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Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization

computers, Materials & Continua 2023年第2期74卷 2677-2693页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Manal Abdullah Alohali Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Information Systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of *** computing and fog computing,two of the most common technologies used in IoT applications,have led to major security *** are on the rise as a result of the usage of these technologies since present security measures are *** artificial intelligence(AI)based security solutions,such as intrusion detection systems(IDS),have been proposed in recent *** technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection(FS)techniques to increase classification accuracy by minimizing the number of features *** the other hand,metaheuristic optimization algorithms have been widely used in feature selection in recent *** this paper,we proposed a hybrid optimization algorithm for feature selection in *** proposed algorithm is based on grey wolf(GW),and dipper throated optimization(DTO)algorithms and is referred to as *** proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better *** the employed IoT-IDS dataset,the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in 2678 CMC,2023,vol.74,no.2 the literature to validate its *** addition,a statistical analysis is performed to assess the stability and effectiveness of the proposed *** results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.

关键词： Feature selection grey wolf optimization dipper throated optimization intrusion detection internet-of-things(IoT)

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Leveraging Machine Learning for More Efficient Real-Time Data Analysis 3

Leveraging Machine Learning for More Efficient Real-Time Dat...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Rajan, T. Surulivel Chavan, Gurunath T. Kumar, Ritesh Saini, Rajat Rajesh, A. Doda, Devendra Kumar Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science Engineering Faculty of Engineering and Technology Karnataka Bangalore India Vivekananda Global University Department of Electrical Engineering Jaipur India

ISBN: (纸本)9798350319125

The present-day state of real-time statistics analysis, in the main, relies on guide evaluation techniques that require trained records scientists as a way to extract significant insights from statistics streams. Leveraging machine-gaining knowledge of (ML) methods provides the capacity for increasing the efficiency and accuracy of real-time statistics analysis. This paper presents an automation process for data analysis, utilizing machine learning algorithms to improve the efficiency and accuracy of real-time data analysis. The proposed process involves data preprocessing, feature selection, algorithm selection, model training, and validation, all of which can be automated to reduce human intervention and errors. Additionally, the paper highlights the benefits of incorporating machine learning in the data analysis process, such as fast and accurate predictions, increased scalability, and reduced costs. The results obtained from implementing this automation process demonstrate its effectiveness in achieving more efficient real-time data analysis. Ultimately, this study provides valuable insights for organizations looking to leverage machine learning for better data analysis and decision-making. we present numerous use case studies illustrating current successes in leveraging ML for more efficient actual-time records evaluation. © 2023 IEEE.

关键词： Deploying Evaluation Leveraging Particular Successes

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A Note on the Frequency Characteristics of Discrete Systems in the Complex Plane

A Note on the Frequency Characteristics of Discrete Systems ...

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2022 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2022

作者： Matousek, Radomil Svarc, Ivan Faculty of Mechanical Engineering Institute of Automation and Computer Science Brno University of Technology Brno Czech Republic

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

Frequency characteristics of discrete-time systems in the complex plane and in linear coordinates are used to design controllers (correction terms) by frequency methods, to determine stability by frequency stability criteria, to assess the dynamic properties and behavior of the discrete system with respect to the harmonic components of the input signal, and to design various filters. Therefore, their determination is of considerable importance and their mapping and compilation into comprehensive summaries from which their shape and waveform can be determined is not yet done for discrete systems. This part is the first aim of the paper. Furthermore, a comparison of the construction of frequency characteristics of continuous and discrete linear time-invariant (LTI) systems is made and their differences are highlighted. Also, a comparison is made between the shape and waveform of the frequency characteristics of continuous and discrete systems with formally similar transfer functions. This comparison showed a relatively high similarity, which could be used to determine the dynamical properties of these systems. From the comparison of the constructions can also be seen how difficult it is to construct discrete versus continuous characteristics. © 2022 IEEE.

关键词： Stability criteria

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Predicting Power Consumption Anomaly Using Statistical and Supervised Machine Learning Techniques 6

Predicting Power Consumption Anomaly Using Statistical and S...

