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A Novel Hybrid Architecture for Superior IoT Threat Detection through Real IoT Environments

Computers, Materials & Continua 2024年第11期81卷 2299-2316页

作者： Bassam Mohammad Elzaghmouri Yosef Hasan Fayez Jbara Said Elaiwat Nisreen Innab Ahmed Abdelgader Fadol Osman Mohammed Awad Mohammed Ataelfadiel Farah H.Zawaideh Mouiad Fadeil Alawneh Asef Al-Khateeb Marwan Abu-Zanona Department of Computer Science Faculty of Computer Science and Information TechnologyJerash UniversityJerash26150Jordan Computer Engineering Department College of Engineering&Information Technology Buraydah CollegesBuraydah51418Saudi Arabia Faculty of Architecture and Design Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityDiriyahRiyadh13713Saudi Arabia Applied College King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Business Intelligence and Data Analysis Faculty of Financial Sciences and BusinessIrbid National UniversityIrbid21110Jordan Faculty of Information Technology Ajloun National UniversityAjlun26810Jordan Department of Management Information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia

As the Internet of Things(IoT)continues to expand,incorporating a vast array of devices into a digital ecosystem also increases the risk of cyber threats,necessitating robust defense *** paper presents an innovative hybrid deep learning architecture that excels at detecting IoT threats in real-world *** proposed model combines Convolutional Neural Networks(CNN),Bidirectional Long Short-Term Memory(BLSTM),Gated Recurrent Units(GRU),and Attention mechanisms into a cohesive *** integrated structure aims to enhance the detection and classification of complex cyber threats while accommodating the operational constraints of diverse IoT *** evaluated our model using the RT-IoT2022 dataset,which includes various devices,standard operations,and simulated *** research’s significance lies in the comprehensive evaluation metrics,including Cohen Kappa and Matthews Correlation Coefficient(MCC),which underscore the model’s reliability and predictive *** model surpassed traditional machine learning algorithms and the state-of-the-art,achieving over 99.6%precision,recall,F1-score,False Positive Rate(FPR),Detection Time,and accuracy,effectively identifying specific threats such as Message Queuing Telemetry Transport(MQTT)Publish,Denial of Service Synchronize network packet crafting tool(DOS SYN Hping),and Network Mapper Operating System Detection(NMAP OS DETECTION).The experimental analysis reveals a significant improvement over existing detection systems,significantly enhancing IoT security *** our experimental analysis,we have demonstrated a remarkable enhancement in comparison to existing detection systems,which significantly strength-ens the security standards of *** model effectively addresses the need for advanced,dependable,and adaptable security solutions,serving as a symbol of the power of deep learning in strengthening IoT ecosystems amidst the constantly evolving cyber threat *** achievemen

关键词： A hybrid deep learning model IoT threat detection real IoT environments cybersecurity attention mechanism

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Dynamic channel estimation-aware routing protocol in mobile cognitive radio networks for smart IIoT applications

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Digital Communications and Networks 2023年第2期9卷 367-382页

作者： Qusay M.Salih Md Arafatur Rahman A.Taufiq Asyhari Muhammad Kamran Naeem Mohammad Patwary Ryan Alturki Mohammed Abdulaziz Ikram Faculty of Computing Universiti Malaysia Pahang26300GambangPahangMalaysia School of Engineering Computing and Mathematical SciencesUniversity of WolverhamptonUK Data Science Program Monash UniversityIndonesiaand Birmingham City UniversityB47XGUK Department of Information Science College of Computer and Information SystemsUmm Al-Qura UniversitySaudi Arabia Computer Science Department Al Jumoum University CollegeUmm Al-Qura UniversitySaudi Arabia

Cognitive Radio Networks(CRNs)have become a successful platform in recent years for a diverse range of future systems,in particularly,industrial internet of things(IIoT)*** order to provide an efficient connection among IIoT devices,CRNs enhance spectrum utilization by using licensed ***,the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel ***,the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User(PU)activity and create a robust routing *** study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT ***,a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method,namely,Channel Availability ***,a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation *** protocol combines lower layer(Physical layer and data Link layer)sensing that is derived from the channel estimation ***,it periodically updates and stores the routing table for optimal route ***,in order to achieve higher throughput and lower delay,a new routing metric is *** evaluate the performance of the proposed protocol,network simulations have been conducted and also compared to the widely used routing protocols,as a *** simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio(with an improved margin of around 5–20%approximately)under varying numbers of PUs and cognitive users in Mobile Cognitive Radio Networks(MCRNs).Moreover,the cross-layer routing protocol successfully achiev

关键词： Channel selection Cross-layer design Mobile cognitive radio networks Routing protocol IIoT applications

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An Attention-based LSTM price prediction model based on a single time series information 6

An Attention-based LSTM price prediction model based on a si...

