer security, according to the claims. The material is split into two halves using this method, which also provides a reliable transmission mechanism. Because of the technology, hackers are unable to create vulnerabilities in the security system. There was a need for security in both the cloud and the edge environment in order to prevent intruders. Increasing the reliability and security of data transmission over a variety of networks was the motivation for the development of the two fish approach. Because of its security mechanism, it has been able to survive assaults from crackers and hackers. The paper that is being presented advises doing research on the safety of edge transmissions that are based on slicing. In order to prevent attackers from launching attacks from several networks, it is necessary to establish decentralised security. Layered security will help increase encryption standards, which is a significant benefit. Over the course of various investigations, computer security edge generators have been the focus of attention. Quite a few of them have already been extensively discussed. For the purpose of developing a reliable method of transmission, the proposed research splits the data into two parts, each of which is further encrypted at a different level. As a result of the development of multilayer encryption, the reliability and safety of data transm

关键词： Fish

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Skill-Based Edge-Brain Smart Manufacturing: A Case of Grasp Pose Selection Skill 6

Skill-Based Edge-Brain Smart Manufacturing: A Case of Grasp ...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Lee, Sukhan Jang, Byungwoo Hyeon, Seokjong Lee, Soojin Lee, Jaesun Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Artificial Intelligence Sungkyunkwan University Suwon16419 Korea Republic of Department of Computer Science and Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of AI System Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Regional Industry Innovation Korea Institute of Industrial Technology Ulsan44413 Korea Republic of

The edge-brain framework (EBF) is a S/W framework that allows manufacturers to easily integrate AI-empowered robot work skills into a legacy production system in an edge computing environment with minimal time and effort. For EBF-based smart manufacturing, is a skill-based task-level robot programming environment that plays a key role. In this paper, we present the grasp pose selection skill as a case study to demonstrate the proposed skill-based edge-brain smart manufacturing. The proposed grasp-pose selection skill determines the optimal 6D grasp pose associated with the target object in a cluttered workspace by taking into consideration grasp stability, object manipulability, and collision-free path efficacy. To achieve a real-time operation of optimal grasp pose selection skill, deep learning networks are designed to compute, in particular, the manipulability and collision-free path efficacy indices. Experiments demonstrate the effectiveness of the proposed real-time grasp pose selection skill developed for EBF-based smart manufacturing. © 2024 The Authors. Published by Elsevier B.V.

关键词： Smart manufacturing

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Empowering Diagnosis: Cutting-Edge Segmentation and Classification in Lung Cancer Analysis

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computers, Materials & Continua 2024年第6期79卷 4963-4977页

作者： Iftikhar Naseer Tehreem Masood Sheeraz Akram Zulfiqar Ali Awais Ahmad Shafiq Ur Rehman Arfan Jaffar Faculty of Computer Sciences&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Information System Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Lung cancer is a leading cause of global mortality *** detection of pulmonary tumors can significantly enhance the survival rate of ***,various computer-Aided Diagnostic(CAD)methods have been developed to enhance the detection of pulmonary nodules with high ***,the existing method-ologies cannot obtain a high level of specificity and *** present study introduces a novel model for Lung Cancer Segmentation and Classification(LCSC),which incorporates two improved architectures,namely the improved U-Net architecture and the improved AlexNet *** LCSC model comprises two distinct *** first stage involves the utilization of an improved U-Net architecture to segment candidate nodules extracted from the lung ***,an improved AlexNet architecture is employed to classify lung *** the first stage,the proposed model demonstrates a dice accuracy of 0.855,a precision of 0.933,and a recall of 0.789 for the segmentation of candidate *** suggested improved AlexNet architecture attains 97.06%accuracy,a true positive rate of 96.36%,a true negative rate of 97.77%,a positive predictive value of 97.74%,and a negative predictive value of 96.41%for classifying pulmonary cancer as either benign or *** proposed LCSC model is tested and evaluated employing the publically available dataset furnished by the Lung Image Database Consortium and Image Database Resource Initiative(LIDC-IDRI).This proposed technique exhibits remarkable performance compared to the existing methods by using various evaluation parameters.

