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One Dimensional Conv-BiLSTM Network with Attention Mechanism for IoT Intrusion Detection

computers, Materials & Continua 2023年第12期77卷 3765-3781页

作者： Bauyrzhan Omarov Zhuldyz Sailaukyzy Alfiya Bigaliyeva Adilzhan Kereyev Lyazat Naizabayeva Aigul Dautbayeva Department of Information Systems Al-Farabi Kazakh National UniversityAlmatyKazakhstan Department of Information Technology and Security Abylkas Saginov Karaganda Technical UniversityKaragandaKazakhstan Department of Computer Science and Information Technology K.Zhubanov Aktobe Regional UniversityAktobeKazakhstan Department of Information Systems International Information Technology UniversityAlmatyKazakhstan Department of Computer Science Korkyt Ata Kyzylorda State UniversityKyzylordaKazakhstan

In the face of escalating intricacy and heterogeneity within Internet of Things(IoT)network landscapes,the imperative for adept intrusion detection techniques has never been more *** paper delineates a pioneering deep learning-based intrusion detection model:the One Dimensional Convolutional Neural Networks(1D-CNN)and Bidirectional Long Short-Term Memory(BiLSTM)Network(Conv-BiLSTM)augmented with an Attention *** primary objective of this research is to engineer a sophisticated model proficient in discerning the nuanced patterns and temporal dependencies quintessential to IoT network traffic data,thereby facilitating the precise categorization of a myriad of intrusion ***:The proposed model amal-gamates the potent attributes of 1D convolutional neural networks,bidirectional long short-term memory layers,and attention mechanisms to bolster the efficacy and resilience of IoT intrusion detection systems.A rigorous assessment was executed employing an expansive dataset that mirrors the convolutions and multifariousness characteristic of genuine IoT network settings,encompassing various network traffic paradigms and intrusion ***:The empirical evidence underscores the paramountcy of the One Dimensional Conv-BiLSTM Network with Attention Mechanism,which exhibits a marked superiority over conventional machine learning ***,the model registers an exemplary AUC-ROC metric of 0.995,underscoring its precision in typifying a spectrum of intrusions within IoT ***:The presented One Dimensional Conv-BiLSTM Network armed with an Attention Mechanism stands out as a robust and trustworthy vanguard against IoT network *** prowess in discerning intricate traffic patterns and inherent temporal dependencies transcends that of traditional machine learning *** commendable diagnostic accuracy manifested in this study advocates for its tangible *** investigation indubitably a

关键词： Intrusion detection attention deep learning IoT CNN BiLSTM

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A Language Framework for Measuring Semantic and Syntactic Similarity for Arabic Texts

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SN computer Science 2024年第4期5卷 351页

作者： Ismail, Shimaa Alsammak, AbdelWahab Elshishtawy, Tarek Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Benha 13511 Egypt Computer Systems Department Faculty of Engineering Shoubra Benha University Benha Egypt

A language framework for determining the similarity of two snipped texts is proposed. The edit distance concept is employed as a frame algorithm to capture syntactic and semantic similarities. In the proposed work, syntax level distances between lemma-form words are calculated, while partial edit costs are allowed to embed semantic similarity measurements. Many knowledge resources have been used, such as words’ synonyms, negation rules, and word semantic spaces. A researchable Arabic thesaurus dictionary is built in two forms, surface form and lemma form. Semantic word spaces are generated from one of the word embedding models, which represents the words in vector spaces. The algorithm is enhanced to overcome problems with different word orders between sentences by a word permutation technique that elects the best alignment of the snipped text words to yield the best matching score. The algorithm also studied the effect of negation words on textual similarity. The proposed approach was implemented to find the similarity between Arabic language texts. Results are compared with other state-of-the-art algorithms using two benchmark datasets. The experimental results show that the proposed approach achieves a higher Pearson correlation coefficient compared to other works. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Arabic text similarity Lexical similarity Negation effect Permutation feature Semantic similarity Word embedding

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A Comprehensive Review on Deep Learning Techniques on Cyber Attacks on Cyber Physical systems

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SN computer Science 2024年第7期5卷 891页

作者： Sagar, Maloth Vanmathi, C. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore 632014 India