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6th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2024

作者： Tian, Jiaming Dang, Yuzhuo Huang, Hongbin National University of Defense Technology Laboratory for Big Data and Decision Changsha China Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9798331516550

With the continuous improvement of people's living standards, people's income from work has gradually increased, and their remaining savings have also gradually increased. In order to avoid the problem of currency devaluation and shrinking savings caused by inflation, investment is regarded as an indispensable asset allocation method. Among investment products, gold has always been favored for its store of value;in the past decade, the cryptocurrency Bitcoin has been limited in number due to its unique technology, causing a new wave of investment in Bitcoin. At present, most of the existing forecasting models are for forecasting stock prices, and there are multiple forecasting models. We used grayscale prediction, time series analysis method and LSTM prediction model based on Attention mechanism to predict and analyze the results of two investment products. The experiment found that the LSTM prediction model based on Attention mechanism had the best prediction performance of the two investment products. © 2024 IEEE.

关键词： Prediction models

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Process Quality Assurance of Artificial Intelligence in Medical Diagnosis 6

Process Quality Assurance of Artificial Intelligence in Medi...

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6th International Conference on Intelligent systems and Computer Vision, ISCV 2024

作者： Moghadasi, Negin Piran, Misagh Valdez, Rupa S. Baek, Stephen Moghaddasi, Negar Polmateer, Thomas L. Lambert, James H. University of Virginia Dept. of Systems and Information Engineering CharlottesvilleVA United States Ruhr University of Bochum Dept. of Radiology Nuclear Medicine and Molecular Imaging Heart and Diabetes Center North-Rhine Westphalia Bad Oeynhausen Germany University of Virginia School of Data Science CharlottesvilleVA United States Western University of Health Sciences Dept. of Dentistry PomonaCA United States CCALS University of Virginia Dept. of Systems and Information Engineering CharlottesvilleVA United States

ISBN: (纸本)9798350350180

While artificial Intelligence (AI) has shown promising results in the healthcare field, it is undeniable that there are risks associated with AI in healthcare that society must acknowledge. This paper presents a comprehensive systems modeling framework aimed at evaluating trust and being responsive to reasons for mistrust and distrust in AI-assisted medical diagnosis, with a specific focus on the diagnosis of cardiac sarcoidosis, utilizing Explainable Artificial Intelligence (XAI) techniques. The design includes two primary sections: 1. Identifying the most and least disruptive scenarios to the system, as well as the most important initiatives for the system. 2. Utilizing XAI techniques such as SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and Anchors models to provide explanations on how machine learning models justify their outcomes. The findings indicate the significance of employing explainable AI in critical domains like healthcare, where lives of patients are at risk. XAI can be employed to analyze the outcomes for AI users, determining the significance of features, improving comprehension of AI outputs, enhancing transparency, explainability, and interpretability of AI outputs, and facilitating data assessment. © 2024 IEEE.

关键词： Anchors Ethical AI Explainable AI LIME Multi-Criteria Decision Analysis Risks of AI SHAP systems engineering

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Average Performance Analysis of Multi-Class Classification Based on Error-Correcting Output Codes

Average Performance Analysis of Multi-Class Classification B...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Kobayashi, Manabu Kumoi, Gendo Yagi, Hideki Hirasawa, Shigeichi Center for Data Science Waseda University Tokyo Japan Nagaoka University of Technology Department of Information and Management Systems Engineering Niigata Japan University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan

ISBN: (纸本)9781665410205

In machine learning, one of the methods to solve multiclass classification problems is a framework called Error-Correcting Output Codes (ECOC), which constructs a multiclass classifier by combining a lot of binary classifiers. ECOC assigns binary codewords to each category, and the multiclass classification performance varies depending on the code. In this study, we treat each element of the codeword as a random variable and evaluate the average performance of ECOC. As a result, for M class classification if the number of binary classifiers is O(M), then the average error probability of various codes approaches that of MAP estimation. We show that the important points are the ratio between the number of binary classifiers and M and the difference between the maximum posterior probability and the second highest posterior probability for the categories. © 2024 IEEE.