关键词： Lung cancer segmentation AlexNet U-Net classification

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Resilience assessment framework toward interdependent bus-rail transit network:Structure,critical components,and coupling mechanism

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Communications in Transportation Research 2023年第1期3卷 118-129页

作者： Bing Liu Xiaoyue Liu Yang Yang Xi Chen Xiaolei Ma School of Transportation Science and Engineering Beihang UniversityBeijing100191China Department of Civil&Environmental Engineering University of UtahSalt Lake CityUT84112USA School of Computer Science and Engineering Beihang UniversityBeijing100191China Key Laboratory of Intelligent Transportation Technology and System Ministry of EducationBeijing100191China

Understanding the interdependent nature of multimodal public transit networks(PTNs)is vital for ensuring the resilience and robustness of transportation ***,previous studies have predominantly focused on assessing the vulnerability and characteristics of single-mode PTNs,neglecting the impacts of heterogeneous disturbances and shifts in travel behavior within multimodal ***,this study introduces a novel resilience assessment framework that comprehensively analyzes the coupling mechanism,structural and functional characteristics of bus-rail transit networks(BRTNs).In this framework,a network performance metric is proposed by considering the passengers’travel behaviors under various ***,stations and subnetworks are classified using the k-means algorithm and resilience metric by simulating various disturbances occurring at each station or *** proposed framework is validated via a case study of a BRTN in Beijing,*** indicate that the rail transit network(RTN)plays a crucial role in maintaining network function and resisting external disturbances in the interdependent ***,the coupling interactions between the RTN and bus transit network(BTN)exhibit distinct characteristics under infrastructure component disruption and functional *** findings provide valuable insights into emergency management for PTNs and understanding the coupling relationship between BTN and RTN.

关键词： Complex network Bus-rail transit network Resilience analysis Interdependent coupling relationship Classification analysis

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Improved Density Peaking Algorithm for Community Detection Based on Graph Representation Learning

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computer systems Science & engineering 2022年第12期43卷 997-1008页

作者： Jiaming Wang Xiaolan Xie Xiaochun Cheng Yuhan Wang Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin541006GuangxiChina School of Information Science and Engineering Guilin University of TechnologyGuilin541006GuangxiChina Department of Computer Science Middlesex UniversityLondon

There is a large amount of information in the network data that we canexploit. It is difficult for classical community detection algorithms to handle network data with sparse topology. Representation learning of network data is usually paired with clustering algorithms to solve the community detection ***, there is always an unpredictable distribution of class clusters outputby graph representation learning. Therefore, we propose an improved densitypeak clustering algorithm (ILDPC) for the community detection problem, whichimproves the local density mechanism in the original algorithm and can betteraccommodate class clusters of different shapes. And we study the communitydetection in network data. The algorithm is paired with the benchmark modelGraph sample and aggregate (GraphSAGE) to show the adaptability of ILDPCfor community detection. The plotted decision diagram shows that the ILDPCalgorithm is more discriminative in selecting density peak points compared tothe original algorithm. Finally, the performance of K-means and other clusteringalgorithms on this benchmark model is compared, and the algorithm is proved tobe more suitable for community detection in sparse networks with the benchmarkmodel on the evaluation criterion F1-score. The sensitivity of the parameters ofthe ILDPC algorithm to the low-dimensional vector set output by the benchmarkmodel GraphSAGE is also analyzed.

关键词： Representation learning data mining low-dimensional embedding community detection density peaking algorithm

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Intelligent Caching Strategies for 5G Edge Networks using Machine Learning 4

Intelligent Caching Strategies for 5G Edge Networks using Ma...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Guduri, Naga Venkata Ramakrishna Sethu, S. Shalinirajan, R. Reena, R. Karthikeyan, M. Uthayakumar, G.S. University of Technology and Applied Sciences-Al Musannah Electrical and Electronics Section Engineering Department Oman Department of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Department of Computer Science and Business System Tamilnadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Tamilnadu Chennai India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350300888

To reduce latency and improve the user experience, content caching solutions at the network periphery will become increasingly important as 5G networks and data-intensive applications. In this proposed work, the deployment of a caching architecture for 5G periphery networks using machine learning is done. Utilizing information on user behavior, network conditions, and content prominence enables more accurate forecasts and cache management decisions. This trains the prediction models using a wide range of machine-learning techniques to accommodate the ever-changing needs of both networks and content. To evaluate the efficiency of the proposed architecture by measuring its ability to increase cache hit rates and decrease latency. These techniques significantly outperform the standard caching methods. With the intelligent caching architecture of 5G periphery networks, throughput is increased, latency is reduced, and congestion is alleviated. The findings of this study contribute to the growing corpus of literature on AI-driven optimization in 5G networks and shed light on how such solutions could be enhanced for future networks. Neural Networks achieved 96% accuracy than SVM which is 93%. © 2023 IEEE.

关键词： 5G mobile communication systems

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