Cyber-Physical systems (CPSs), which integrate control, computing, and communication, are considered as next-generation intelligent systems.A major concern in CPS is to ensure security. If security is not ensured, then it leads to both property damage and *** CPS is closely associated with real life, it is a hot research area in present security research. Nevertheless, cyber security issues are also raised. More efforts are still required for understanding CPS security from a more comprehensive perspective even though there are several studies on CPS *** identify and detect cyber security in CPS, numerous methods have been applied. The majority of recently published studies focused on Machine Learning (ML) techniques, with little attention paid to the development of Deep Learning (DL). Hence, the analysis of cyber security on CPS utilizing several algorithms of DL techniques is systematically *** appropriate cyber security risk levels and their corresponding detection and mitigation processesare determined. This study could augment the adaptability of the system of CPS data. Also, it could increase the generalization of the models of security attacks in CPS. Using DL algorithms is strongly recommended by most authors as a means to identify cyber security issues in *** CNN-centric LSTM, Deep Multi-architectural Approach, and DL-Meta classifier attained the highest accuracy in the detection rate of cyber security attacks in CPS when analogized with the other algorithms in DL techniques. Hence, for several critical systems in CPSs, the result renders clear guidelines to apply other regularization on DL techniques. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： CPS Cyber security Cyber-attack Cyber-attack detection Deep learning Risk analysis Security threats

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5G Resource Allocation Using Feature Selection and Greylag Goose Optimization Algorithm

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computers, Materials & Continua 2024年第7期80卷 1179-1201页

作者： Amel Ali Alhussan S.K.Towfek Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Science and Intelligent Systems Research Center BlacksburgVA 24060USA

In the contemporary world of highly efficient technological development,fifth-generation technology(5G)is seen as a vital step forward with theoretical maximum download speeds of up to twenty gigabits per second(Gbps).As far as the current implementations are concerned,they are at the level of slightly below 1 Gbps,but this allowed a great leap forward from fourth generation technology(4G),as well as enabling significantly reduced latency,making 5G an absolute necessity for applications such as gaming,virtual conferencing,and other interactive electronic *** of this change are not limited to connectivity alone;it urges operators to refine their business strategies and offers users better and improved digital *** essential factor is optimization and the application of artificial intelligence throughout the general arrangement of intricate and detailed 5G *** Binary Greylag Goose Optimization(bGGO)to achieve a significant reduction in the feature set while maintaining or improving model performance,leading to more efficient and effective 5G network management,and Greylag Goose Optimization(GGO)increases the efficiency of the machine ***,the model performs and yields more accurate *** work proposes a new method to schedule the resources in the next generation,5G,based on a feature selection using GGO and a regression model that is an ensemble of K-Nearest Neighbors(KNN),Gradient Boosting,and Extra Trees *** ensemble model shows better prediction performance with the coefficient of determination R squared value equal *** proposed framework is supported by several Statistical analyses,such as theWilcoxon signed-rank *** of the benefits of this study are the introduction of new efficient optimization algorithms,the selection of features and more reliable ensemble models which improve the efficiency of 5G technology.

关键词： Optimization ensemble learning 5G technology artificial intelligence greylag goose optimization ANOVA test

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A multi-modal deep learning system for Arabic emotion recognition

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International Journal of Speech Technology 2023年第1期26卷 123-139页

作者： Abu Shaqra, Ftoon Duwairi, Rehab Al-Ayyoub, Mahmoud Department of Computer Information Systems Jordan University of Science and Technology Irbid Jordan Department of Computer Science Jordan University of Science and Technology Irbid Jordan

Emotion analysis is divided into emotion detection, where the system detects if there is an emotional state, and emotion recognition where the system identifies the label of the emotion. In this paper, we provide a multimodal system for emotion detection and recognition using Arabic dataset. We evaluated the performance of both audio and visual data as a unimodal system, then, we exposed the impact of integrating the information sources into one model. We examined the effect of gender identification on the performance. Our results show that identifying speaker’s gender beforehand increases the performance of emotion recognition especially for the models that rely on audio data. Comparing the audio-based system with the visual-based system demonstrates that each model performs better for a specific emotional label. 70% of the angry labels were predicted correctly using the audio model while this percentage was less using the visual model (63%). The accuracy obtained for the surprise class was (40.6%) using the audio model while it was (56.2%) using the visual model. The combination of both modalities improves accuracy. Our final result for the multimodal system was (75%) for the emotion detection task and (60.11%) for emotion recognition task and these results are among the top results achieved in this field and the first which focus on Arabic content. Specifically, the novelty of this work is expressed by exploiting deep learning and multimodal models in emotion analysis and applying it on a natural audio and video dataset for Arabic speaking persons. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Emotion Recognition

