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Adaptive Marine Predator Optimization Algorithm(AOMA)-Deep Supervised Learning Classification(DSLC)based IDS framework for MANET security

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Intelligent and Converged Networks 2024年第1期5卷 1-18页

作者： M.Sahaya Sheela A.Gnana Soundari Aditya Mudigonda C.Kalpana K.Suresh K.Somasundaram Yousef Farhaoui Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600069India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai 602105India JNIAS School of Planning and Architecture Hyderabad 500034India Department of Computer Science and Engineering NPR College of Engineering and TechnologyNatham Dindigul 624401India Department of Computer Science and Engineering PSNA College of Engineering and TechnologyPoolangulathupatti 620009India Department of Computer Science and Engineering Sri Muthukumaran Institute of TechnologyChennai 600069India Department of Computer Science Moulay Ismail UniversityMeknes 5003Morocco

Due to the dynamic nature and node mobility,assuring the security of Mobile Ad-hoc Networks(MANET)is one of the difficult and challenging tasks *** MANET,the Intrusion Detection system(IDS)is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular *** machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of ***,it still has significant flaws,including increased algorithmic complexity,lower system performance,and a higher rate of ***,the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning ***,the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields,which increases the overall intrusion detection performance of ***,a novel Adaptive Marine Predator Optimization Algorithm(AOMA)is implemented to choose the optimal features for improving the speed and intrusion detection performance of ***,the Deep Supervise Learning Classification(DSLC)mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training *** evaluation,the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.

关键词： Intrusion Detection system(IDS) Security Mobile Ad-hoc Network(MANET) min-max normalization Adaptive Marine Predator Optimization Algorithm(AOMA) Deep Supervise Learning Classification(DSLC)

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Security Assessment Algorithms for Transmission and Services in SDN-Based Mobile Networks 9

Security Assessment Algorithms for Transmission and Services...

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9th International Conference on computer and Communications, ICCC 2023

作者： Zhu, Wei Bao, Chunhui Chen, Tianxiang Zhuang, Yi Jiangsu Automation Research Institute Embedded Computer System Architecture Lianyungang China Nanjing University of Aeronautics and Astronautics Faculty of Computer Science and Technology Nanjing China

ISBN: (纸本)9798350317251

In response to the challenge of comprehensively security assessment and analysis in existing mobile networks from both upper-layer services and network transmission levels, A SDN-based security assessment model for transmission and service security in mobile networks (SAMTS) is proposed. This model introduces security assessment algorithms, in which static configuration information and dynamic abnormal information are considered comprehensively to judge the trend and the risk degree of attack when evaluating the security of network nodes. Meanwhile, the node importance of service orientation and transmission is integrated for security evaluation. The effectiveness of the proposed method is verified by simulation and comparison experiments, demonstrating its higher evaluation accuracy compared with similar algorithms. © 2023 IEEE.

关键词： Wireless networks

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Collaborative Deadline-sensitive Multi-task Offloading in Vehicular-Cloud Networks 17

Collaborative Deadline-sensitive Multi-task Offloading in Ve...

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17th International Conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Kumar, Prasanna Sushma, S.A. Chandrasekaran, K. Addya, Sourav Kanti National Institute of Technology Karnataka Cloud and Smart System Services Lab Department of Computer Science and Engineering Surathkal India Siddaganga Institute of Technology Cloud and Smart System Services Lab Tumakuru India

ISBN: (纸本)9798331531195

With the growing technological advancements in the Internet and advanced functionalities in vehicular networks, it becomes crucial to execute tasks quickly and efficiently. However, the limited onboard computational capacity and vehicle mobility make it challenging to accomplish latency-sensitive tasks efficiently. Task offloading provides a promising solution to overcome these challenges. Cloud data centers provide efficient solutions, but returning the results to the vehicles takes longer due to the large physical distance. Leveraging edge servers to execute latency-sensitive tasks provides a fast, interactive response and less transmission cost. However, in a dynamic network, vehicles will be in constant motion with varying speeds, resulting in frequent handoffs from one base station to another. Our proposed work aims to select the optimal nodes to perform binary offloading with minimum cost using the collaborative vehicular network. We use a greedy-based offloading approach to address these challenges and achieve better quality-of-service and quality-of-experience in a dynamic environment to minimize costs, delay reduction ratio, and satisfaction ratio. The proposed work outperforms the baseline by 60.44%, 53.43% in reducing total system cost, delay reduction ratio, and 36% improvement in the satisfaction ratio compared to baseline algorithms. © 2025 IEEE.

