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Intrusion detection and prevention system for an IoT environment

Digital Communications and Networks 2022年第4期8卷 540-551页

作者： Ajay Kumar K.Abhishek M.R.Ghalib A.Shankar X.Cheng Department of Computer Science and Engineering NIT PatnaBiharIndia Faculty of Science Engineering Computing(SEC)De Montfort UniversityDubaiUnited Arab Emirates Department of CSE Amity School of Engineering and TechnologyAmity UniversityUttar PradeshIndia Department of Computer Science Middlesex UniversityLondonUK

Internet of Things(IoT)security is the act of securing IoT devices and *** devices,including industrial machines,smart energy grids,and building automation,are extremely *** the goal of shielding network systems from illegal access in cloud servers and IoT systems,Intrusion Detection systems(IDSs)and Network-based Intrusion Prevention systems(NBIPSs)are proposed in this *** intrusion prevention system is proposed to realize NBIPS to safeguard top to bottom *** proposed NBIPS inspects network activity streams to identify and counteract misuse *** NBIPS is usually located specifically behind a firewall,and it provides a reciprocal layer of investigation that adversely chooses unsafe *** IPS sensors can be installed either in an inline or a passive *** inline sensor is installed to monitor the traffic passing through *** sensors are installed to stop attacks by blocking the traffic using an IoT signature-based protocol.

关键词： IDS and IPS Cloud Firewall Denial of service Smart environment Internet of things NBIPS

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Quick Hidden Layer Size Tuning in ELM for Classification Problems

Mendel

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Mendel 2024年第1期30卷 1-14页

作者： Albtoush, Audi Fernandez-Delgado, Manuel Maarouf, Haitham Al Nawaiseh, Asmaa Jameel Faculty of Computer Science and Information Technology Jerash University Jordan Santiago De Compostela University Spain Software Engineering Department Mut’ah university Jordan

The extreme learning machine is a fast neural network with outstanding performance. However, the selection of an appropriate number of hidden nodes is time-consuming, because training must be run for several values, and this is undesirable for a real-time response. We propose to use moving average, exponential moving average, and divide-and-conquer strategies to reduce the number of training’s required to select this size. Compared with the original, constrained, mixed, sum, and random sum extreme learning machines, the proposed methods achieve a percentage of time reduction up to 98% with equal or better generalization ability. © 2024, Brno University of Technology. All rights reserved.

关键词： Knowledge acquisition

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Dempster-shafer deep capsule attention model (DDCAM)

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Multimedia Tools and Applications 2025年 1-29页

作者： Mehravaran, Zahra Ghanizadeh, Ahmad Navid Hamidzadeh, Javad Harati, Ahad Mashhad Iran Department of Computer Science Saarland University Saarbrücken Germany Faculty of Computer Engineering and Information Technology Sadjad University Mashhad Iran

A capsule neural network faces significant challenges in achieving high accuracy on complex datasets due to its high computational complexity and limited ability to represent features. To overcome these limitations, this paper proposes a novel framework, Dempster-Shafer Deep Capsule Attention Model (DDCAM), that combines 3D convolution with attention mechanisms to improve feature representation. This model utilizes the Dempster-Shafer Theory to manage uncertainty and optimize feature selection, resulting in more reliable and accurate learning. In this architecture, dense capsule blocks are used to improve gradient flow and mitigate the effects of vanishing gradients. The DDCAM consistently outperforms state-of-the-art capsule networks on a wide range of datasets, especially complex datasets such as MNIST, CIFAR10, SVHN, and Fashion MNIST. DDCAM improves accuracy by 1.29% on CIFAR10 and 0.76% on SVHN compared to existing models, while achieving precision improvements of 1.22% and 1.08% on CIFAR10 and SVHN, respectively. Additionally, experiments conducted on the AffNIST dataset demonstrate the robustness of DDCAM in handling affine transformations, where it achieved significantly higher accuracy compared to existing models. These results illustrate the model’s enhanced ability to capture complex data relationships and improve both accuracy and precision, advancing capsule neural network performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Affine transforms

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Harnessing Long Short Term Memory Networks for Stock Market Forecasts

Harnessing Long Short Term Memory Networks for Stock Market ...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Faheem, Alishba Arshad, Usama Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Faculty of Computer Science and Engineering Department of Artificial Intelligence Topi Pakistan

