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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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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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Energy-Aware Heterogeneous Federated Learning via Approximate DNN Accelerators

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第6期44卷 2054-2066页

作者： Pfeiffer, Kilian Balaskas, Konstantinos Siozios, Kostas Henkel, Jorg Karlsruhe Institute of Technology Chair for Embedded Systems Karlsruhe76131 Germany University of Patras Department of Computer Engineering and Informatics Patras26504 Greece Aristotle University of Thessaloniki Department of Physics Thessaloniki54124 Greece

In Federated Learning (FL), devices that participate in the training usually have heterogeneous resources, i.e., energy availability. In current deployments of FL, devices that do not fulfill certain hardware requirements are often dropped from the collaborative training. However, dropping devices in FL can degrade training accuracy and introduce bias or unfairness. Several works have tackled this problem on an algorithm level, e.g., by letting constrained devices train a subset of the server neural network (NN) model. However, it has been observed that these techniques are not effective w.r.t. accuracy. Importantly, they make simplistic assumptions about devices’ resources via indirect metrics, such as multiply accumulate (MAC) operations or peak memory requirements. We observe that memory access costs (that are currently not considered in simplistic metrics) have a significant impact on the energy consumption. In this work, for the first time, we consider on-device accelerator design for FL with heterogeneous devices. We utilize compressed arithmetic formats and approximate computing, targeting to satisfy limited energy budgets. Using a hardware-aware energy model, we observe that, contrary to the state of the art’s moderate energy reduction, our technique allows for lowering the energy requirements (by 4× ) while maintaining higher accuracy. © 1982-2012 IEEE.

关键词： Budget control

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A fusion of machine learning and cryptography for fast data encryption through the encoding of high and moderate plaintext information blocks

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Multimedia Tools and Applications 2025年第8期84卷 5349-5375页

作者： Shafique, Arslan Mehmood, Abid Alawida, Moatsum Elhadef, Mourad Rehman, Mujeeb Ur School of Biomedical Engineering University of Glasgow Glasgow United Kingdom Department of Computer Sciences Abu Dhabi University Abu Dhabi United Arab Emirates Cyber Technology Institute School of Computer Science and Informatics De Montfort University Leicester United Kingdom

Within the domain of image encryption, an intrinsic trade-off emerges between computational complexity and the integrity of data transmission security. Protecting digital images often requires extensive mathematical operations for robust security. However, this computational burden makes real-time applications unfeasible. The proposed research addresses this challenge by leveraging machine learning algorithms to optimize efficiency while maintaining high security. This methodology involves categorizing image pixel blocks into three classes: high-information, moderate-information, and low-information blocks using a support vector machine (SVM). Encryption is selectively applied to high and moderate information blocks, leaving low-information blocks untouched, significantly reducing computational time. To evaluate the proposed methodology, parameters like precision, recall, and F1-score are used for the machine learning component, and security is assessed using metrics like correlation, peak signal-to-noise ratio, mean square error, entropy, energy, and contrast. The results are exceptional, with accuracy, entropy, correlation, and energy values all at 97.4%, 7.9991, 0.0001, and 0.0153, respectively. Furthermore, this encryption scheme is highly efficient, completed in less than one second, as validated by a MATLAB tool. These findings emphasize the potential for efficient and secure image encryption, crucial for secure data transmission in rea-time applications. © The Author(s) 2024.

关键词： Cryptography

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Supremal and Robust Control Against Actuator Jamming Attacks in Cyber-Physical Systems

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第9期9卷 5435-5447页

作者： Duo, Wenli Wang, Shouguang Zhou, MengChu You, Dan Winiewski, Remigiusz Bazydlo, Grzegorz Albeshri, Aiiad Macau University of Science and Technology Institute of Systems Engineering 999078 China Zhejiang Gongshang University School of Information and Electronic Engineering Hangzhou310018 China New Jersey Institute of Technology Helen and John C. Hartmann Department of Electrical and Computer Engineering NewarkNJ07102 United States University of Zielona Góra Institute of Control and Computation Engineering Zielona Góra65-516 Poland King Abdulaziz University Department of Computer Science Jeddah21481 Saudi Arabia