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Evaluating the Efficacy of Latent Variables in Mitigating Data Poisoning Attacks in the Context of Bayesian Networks:An Empirical Study

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computer Modeling in Engineering & Sciences 2024年第5期139卷 1635-1654页

作者： Shahad Alzahrani Hatim Alsuwat Emad Alsuwat Independent Researcher Specializing in Data Security and Privacy Taif26571Saudi Arabia Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah24382Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif26571Saudi Arabia

Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among ***,the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data *** of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks,wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their *** this research paper,we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning *** framework utilizes latent variables to quantify the amount of belief between every two nodes in each causal model over *** use our innovative methodology to tackle an important issue with data poisoning assaults in the context of Bayesian *** regard to four different forms of data poisoning attacks,we specifically aim to strengthen the security and dependability of Bayesian network structure learning techniques,such as the PC *** doing this,we explore the complexity of this area and offer workablemethods for identifying and reducing these sneaky ***,our research investigates one particular use case,the“Visit to Asia Network.”The practical consequences of using uncertainty as a way to spot cases of data poisoning are explored in this inquiry,which is of utmost *** results demonstrate the promising efficacy of latent variables in detecting and mitigating the threat of data poisoning ***,our proposed latent-based framework proves to be sensitive in detecting malicious data poisoning attacks in the context of stream data.

关键词： Bayesian networks data poisoning attacks latent variables structure learning algorithms adversarial attacks

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Enhancing connectivity and coverage in wireless sensor networks: a hybrid comprehensive learning-Fick’s algorithm with particle swarm optimization for router node placement

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Neural Computing and Applications 2024年第34期36卷 21671-21702页

作者： Amer, Dina A. Soliman, Sarah A. Hassan, Asmaa F. Zamel, Amr A. Computer Science Department Higher Technological Institute Tenth of Ramadan44228 Egypt Management Information Systems Department Faculty of Management Economics and Business Technology Egyptian Russian University Badr11829 Egypt Computer and Systems Engineering Department Faculty of Engineering Zagazig University Zagazig44519 Egypt

Wireless Sensor Networks (WSNs) are essential for collecting and transmitting data in modern applications that rely on data, where effective network connectivity and coverage are crucial. The optimal placement of router nodes within WSNs is a fundamental challenge that significantly impacts network performance and reliability. Researchers have explored various approaches using metaheuristic algorithms to address these challenges and optimize WSN performance. This paper introduces a new hybrid algorithm, CFL-PSO, based on combining an enhanced Fick’s Law algorithm with comprehensive learning and Particle Swarm Optimization (PSO). CFL-PSO exploits the strengths of these techniques to strike a balance between network connectivity and coverage, ultimately enhancing the overall performance of WSNs. We evaluate the performance of CFL-PSO by benchmarking it against nine established algorithms, including the conventional Fick’s law algorithm (FLA), Sine Cosine Algorithm (SCA), Multi-Verse Optimizer (MVO), Salp Swarm Optimization (SSO), War Strategy Optimization (WSO), Harris Hawk Optimization (HHO), African Vultures Optimization Algorithm (AVOA), Capuchin Search Algorithm (CapSA), Tunicate Swarm Algorithm (TSA), and PSO. The algorithm’s performance is extensively evaluated using 23 benchmark functions to assess its effectiveness in handling various optimization scenarios. Additionally, its performance on WSN router node placement is compared against the other methods, demonstrating its competitiveness in achieving optimal solutions. These analyses reveal that CFL-PSO outperforms the other algorithms in terms of network connectivity, client coverage, and convergence speed. To further validate CFL-PSO’s effectiveness, experimental studies were conducted using different numbers of clients, routers, deployment areas, and transmission ranges. The findings affirm the effectiveness of CFL-PSO as it consistently delivers favorable optimization results when compared to existing meth

关键词： Particle swarm optimization (PSO)

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Effective Traffic Rerouting for Video Streaming in Software Defined Networking

Effective Traffic Rerouting for Video Streaming in Software ...