ISBN: (纸本)9798350349719

Stock market trend prediction is a classical re-gression problem with effects on stock exchange. This research is presenting a detailed exploration of how Long Short Term Memory (LSTM) networks may be used for predicting stock market trends. The LSTM model was employed instead of the conventional Recurrent Neural Networks (RNNs), to learn from historical financial data. The study examines the operations of the LSTM model and the optimization of hyper parameters that lead to accurate prediction outcomes from the given dataset. Our model gave encouraging results that underlined the LSTM model's prowess in capturing intricate temporal dependencies in data. The model's result were in a low Root Mean Square Error (RMSE) score and an impressively high accuracy level that were good indicators of the model's adaptability at trend identification. Our study amplifies the need for advanced machine learning algorithms like LSTM that increase prediction accuracy while offering insights into decision-making processes for both investors and financial analysts at the same time. Further, it also hints at the potential of deep learning within the stock market. The paper concludes by discussions on all possible improvements to the existing model, future directions, and how incorporating additional data types can increase our predictive accuracy. This research primarily underscores the significant role LSTM plays in deciphering complex financial data trends accurately. © 2024 IEEE.

关键词： Long short-term memory

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Investigating EEG Connectivity Differences Between Right and Left-Handers 3

Investigating EEG Connectivity Differences Between Right and...

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3rd IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Almabruk, Tahani Adalhamid, Samia Almaghairbe, Rafig Hadoth, Laila Yousef, Aisha Tan, Tele Omar Al-Mukhtar University Faculty of Science Department of Computer Science Elbeida Libya University of Derna Faculty of Science Department of Computer Science Derna Libya Curtin University Faculty of Science and Engineering Department of Mechanical Engineering Perth Australia

ISBN: (纸本)9798350319897

An extensive knowledge of cognitive development from early infancy to late adolescence is required to investigate brain neural pathways. The success in recognizing diverse stimuli is one of the remarkable frameworks that used to examine the essential variations in cognitive processes. Recognizing the intended stimuli has been proven to be influenced by several factors such as participants 'gender and handedness. In this study, we investigated whether these cognitive variations regarding handedness take place at age nine-year. 33-EEG channels were recorded while seven right-handed and seven left-handed participants completed the Eriksen Flanker task-experimental paradigm with a variety of stimuli for evaluating attention and cognition. In each handedness group, the imaginary coherence was computed to evaluate the functional connectivity of each child. It was observed that left and right-handers activate distinct cortical regions, particularly in the alpha and beta bands. Additionally, the findings showed that brain connectivity at the incongruent Flanker stimuli can be utilized to differentiate the two targeted group. © 2023 IEEE.

关键词： coherence Flanker conflict handedness

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Predictive Analysis of Sarajevo's AQI using Machine Learning Models for Varied Data Granularity and Prediction Windows 47

Predictive Analysis of Sarajevo's AQI using Machine Learning...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Zolota, Emina Hasić, Vahidin Mević, Amina Delić, Amra Krivić, Senka University Of Sarajevo Faculty Of Electrical Engineering Department For Computer Science And Informatics Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9798350382495

This study scrutinizes five years of Sarajevo's Air Quality Index (AQI) data using diverse machine learning models - Fourier autoregressive integrated moving average (Fourier ARIMA), Prophet, and Long short-term memory (LSTM) - to forecast AQI levels. Focusing on various prediction frames, we evaluate model performances and identify optimal strategies for different temporal granularities. Our research unveils subtle insights into each model's efficacy, shedding light on their strengths and limitations in predicting AQI across varied timeframes. This research presents a robust framework for automatic optimization of AQI predictions, emphasizing the influence of temporal granularity on prediction accuracy, automatically selecting the most efficient models and parameters. These insights hold significant implications for data-driven decision-making in urban air quality control, paving the way for proactive and targeted interventions to improve air quality in Sarajevo and similar urban environments. © 2024 IEEE.

关键词： Air quality

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Performance Evaluation of Intrusion Detection System with SVM 6

Performance Evaluation of Intrusion Detection System with SV...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Diaz, Ricky Aurelius Nurtanto Putra, I. Ketut Gede Darma Sudarma, Made Sukarsa, I. Made Department of Computer Systems Faculty of Informatics and Computer Institut Teknologi dan Bisnis STIKOM Bali Bali Indonesia Department of Information Technology Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia Department of Electrical Engineering Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia

ISBN: (纸本)9798331539603

Various machine learning techniques have been proposed to improve the effectiveness of Intrusion Detection systems (IDS), where IDS is one of the important parts of the network that functions to maintain network security. With its various capabilities and reputation, Support Vector Machine (SVM) is often applied in various classification cases. However, it has constraints with accuracy and computing time when it comes to data that has large dimensions. Utilizing metaheuristic techniques is one possibility that researchers can use to solve this problem. To overcome this, in this study, we will evaluate the performance of SVM for the IDS case by utilizing the metaheuristic Grey Wolf Optimization (GWO) algorithm. From the experiments with three different iteration models, the best results of the combination of SVM and GWO were found in a model with 10 GWO iterations and 10 populations with an accuracy of 97.47% using 15 features out of 48 original features of the UKM-IDS20 dataset. © 2024 IEEE.