Cyber-physical systems, such as unmanned aerial vehicles and connected and autonomous vehicles, are vulnerable to cyber attacks, which can cause significant damage to society. This paper examines the attack issue in cyber-physical systems within the framework of discrete event systems. Specifically, we consider a scenario where a malicious intruder injects a jamming signal into an actuator channel. It disrupts the transmission of control commands and prevents an actuator from receiving them. This is termed an actuator jamming attack. In the paper, we first analyze the closed-loop system behavior under such an attack. An attack structure is constructed to illustrate how an intruder exploits a jamming attack to drive a system into unsafe states. Then, we study the supervisory control problem for a system exposed to such an attack. The problem is reduced to a basic supervisory control one in discrete event systems by introducing the concept of dynamically controllable language. A solution to this problem is explored, where we establish an existence condition for a supremal and robust supervisor that is capable of defending against actuator jamming attacks, and design an algorithm to derive it. Finally, the effectiveness of our method is illustrated by an intelligent automated guided vehicle system. © 2016 IEEE.

关键词： Discrete event simulation

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A novel medical image data protection scheme for smart healthcare system

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CAAI Transactions on Intelligence technology 2024年第4期9卷 821-836页

作者： Mujeeb Ur Rehman Arslan Shafique Muhammad Shahbaz Khan Maha Driss Wadii Boulila Yazeed Yasin Ghadi Suresh Babu Changalasetty Majed Alhaisoni Jawad Ahmad School of Computer Science and Informatics Cyber Technology InstituteDe Montfort UniversityLeicesterUK Department of Biomedical Engineering University of GlasgowGlasgowUK School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghUK Robotics and Internet‐of‐Things Laboratory Prince Sultan UniversityRiyadhSaudi Arabia Department of Computer Science Al Ain UniversityAl AinUnited Arab Emirates Department of Computer Engineering College of Computer Science King Khalid UniversityAbhaSaudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

The Internet of Multimedia Things(IoMT)refers to a network of interconnected multimedia devices that communicate with each other over the ***,smart healthcare has emerged as a significant application of the IoMT,particularly in the context of knowledge‐based learning *** healthcare systems leverage knowledge‐based learning to become more context‐aware,adaptable,and auditable while maintain-ing the ability to learn from historical *** smart healthcare systems,devices capture images,such as X‐rays,Magnetic Resonance *** security and integrity of these images are crucial for the databases used in knowledge‐based learning systems to foster structured decision‐making and enhance the learning abilities of ***,in knowledge‐driven systems,the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel,leading to data trans-mission *** address the security and latency concerns,this paper presents a lightweight medical image encryption scheme utilising bit‐plane decomposition and chaos *** results of the experiment yield entropy,energy,and correlation values of 7.999,0.0156,and 0.0001,*** validates the effectiveness of the encryption system proposed in this paper,which offers high‐quality encryption,a large key space,key sensitivity,and resistance to statistical attacks.

关键词： data analysis medical image processing security

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Insights into research on blockchain for smart contracts: a bibliometric analysis

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Multimedia Tools and Applications 2024年 1-25页

作者： Singh, Renu Gupta, Ashlesha Mittal, Poonam Faculty of Informatics and Computing J.C. Bose University of Science and Technology YMCA Faridabad India Department of Computer Science & amp Engineering Indraprastha Institute of Information Technology IIIT New Delhi India

Over the past few years, blockchain technology has gained significant attention. This surge in popularity can be attributed to the emergence of cryptocurrencies and the development of smart contracts. Cryptocurrency is a digital currency that eliminates the problem of double spending. Cryptocurrencies like Bitcoin, Ethereum, Litecoin, Stellar, Zcash, Maker, Aave, etc. become popular and are preferred for money transfers. Smart contracts are the next popular technology on the blockchain after cryptocurrency. It can be considered a piece of code that can execute automatically when the predefined conditions are fulfilled. Researchers believe that the potential of blockchain with smart contracts is only in its initial stages and that its true potential has yet to be fully discovered. Hence, an extensive bibliometric analysis is conducted to understand blockchain trends for smart contracts and to give future directions in this field. For this analysis, various steps are followed, starting with formulating the research question, defining the scope of our research, extracting and analyzing data, answering the research question, and finally, drawing a conclusion. This research paper will be fruitful for scholars and researchers, providing an extensive statistical and network analysis of extracted smart contracts publications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Bibliometric analysis Blockchain Crypto contract Scientometrics Scopus Smart contracts VOSViewer