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6th IEEE Information technology and Mechatronics engineering Conference, ITOEC 2022

作者： Tan, Yi-Fei Tan, Wooi-Nee El-Hadad, Rawan Buhari, Abudhahir Faculty of Engineering Multimedia University Persiaran Multimedia Selangor Cyberjaya63100 Malaysia Faculty of Engineering Science and Technology Infrastructure University Department of Computer Science and Networking Kuala Lumpur Malaysia

ISBN: (纸本)9781665431859

Power has become an essential element of daily life in the modern world. At the same time, over usage of electricity may lead to excessive power consumption, causing the device to short circuit or be on fire. Therefore, it is crucial to monitor and forecast the anomalies in power consumption to avoid any tragedy. In this paper, the authors proposed a method of predicting anomalies in power consumption. The proposed method uses a statistical approach in labeling;the labeled power consumption data are then used to form the data instances. Later, supervised machine learning classification techniques, namely Support Vector Machine, Decision Tree, and Random Forest, are implemented on the data instances to predict the power consumption anomalies. The experimental results demonstrate that the precision, recall, and F1 score are achieving better results when the training dataset is larger. Besides that, the results show that the techniques used to handle the imbalanced data will affect the performance of models. © 2022 IEEE.

关键词： Electric power utilization

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Effectiveness of Brief Mindfulness-based Intervention among Students with High Neuroticism based on Peripheral Physiological Data: A Preliminary Study 5

Effectiveness of Brief Mindfulness-based Intervention among ...

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5th IEEE International Symposium in Robotics and Manufacturing Automation, ROMA 2022

作者： Nik Rashidi, Nik Amalia Nadirah Binti Izhar, Lila Iznita Sebastian, Patrick Elamvazuthi, Irraivan Al Idris, Syahril Anuar Universiti Teknologi Petronas Electrical and Electronics Engineering Perak Malaysia University Malaysia of Computer Science and Engineering Faculty of Engineering and Frontier Technology Selangor Cyberjaya Malaysia

ISBN: (数字)9781665459327

ISBN: (纸本)9781665459327

Stress is a major problem among university students due to the circumstances from internal as well as external such as relationship problems, low self-confidence, and academic pressure. We administered self-report questionnaires and acquired peripheral physiological signals to observe the effectiveness of a brief mindfulness-based intervention (MI) in improving relaxation and thus reducing stress. It was found that skin conductance (SC) showed significant change with p=0.0016 after MI unlike blood volume pulse (BVP) and respiratory rate (RR), even though both were found to be slightly better than that before MI during the resting states and Stroop Task. SC can thus be a potential indicator for the effectiveness of MI in helping the student subjects to cope with stress better and to improve their relaxation and attention levels. © 2022 IEEE.

关键词： Students

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An Enhanced Semantic Analysis of Potential Recommender Systems for Big Data Management 3

An Enhanced Semantic Analysis of Potential Recommender Syste...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Mishra, Rahul Pund, Sachin S. Dewangan, Omprakash Chandrakar, Ruchi Khunte, Nuresh Kr Ammisetty, Veeraswamy Kalinga University Naya Raipur India Shri Ramdeobaba College of Engineering and Management Department of Industrial Engineering Maharashtra Nagpur India Kalinga University Raipur Department of Computer Science and Information Technology Chhattisgarh India Kalinga University Department Faculty of Technology Chattishgarh Naya Raipur India Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering Vaddeswaram India

ISBN: (纸本)9798350319125

this paper proposes a more desirable semantic analysis for capacity recommender structures for large facts management. The proposed approach is primarily based on ontologies, text-mining, and semantic relatedness processes. The proposed technique uses a site particular ontology to perceive relationships among exceptional objects of the domain, as well as semantically and syntactically analyzing text from literature. The goals of the proposed technique are to: (I) identify the relationships among special objects (places, sports, and attendances;(ii) perceive the effect of each courting on the advice performance;(iii) and discover feasible styles from the relationships. to evaluate the proposed method, the effects are in comparison with current methods. The comparison results display that the proposed technique plays higher than other strategies in both efficiency and accuracy. Moreover, the proposed approach can be applied to other domains, supplying new opportunities in recommendation gadget layout. © 2023 IEEE.