关键词： Coding errors

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Joint Routing and Scheduling for In-Vehicle Networks: A Deterministic Perspective

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Wang, Wenxun Wang, Wenyong Yu, Haisheng Wu, Bochun Zou, Sai Ni, Wei Zhang, Ji School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China International Institute of Next Generation Internet Macau University of Science and Technology Macau China State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China College of Big Data and Information Engineering Guizhou University Guiyang China Data61 Business Unit CSIRO Canberra ACT Australia PA Company SC Branch China Telecom Co. LTD Chendu China

Deterministic transmission refers to the guaranteed delivery of data to its destination within a specific time frame along an optimal route, making it an indispensable prerequisite for the operation of autonomous vehicles. The in-vehicle networks (IVNs) face significant challenges in the deterministic transmission of large volumes of perception and control data from and to various vehicle components. In this paper, the cooperative scheduling and routing problem among multi-area control units (ACUs) in IVN is discussed from the view of deterministic transmission, and a ResVmix method is proposed to find the optimal two-layer assignment with ACU cooperation. The multi-cycle queuing and forwarding (multi-CQF) mechanism is employed to schedule the perceived data according to different quality of service (QoS) requirements. The cooperation among multiple ACUs enables deterministic routing of data. The two-layer optimization problems on multi-CQF and ACU are formulated as a+ decentralized partially observable Markov decision processes (Dec-POMDP) and solved using the enhanced multi-agent deep reinforcement learning (MADRL) method. Simulation results demonstrate that ResVmix significantly enhances the deterministic transmission performance of IVN. Compared to traditional shortest path algorithms and state-of-the-art MADRL methods, ResVmix achieves a 22.4% increase in arrival rate and a 20.1% increase in confirmed arrivals, respectively. IEEE

关键词： Reinforcement learning

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ProfileOTMM: Automated Glycan Motif Analysis Through Pattern Recognition of Tree Structures

ProfileOTMM: Automated Glycan Motif Analysis Through Pattern...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Totsuka, Kento Hosoda, Masae Aoki-Kinoshita, Kiyoko F. Shinomiya, Norihiko Graduate School of Science and Engineering Soka University Tokyo Japan Joint Support-Center for Data Science Research Research Organization of Information and Systems Database Center for Life Science Chiba Japan Tokyo Japan

ISBN: (纸本)9798350350821

In recent times, tree-structured data has been rapidly spreading with the advent of the World Wide Web and Bioinformatics. It is employed as a common format for the storage and depiction of data. Glycans, essential substances in all organisms, are biomolecules that play diverse functional roles in the body. They are commonly modeled as tree-structured data and analyzed using machine learning techniques. Nevertheless, more detailed information is imperative to comprehensively understand the biological attributes and functionalities of gly-cans. In this research, we aim to propose a new model called profileOTMM (profile Ordered Tree Markov Model) for the complete automation of glycan analysis. As an initial step, feature engineering was conducted using the OTMM model, which serves as the foundational model for profileOTMM. By improving the accuracy of glycan data input through OTMM, we integrated monosaccharides, the elemental constituents of glycans, along with their linkage types into tree-structured glycan data. This enhanced approach enabled the extraction of biologically meaningful structures with a high degree of precision and accuracy, surpassing the results of previous research efforts. This research will encourage the understanding of glycan structures and their functions. © 2024 IEEE.

关键词： Adversarial machine learning

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An optimal hybrid cascade regional convolutional network for cyberattack detection

An optimal hybrid cascade regional convolutional network for...