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A Terramechanics-based Dynamic Model for Motion Control of Unmanned Tracked Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Zhang, Ruizeng Zhou, Wei Liu, Haiou Gong, Jianwei Chen, Huiyan Khajepour, Amir School of Mechanical Engineering Beijing Institute of Technology Beijing China School of Computer Science and Informatics Cardiff University Cardiff U.K Department of Mechanical and Mechatronics Engineering University of Waterloo Waterloo Canada

Existing terramechanics-based dynamic models for tracked vehicles (TRVs) are widely used in dynamics analysis. However, these models are incompatible with model-based controller design due to their high complexity and computational costs. This study presents a novel and simplified terramechanics-based dynamic model for TRVs that can be used in optimization-based real-time motion controller design. To this end, we approximated the track-ground interactions with an averaged term of the track-ground shear stresses to make the model computationally efficient and linearizable. By introducing the concepts of slip ratio and slip angle in the field of wheeled vehicles, the terramechanics-based dynamic model was finally simplified into a compact and practical single-track dynamic model reducing the demand for precise slip ratio measurements. The single-track model enables us to design an efficient motion control scheme by considering lateral and longitudinal dynamics separately. Finally, the proposed dynamic model was verified and validated under various road conditions using a real TRV. Additionally, the performance of different models was compared in simulation as an example to demonstrate that the proposed model outperforms the existing ones in TRV path-following tasks. IEEE

关键词： Model predictive control

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Guest Editorial: Introduction to Heterogeneous Robotic Systems in 5G and Beyond Networks

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IEEE Wireless Communications 2023年第2期30卷 64-65页

作者： Lu, Huimin Ho, Pin-Han Khalfi, Bassem Verikoukis, Christos Kyushu Institute of Technology Japan Department of Electrical and Computer Engineering The University of Waterloo Canada Oregon State University CorvallisOR United States University of Patras Department of Computer Engineering & Informatics Greece

Remote intelligence in the application of robotics and the autonomous system relies heavily on seamless wireless connections. The 5G mobile network technology meets traditional manufacturing enterprises' application requirements for wireless networks based on robot transformation and upgrading, robot interconnection, and remote interactive applications in production. However, there exists many challenging 5G communication issues, such as different communication protocols in the system varies with different robots and no dis-ruptive changes to the physical layer.

关键词： Special issues and sections Robots 5G mobile communication Physical layer Autonomous systems Heterogeneous networks

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Implementing a Robust and Scalable Detect-and-Avoid Algorithm for UAV Navigation in ROS 15

Implementing a Robust and Scalable Detect-and-Avoid Algorith...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Meimetis, Dimitrios Daramouskas, Ioannis Lappas, Vaios Perikos, Isidoros Kapoulas, Vaggelis Paraskevas, Michael Nkua Department of Aerospace Science & Technology Athens Greece Computer Technology Institute and Press 'Diophantus' Patras Greece UoP Applied Mechanics Lab Patras Greece UoP Computer Engineering and Informatics Department Patras Greece Computer Technology Institute and Press 'Diophantus' University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350368833

Unmanned aerial vehicles (UAVs) have garnered significant attention from the research community during the last decade, due to their diverse capabilities and potential applications. One of the most critical functions that drones must execute efficiently is navigation in real-world environments. This paper presents a decentralized approach for enabling unmanned aerial vehicles (UAVs) to navigate safely in unknown environments and avoid obstacles. Leveraging the Optimal Reciprocal Collision Avoidance (ORCA) algorithm, implemented in the Robot Operating System (ROS), our method facilitates conflict detection and resolution in 2D environments. Through simulations using ROS, Gazebo, and Iris drones, we validate the effectiveness of our approach in scenarios with initial trajectory conflicts. Our work addresses the pressing need for UAVs to autonomously plan and execute safe flights, laying the groundwork for enhanced UAV capabilities in various real-world applications. The simulation results demonstrate the efficiency and robustness of our approach. © 2024 IEEE.

关键词： Drones

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