关键词： opportunities recommendation relationships strategies

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Educational FinTech: Promoting Stakeholder Confidence Through Automatic Incidence Resolution

Educational FinTech: Promoting Stakeholder Confidence Throug...

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作者： Nwankwo, Wilson Chinedu, Paschal U. Daniel, Aliu Shaba, Saliu Mohammed Muyideen, Momoh Omuya Nwankwo, Chukwuemeka P. Adigwe, Wilfred Oghoriodo, Duke Uwadia, Francis Department of Computer Science Edo State University Uzairue Uzairue Nigeria Department of Computer Engineering Edo State University Uzairue Uzairue Nigeria Federal College of Freshwater Fisheries Technology New Bussa Nigeria Faculty of Air Engineering Air Force Institute of Technology Kaduna Kaduna Nigeria Department of Computer Science Delta University of Science and Technology Ozoro Nigeria Department of Cyber Security Delta University of Science and Technology Ozoro Nigeria

Though FinTech solutions have the potentials to increase efficiency and convenience with education-related payments and remittances in developing countries, it is noted that tens of thousands of stakeholders are still hesitant on using electronic channels due to high transaction failure rates, lack of transparency, and the extensive effort and time stakeholders expend in getting failed transactions reconciled especially when such transactions are false positives that is, the transaction appears to have failed yet the bank account of the payer is debited. This paper is intended to provide a fine-grained model for remedying these problems. In this paper, we address the challenges of false positives in FinTech transactions. We adopt a case study approach complemented by the famous object-based systems development methodology. We demonstrate an improvement on a traditional multi-channel FinTech solution earlier implemented in a public University. The improvement is made by a smart integration of an algorithm that keep the histories of every action of the payer during the payment process as well as the intermediate processes across the application programming interfaces (APIs) used during electronic data interchange (EDI). Whenever there is a false positive, the user/payer can remedy this by using a verification button which queries all the records in the transaction workflow and updates the true status of the payer on the institutional FinTech platform. By this action, the payer’s usual need to seek human support on reconciliation of false positive transactions from the FinTech operator, client support, or the payer’s bank, is eliminated. We present part of a Tertiary Education Enterprise Resource Planning System (TEERPS), an end-to-end solution for all academic and administrative workflows including financial transaction processing. We observed the system in a production environment over several months and the results have shown that the integration of a reverse verifi

关键词： E-learning

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An Investigation Through Stochastic Procedures for Solving the Fractional Order computer Virus Propagation Mathematical Model with Kill Signals

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Neural Processing Letters 2023年第2期55卷 1783-1797页

作者： Sabir, Zulqurnain Raja, Muhammad Asif Zahoor Mumtaz, Nadia Fathurrochman, Irwan Sadat, R. Ali, Mohamed R. Department of Mathematics and Statistics Hazara University Mansehra Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Department of Computer Science Iqra University Islamabad Campus Islamabad Karachi Pakistan Department of Islamic Educational Management Institute Agama Islam Negeri Curup Rejang Lebong Indonesia Department of Mathematics Zagazig Faculty of Engineering Zagazig University Zagazig Egypt Faculty of Engineering and Technology Future University Cairo Egypt

In this study, the numerical investigations through the stochastic procedures for solving a class of fractional order (FO) computer virus propagation (CVP) mathematical model with kill signals (KS), i.e., CVP-KS is presented. The KS gets alert about those viruses, which can be infected through the computer system to decrease the virus propagation danger. The mathematical model of the CVP-KS is based on the SEIR-KS model. The focus of these investigations is to present the numerical solutions of the FO-SEIR-KS model using the sense of Levenberg–Marquardt backpropagation scheme (LMBS) together with the neural networks (NNs), i.e., LMBS-NNs. The use of the one dynamic of the other makes the model nonlinear. Three different FO values have been used to check the performances of the designed scheme for this FO-SEIR-KS nonlinear mathematical model. The statics used in this study is 80%, 10% and 10% for training, testing and certification for solving the FO-SEIR-KS nonlinear mathematical model. The numerical simulations are performed through the stochastic LMBS-NNs scheme for solving the FO-SEIR-KS nonlinear mathematical model. The obtained results will be compared with the design of database reference solutions based on the Adams–Bashforth–Moulton. In order to accomplish the validity, capability, consistency, competence and accuracy of the LMBS-NNs, the numerical results using the error histograms, regression, mean square error, state transitions and correlation have been provided. © 2022, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Mean square error