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2023 IEEE Conference on computer Applications, ICCA 2023

作者： Su, Hla Myo Maw, Aung Htein University of Computer Studies Faculty of Computer Systems and Technologies Yangon Yangon Myanmar University of Information Technology Faculty of Computer Systems and Technologies Yangon Myanmar

ISBN: (纸本)9781665435994

Video streaming exceeds all other traffic types on the internet. Now, it occupies a significant portion of total internet traffic. The transmission mechanism used by the video stream affects not only network traffic but also the users' Quality of Experience (QoE). Software Defined Networking (SDN) is a manageable, programmable, adaptable, and cost-effective emerging network architecture, which is very suitable for the dynamic natured high-bandwidth of modern applications. The OpenFlow protocol is a fundamental element in the creation of SDN solutions. Traffic engineering is a favorite mechanism used by network administrators when optimizing the existing networks and providing more services to customers is needed. This paper proposes a traffic rerouting approach in the video streaming flow management framework on SDN architecture, which can effectively reroute video streaming traffics to reduce packet loss and improve bit rates. It can calculate the current link utilization over the network links and it uses least-cost path rerouting rather than the default routing, reactive forwarding in the ONOS controller. The proposed method is validated by using the popular Mininet network emulation environment and OpenFlow features. The experimental results verify that the improvement of performance by the measurement of video streaming bit rates and packet loss of video traffic. © 2023 IEEE.

关键词： Video streaming

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Decentralized control in active distribution grids via supervised and reinforcement learning

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Energy and AI 2024年第2期16卷 137-149页

作者： Stavros Karagiannopoulos Petros Aristidou Gabriela Hug Audun Botterud Laboratory for Information and Decision Systems(LIDS) Massachusetts Institute of TechnologyCambridgeUSA EEH-Power Systems Laboratory ETH Zurichhysikstrasse 38092 ZurichSwitzerland Department of Electrical Engineering Computer Engineering&InformaticsCyprus

While moving towards a low-carbon, sustainable electricity system, distribution networks are expected to host a large share of distributed generators, such as photovoltaic units and wind turbines. These inverter-based resources are intermittent, but also controllable, and are expected to amplify the role of distribution networks together with other distributed energy resources, such as storage systems and controllable loads. The available control methods for these resources are typically categorized based on the available communication network into centralized, distributed, and decentralized or local. Standard local schemes are typically inefficient, whereas centralized approaches show implementation and cost concerns. This paper focuses on optimized decentralized control of distributed generators via supervised and reinforcement learning. We present existing state-of-the-art decentralized control schemes based on supervised learning, propose a new reinforcement learning scheme based on deep deterministic policy gradient, and compare the behavior of both decentralized and centralized methods in terms of computational effort, scalability, privacy awareness, ability to consider constraints, and overall optimality. We evaluate the performance of the examined schemes on a benchmark European low voltage test system. The results show that both supervised learning and reinforcement learning schemes effectively mitigate the operational issues faced by the distribution network.

关键词： Supervised learning Reinforcement learning Deep deterministic policy gradient Decentralized control Active distribution systems

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Intelligent Machine Learning Enabled Retinal Blood Vessel Segmentation and Classification

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computers, Materials & Continua 2023年第1期74卷 399-414页

作者： Nora Abdullah Alkhaldi Hanan T.Halawani Department of Computer Science College of Computer Science and Information TechnologyKing Faisal UniversityP.O.Box 400AlAhsa31982Saudi Arabia College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia

Automated segmentation of blood vessels in retinal fundus images is essential for medical image *** segmentation of retinal vessels is assumed to be essential to the progress of the decision support system for initial analysis and treatment of retinal *** article develops a new Grasshopper Optimization with Fuzzy Edge Detection based Retinal Blood Vessel Segmentation and Classification(GOFED-RBVSC)*** proposed GOFED-RBVSC model initially employs contrast enhancement ***,GOAFED approach is employed to detect the edges in the retinal fundus images in which the use of GOA adjusts the membership *** ORB(Oriented FAST and Rotated BRIEF)feature extractor is exploited to generate feature ***,Improved Conditional Variational Auto Encoder(ICAVE)is utilized for retinal image classification,shows the novelty of the *** performance validation of the GOFEDRBVSC model is tested using benchmark dataset,and the comparative study highlighted the betterment of the GOFED-RBVSC model over the recent approaches.

关键词： Edge detection blood vessel segmentation retinal fundus images image classification deep learning

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