关键词： Network intrusion

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Design of PID Droop Control for Fast Power Sharing in Inverter-Based Microgrid

Design of PID Droop Control for Fast Power Sharing in Invert...

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2024 Energy Conversion Congress and Expo Europe, ECCE Europe 2024

作者： Nazeri, Ahmad Ali Friebe, Jens Zacharias, Peter Faculty of Electrical Engineering Computer Science University of Kassel Department of Power Electronics Kassel Germany

ISBN: (纸本)9798350364446

Microgrids (MGs) serve as central interfaces for distributed generation, predominantly employing voltage source inverters (VSIs). These MGs operate in either autonomous or grid-connected modes, facilitating local power generation and storage. However, autonomous MGs, lacking grid support, are susceptible to instability, particularly with multiple generators sharing power. This paper addresses instability challenges in VSI-based MGs through comprehensive system modeling, analysis, and design. A novel approach is proposed, employing a modified virtual inertia PID droop control with virtual impedance, facilitates swift response to frequency and voltage fluctuations, ensuring precise power allocation among paralleled VSIs. Moreover, this paper presents the small signal analysis of the PID droop control and outlines the secondary control design, encompassing DDSRF-PLL, frequency, and voltage restoration loops. System validation is conducted through MATLAB/Simulink modeling and experimental verification on a hardware-in-the-loop (HiL) testbench, confirming the efficacy of the proposed methodologies in low inertia MGs. © 2024 IEEE.

关键词： MATLAB

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Translating Words to Pixels: An Effective Text-To-Image System Utilizing Upsampling and CLIP Models

Translating Words to Pixels: An Effective Text-To-Image Syst...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Saeed, Nuaima Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Faculty of Computer Science and Engineering Department of Artificial Intelligence Topi Pakistan

ISBN: (纸本)9798350349719

Generative AI and text-based image synthesis are being pursued with more interest than ever and adopt a growing audience. This work offers a new way of producing images within a broad range of quality levels, deducing the extra value of the increase resolution. The system applies sophisticated technologies such as diffusion and upscaling models that are guided by the pre-Training of the Contrastive Language-Image PreTraining (CLIP). However, the model shines primarily in its ability to generate visually cohesive and artistic images which are a good illustration of each specified textual prompt. Upsampling technique resolved the major translation problems by generating more high resolution and detailed resultant images of base images. Beyond that, the system also thoroughly illustrated its diversity by accurately producing a comprehensive collection of images based on the text input. This work would largely be a demonstration of the suitability of it for different fields like digital art, advertising, and content creation. Moreover, it delimits the chances of producing innovative and unique content. Future development of many other applications of the system will include the improvement of the system handling more complex textual descriptions, the addition of user-interactive generation features, the optimization of system performance and expanding to multimodal content generation. Our results here only represent a small but important step in this exciting direction. By means of this survey we enrich further knowledges on the field and its farther issues. © 2024 IEEE.

关键词： Signal sampling

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Efficient Traffic Management For Resolving Traffic Congestion On Motorways

Efficient Traffic Management For Resolving Traffic Congestio...

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Arslan, Muhammad Bakar, Rana Abu Ahad, Abdul University of Sahiwal Department of Computer Science Sahiwal Pakistan Chulalongkorn University Faculty of Engineering Department of Computer Engineering Bangkok Thailand

ISBN: (纸本)9798350347050

Traffic jams on the motorways due to accidents or other emergencies have proven difficult to resolve. The time it takes for the cars to line up and move out of the jam on their own accord is usually rather long. This article presents a system named ETMRTCM, which effectively centralized traffic coordination as a solution for overcoming transitory bottlenecks on multi-lane motorways. At initially, warning alerts regarding the traffic congestion happened depending on Vehicle to Vehicle communications. Then, automobiles start negotiating with their neighbors to figure out the best structure (platoon leader, distance gap, speed, and the number of vehicles) for formulating platoons. As a result, cars can move through the congestion more efficiently. After that, the platoon leaders give orders to the troops to switch lanes if there is an open space in the road adjacent to the one in which they are. The trial findings show that our method may shorten the wait time for the last few cars passing through the congested region by up to 22%. The suggested process also efficiently shortens wait times for vehicles entering the crowded zone while keeping things even for those leaving it. © 2023 IEEE.

关键词： Vehicles

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