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Optimizing Energy Efficiency in 5G Networks: Integrating Multi-Packet Reception with Reinforcement Learning and Genetic Algorithms 8

Optimizing Energy Efficiency in 5G Networks: Integrating Mul...

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8th IEEE International Conference on Computational System and Information technology for Sustainable Solutions, CSITSS 2024

作者： Arun, V. Gowtham, A. Arul, U. Angeljulie, J. School of Computing Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Kattankulathur India Madanapalle Andhra Pradesh Annamayya India Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India Karpagam Academy of Higher Education Department of Cse Tamilnadu Coimbatore India

ISBN: (纸本)9798331505462

The rapidly expanding data demand and the explosion of connected devices require energy-efficient answers in 5G networks. This study proposes a three-pronged solution that integrates Multi-Packet Reception (MPR), Reinforcement Learning (RL), and Genetic Algorithms (GA) to improve energy efficiency in 5G networks. MPR allows base stations to process incoming data more efficiently by receiving multiple packets at once. This not only helps the base station 'talk' more efficiently with the devices that are supposed to be connected to it at any moment (which obviously is more inefficient under 5G, the way that MPR can help be more efficient, makes it the prime candidate for meeting the challenge at hand) but also allows the incoming data to be processed at the same rate it could under the old conditions of processing one packet at a time. We use Genetic Algorithms to directly tackle what we consider the core problems of the 5G system: how to efficiently (jointly) serve users and how to efficiently allocate power among the serving base stations to minimize power consumption without sacrificing service quality (QoS). We developed an integrated method that demonstrates how to simultaneously achieve three goals: (1) significant energy savings;(2) improved network performance;and (3) enhanced sustainability under 5G conditions.... We assume a base station [BS] model close to real-world. Based on this model, we simulate our method in several scenarios. In these scenarios, we validate the network performance gains, energy savings, and enhanced sustainability that we design our method to achieve. © 2024 IEEE.

关键词： Base stations

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Incentivizing Recycling Behavior: Repro - A Reward Based CNN-Enabled Framework for Smart Waste Collection

Incentivizing Recycling Behavior: Repro - A Reward Based CNN...

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engineering & Computing Technologies (ICECT), International Conference on

作者： Haider Irfan Usama Arshad Hammad Anwer Faculty of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Topi Pakistan

ISBN: (数字)9798350349719

ISBN: (纸本)9798350349726

Efficient waste management together with certain measures to promote the recycling process are essential for over-coming global environmental issues. In this study, we describe Repro an app, which is targeted at transforming how we collect waste and recycle by integrating CNN technology and a reward-based system. Users, with the Repro app, take pictures of any trash they discard and rely on the underlying CNN model to identify the type of trash correctly. If a user's trash accumulation reaches beyond a given limit the system sends a notification to the trash collectors via the riders app to direct for a timely waste collection. User tokens are provided to users after trash collection, which can be redeemed within the Repro app for various product items and coupons. Moreover, this motivating approach encourages responsible waste management which goes along with the development of environmental custodianship among users. We describe the technical implementation of the CNN-based framework, assess the performance of the level of waste classification accuracy, and predict the behavioral implications of the reward system on recycling behavior. The study illustrates that Repro is a scalable solution for smarter waste collection and recycling endeavors, consequently creating a more eco-minded and sustainable environment.

关键词： Waste management Waste materials Technological innovation Accuracy Neural networks Recycling Task analysis

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