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作者： Alqahtani, Ali Khan, Surbhi Bhatia Department of Networks and Communications Engineering College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Data Science School of Science Engineering and Environment University of Salford Salford United Kingdom Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

Cyber-physical systems (CPS) and the Internet of Things (IoT) technologies link urban systems through networks and improve the delivery of quality services to residents. To enhance municipality services, information and communication technologies (ICTs) are integrated with urban systems. However, the large number of sensors in a smart city generates a significant amount of delicate data, like medical records, credit card numerics, and location coordinates, which are transported across a network to data centers for analysis and processing. This makes smart cities vulnerable to cyberattacks because of the resource constraints of their technology infrastructure. Applications for smart cities pose many security challenges, such as zero-day attacks resulting from exploiting weaknesses in various protocols. Therefore, this paper proposes an optimal hybrid transit search-cascade regional convolutional neural network (hybrid TS-Cascade R-CNN) to detect cyberattacks. The proposed model combines the hybrid transit-search approach with the cascade regional convolutional neural network to create an optimal solution for cyberattack detection. The cascade regional convolutional network uses a hybrid transit search algorithm to enhance the effectiveness of cyberattack detection. By integrating these two approaches, the system can leverage both global traffic patterns and local indicators to improve the accuracy of attack detection. During the training process, the proposed model recognizes and classifies malicious input even in the presence of sophisticated attacks. Finally, the experimental analysis is carried out for various attacks based on different metrics. The accuracy rate attained by the proposed approach is 99.2%, which is acceptable according to standards. © 2023 John Wiley & Sons Ltd.

关键词： Zero-day attack

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Gradual Domain Adaptation via Manifold-Constrained Distributionally Robust Optimization 38

Gradual Domain Adaptation via Manifold-Constrained Distribut...

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38th Conference on Neural information Processing systems, NeurIPS 2024

作者： Saberi, Amirhossein Najafi, Amir Behjati, Amin Emrani, Ala Zolfi, Yasaman Shadrooy, Mahdi Motahari, Abolfazl Khalaj, Babak H. Department of Electrical Engineering Sharif University of Technology Tehran Iran Department of Computer Engineering Sharif University of Technology Tehran Iran Sharif Center for Information Systems and Data Science Sharif University of Technology Tehran Iran

The aim of this paper is to address the challenge of gradual domain adaptation within a class of manifold-constrained data distributions. In particular, we consider a sequence of T ≥ 2 data distributions P1, ..., PT undergoing a gradual shift, where each pair of consecutive measures Pi, Pi+1 are close to each other in Wasserstein distance. We have a supervised dataset of size n sampled from P0, while for the subsequent distributions in the sequence, only unlabeled i.i.d. samples are available. Moreover, we assume that all distributions exhibit a known favorable attribute, such as (but not limited to) having intra-class soft/hard margins. In this context, we propose a methodology rooted in Distributionally Robust Optimization (DRO) with an adaptive Wasserstein radius. We theoretically show that this method guarantees the classification error across all Pis can be suitably bounded. Our bounds rely on a newly introduced compatibility measure, which fully characterizes the error propagation dynamics along the sequence. Specifically, for inadequately constrained distributions, the error can exponentially escalate as we progress through the gradual shifts. Conversely, for appropriately constrained distributions, the error can be demonstrated to be linear or even entirely eradicated. We have substantiated our theoretical findings through several experimental results. © 2024 Neural information processing systems foundation. All rights reserved.

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Review of data security within energy blockchain:A comprehensive analysis of storage,management,and utilization

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High-Confidence Computing 2024年第3期4卷 82-102页

作者： Yunhua He Zhihao Zhou Yan Pan Fahui Chong Bin Wu Ke Xiao Hong Li School of Information Science and Technology North China University of TechnologyBeijing 100144China China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China Key Laboratory in Blockchain and Data Security by Ministry of Industry and Information Technology Beijing 100040China Institute of Information Engineering Chinese Academy of Sciences Beijing 100195China

Energy systems are currently undergoing a transformation towards new paradigms characterized by decarbonization,decentralization,democratization,and *** this evolving context,energy blockchain,aiming to enhance efficiency,transparency,and security,emerges as an integrated technological solution designed to address the diverse challenges in this *** security is essential for the reliable and efficient functioning of energy *** pressing need to address challenges related to secure data storage,effective data management,and efficient data utilization is increasingly *** paper offers a comprehensive survey of academic discourse on energy blockchain data security over the past five years,adopting an all-encompassing perspective that spans data storage,management,and *** work systematically evaluates and contrasts the strengths and weaknesses of various research ***,this paper proposes an integrated hierarchical on-chain and off-chain security energy blockchain architecture,specifically designed to meet the complex security requirements of multi-blockchain business ***,this paper identifies key directions for future research,particularly in advancing the integration of storage,management,and utilization of energy blockchain data security.

关键词： Energy blockchain data security Storage Management Utilization

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