关键词： Quality of service

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An Ensemble Approach for Stroke Prediction 17

An Ensemble Approach for Stroke Prediction

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17th IEEE International Symposium on Embedded Multicore/Many-core systems-on-Chip, MCSoC 2024

作者： Mashi, Boryanka Todorova Hamada, Mohamed Tanimu, Jesse Jeremiah Robert, Patience Samson, Tsentob Joy Bayero University Kano Department of Computer Science Kano Nigeria University of Aizu Information System Division Aizu Japan Federal Polytechnic Bali Department of Computer Science Bali Nigeria Airforce Institute of Tech. Kaduna Department of Computer Science Kaduna Nigeria

ISBN: (纸本)9798331530471

Stroke, a global leading cause of disability and mortality, places a significant burden on healthcare systems and individuals. It accounts for approximately 5 million of total global deaths, affecting millions of people annually. The early identification of stroke symptoms holds paramount significance as it not only facilitates immediate medical attention but also offers valuable insights for predicting the long-term risk of stroke and promoting a healthier life. In this research endeavor, we harness the power of machine learning (ML) to construct a series of robust models aimed at predicting the occurrence of stroke over extended periods. Our primary contribution lies in the formulation of a sophisticated ensemble methodology, which exhibits remarkable performance validated through an array of metrics, including Area Under the Curve (AUC), precision, recall, F-measure, and accuracy. Our experimental findings have unveiled the superior efficacy of the ensemble classification model when compared to alternative methods. In addition to an impressive AUC score of 97.9%, this model exhibits remarkable accuracy, precision, recall and F1-score rates, all standing at a high 97.9%, 98.2%, 97.9%, and 98.0% respectively. These results substantiate the potential of our stacking approach as an exceptional tool for long-term stroke risk prediction and underscore its significance in the realm of stroke research and healthcare. © 2024 IEEE.

关键词： Prediction models

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Handwritten Prescription Recognition Using VGG Based architecture with Bi-LSTM

Handwritten Prescription Recognition Using VGG Based Archite...

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2024 International Research Conference on Smart Computing and systems Engineering, SCSE 2024

作者： Kavinda, Isuru Fernando, Harinda Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer System Engineering Malabe Sri Lanka

ISBN: (纸本)9798350375688

Illegible handwriting on medical prescriptions poses a significant challenge, often leading to the misinterpretation of drug names and dosages. This issue primarily stems from doctors' use of Latin abbreviations, driven by time constraints during patient consultations. The resultant discrepancies in prescription interpretation can potentially expose patients to incorrect medications and dosages, leading to adverse effects. In response to this concern, this study proposes a novel system integrating Convolution Neural Network (CNN) and Recurrent Neural Network (RNN) models. Promising findings are reported in this study, achieving a loss of 0.4874 and an accuracy of 0.83 in the latest trial. The final model offers a potential solution to the persistent problem of illegible prescriptions, enhancing patient safety in the healthcare domain. © 2024 IEEE.

关键词： Long short-term memory

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Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition

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SN computer science 2024年第5期5卷 564页

作者： Duhayyim, Mesfer Al Sharif, Mahir Mohammed Munjal, Muskaan Hilal, Anwer A. Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj 16273 Saudi Arabia Department of Computer Science Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Information System Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan

Sleep quality prediction in Internet of Things (IoT) involves leveraging a system of interrelated devices to gather as well as analyse related data. Smart devices like wearable devices or smart mattresses endlessly monitor many parameters such as body movement, heart rate, and environmental situations in bedroom. Real-time recommendations and feedback transported to consumers, allowing them to create informed lifestyle changes for superior sleep hygiene. As IoT ecosystem continues to develop, a combination of various data streams as well as classy analytics holds assurance for further refining sleep quality forecasts and improving overall well-being. The article designs a Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition (BESHDL-SQP) technique. The purpose of the BESHDL-SQP technique is to recognize and classify the quality of the sleep level in the IoT environment. In the presented BESHDL-SQP technique, the preliminary stage of data pre-processing using min–max normalization involved to normalize the input data. For predicting sleep quality, hierarchical long short-term memory (LSTM) technique exploited. Lastly, BES model utilized for optimal hyperparameter selection of LSTM method which helps in attaining enhanced results. To validate better performance of BESHDL-SQP technique, a huge range of simulation analysis is carried out. The extensive results stated that the BESHDL-SQP technique exhibits supreme performance over other models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bald eagle search Internet of things LSTM Sleep quality recognition Smart devices

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A Hybrid Classification and Identification of Pneumonia Using African Buffalo Optimization and CNN from Chest X-Ray Images

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computer Modeling in Engineering & sciences 2024年第3期138卷 2497-2517页

作者： Nasser Alalwan Ahmed I.Taloba Amr Abozeid Ahmed Ibrahim Alzahrani Ali H.Al-Bayatti Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Information System Department Faculty of Computers and InformationAssiut UniversityAssiutEgypt Mathematics Department Faculty of ScienceAl-Azhar UniversityCairoEgypt Cyber Technology Institute(CTI) De Montfort UniversityLeicesterUK

An illness known as pneumonia causes inflammation in the *** there is so much information available fromvarious X-ray images,diagnosing pneumonia has typically proven *** improve image quality and speed up the diagnosis of pneumonia,numerous approaches have been *** date,several methods have been employed to identify *** Convolutional Neural Network(CNN)has achieved outstanding success in identifying and diagnosing diseases in the fields of medicine and ***,these methods are complex,inefficient,and imprecise to analyze a big number of *** this paper,a new hybrid method for the automatic classification and identification of Pneumonia from chest X-ray images is *** proposed method(ABOCNN)utilized theAfrican BuffaloOptimization(ABO)algorithmto enhanceCNNperformance and *** Weinmed filter is employed for pre-processing to eliminate unwanted noises from chest X-ray images,followed by feature extraction using the Grey Level Co-Occurrence Matrix(GLCM)*** features are then selected from the dataset using the ABO algorithm,and ultimately,high-performance deep learning using the CNN approach is introduced for the classification and identification of *** results on various datasets showed that,when contrasted to other approaches,the ABO-CNN outperforms them all for the classification *** proposed method exhibits superior values like 96.95%,88%,86%,and 86%for accuracy,precision,recall,and F1-score,respectively.

关键词： African buffalo optimization convolutional neural network pneumonia X-ray

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A Comparative Study on Deep Learning Models for Stock Price Prediction 5

A Comparative Study on Deep Learning Models for Stock Price ...

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5th Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2024

作者： He, Zhexin Zhang, Huan Por, Lip Yee Chongqing College of Architecture and Technology Chongqing401331 China Chongqing Academy of Science and Technology Chongqing401123 China Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia

ISBN: (纸本)9781643685243

With the advancement of deep learning technology, researchers have begun employing deep neural network models such as LSTM, MLP, CNN, and GRU to tackle nonlinear prediction problems in stock markets. This study harnesses these deep learning models, including LSTM, MLP, CNN, and GRU, to investigate the nonlinear forecasting of stock market price indices. The experiment selects globally representative stock indices and, through analyzing short-term, mid-term, and long-term data, discovers that the GRU model demonstrates exceptional performance in both prediction accuracy and generalization capabilities. To further enhance precision, the research constructs a filtering ensemble decision-making system based on multiple GRU models. This strategy reduces the error by at least 0.3% in medium to long-term predictions, significantly outperforming a single standalone GRU model. Consequently, the GRU-filtered ensemble decision model has been empirically proven as an effective tool for predicting stock indices, not only broadening the methods available for stock market prediction but also deepening the understanding and application of the generalization abilities of deep learning models within financial markets. © 2024 The Authors.

关键词： Long short-term memory

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Games for Artificial Intelligence Research: A Review and Perspectives

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 5949-5968页

作者： Hu, Chengpeng Zhao, Yunlong Wang, Ziqi Du, Haocheng Liu, Jialin Research Institute of Trustworthy Autonomous System Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China LightSpeed Studios Tencent Shenzhen518057 China

Games have been the perfect test beds for artificial intelligence (AI) research for the characteristics that widely exist in real-world scenarios. Learning and optimization, decision-making in dynamic and uncertain environments, game theory, planning and scheduling, design, and education are common research areas shared between games and real-world problems. Numerous open-source games or game-based environments have been implemented to study AI. In addition to single- or multiplayer, collaborative, and adversarial games, there has also been growing interest in implementing platforms for creative design in recent years. Those platforms provide ideal benchmarks for exploring and comparing AI ideas and techniques. This article reviews the games and game-based platforms for AI research, provides guidance on matching particular types of AI with suitable games for testing and matching particular needs in games with suitable AI techniques, discusses the research trend induced by the evolution of those games and platforms, and gives an outlook. © 2024 IEEE.

关键词： Reinforcement learning

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Taxonomy of Multi-Agent systems Attacks and their Defense Tactics in Certifying Security of Cyber Physical systems

Taxonomy of Multi-Agent Systems Attacks and their Defense Ta...

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2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Jain, Garima Solomon Jebaraj, N.R. Noida Institute of Engineering and Technology Department of Computer Science & Business System Uttar Pradesh Greater Noida India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350344387

The utilization of multi-agent systems has been increasingly prevalent across various sectors, owing to their notable efficacy in execution. However, a multitude of hazards exist that possess the capability to undermine the integrity of the agent and pose a threat to the overall security of the system. Therefore, it is crucial to carefully evaluate and address security vulnerabilities during the process of creating Multi-Agent systems (MAS). This survey assesses different models that aim to guarantee the security of Multi-Agent systems (MAS). These models were developed based on principles related to the agents' functionality and communication. This study offers a comprehensive examination and categorization of the prevailing threats targeting Multi-Agent systems (MASs). Following this, we conduct a comprehensive analysis and assessment of diverse security strategies described in scholarly sources, classifying them into prevention, detection, and resiliency approaches based on their established credibility and reliability. In conclusion, we offer a suggestion for the optimal security approach to address particular types of threats, considering the most recent breakthroughs in the field of study. © 2023 IEEE.

关键词： Attacks Cyber Physical systems Multi Agent systems Resiliency Security

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