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Deep Neural Network Based Real-Time Intrusion Detection System

SN computer science 2022年第2期3卷 145页

作者： Thirimanne, Sharuka Promodya Jayawardana, Lasitha Yasakethu, Lasith Liyanaarachchi, Pushpika Hewage, Chaminda Faculty of Engineering Sri Lanka Technological Campus Padukka Sri Lanka Faculty of Engineering University of Peradeniya Peradeniya Sri Lanka Department of Computer Science Cardiff Metropolitan University Cardiff United Kingdom

In recent years, due to the rapid growth in network technology, numerous types of intrusions have been uncovered that differ from the existing ones, and the conventional firewalls with specific rule sets and policies are incapable of identifying those intrusions in real-time. Therefore, that demands the requirement of a real-time intrusion detection system (RT-IDS). The ultimate purpose of this research is to construct an RT-IDS capable of identifying intrusions by analysing the inbound and outbound network data in real-time. The proposed system consists of a deep neural network (DNN) trained using 28 features of the NSL-KDD dataset. In addition, it contains the machine learning (ML) pipeline with sequential components for categorical data encoding and feature scaling, which is used before transmitting the real-time data to the trained DNN model to make predictions. Moreover, a real-time feature extractor, which is a C++ program that sniffs data from the real-time network traffic and derives relevant data related to the features of the NSL-KDD dataset using the sniffed data, is deployed between the gateway router and the local area network (LAN). Together with the trained DNN model, the ML pipeline is hosted in a server that can be accessed via a representational state transfer application programming interface (REST API). The DNN has revealed outstanding testing performance results achieving 81%, 96%, 70% and 81% for accuracy, precision, recall and f1-score accordingly. This research comprises a comprehensive technical explanation concerning the implementation and functionality of the complete system. Moreover, leveraging the extensive explanations provided in this paper, advanced IDSs capable of identifying modern intrusions can be constructed. © The Author(s) 2022.

关键词： Deep neural network Machine learning Pipelines Real-time intrusion detection system

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Functional 3D-Printed Finger Prosthesis with Compliant Fingertips for Support of iADLs

Functional 3D-Printed Finger Prosthesis with Compliant Finge...

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

作者： Wallace, Lankha Hassan, Modar Shimizu, Yukiyo Kikuchi, Akira Suzuki, Kenji School of Science and Engineering University of Tsukuba Tsukuba Japan Institute of Systems and Information Engineering University of Tsukuba Tsukuba Japan University of Tsukuba Faculty of Medicine Department of Rehabilitation Medicine Tsukuba Japan University of Tsukuba Tsukuba Plant-Innovation Research Center Tsukuba Japan

ISBN: (纸本)9781665410205

We propose a method for fabricating custom passive finger prostheses with compliant fingertips. The prosthetic devices detailed in this study are intended to mimic the compliance of biological fingertips: improving the capacity and comfort of keyboard typing, the manipulation of small objects, and instrumental activities of daily living in general. The proposed method utilizes 3D scanning, 3D printing, and computer-aided design to realize the compliant fingertips feature and to create custom-fit prostheses. We present the clinical application of the proposed prostheses on two end users and the evaluation results of typing speed and gross finger dexterity. The results showed successful fitting of the developed prostheses on both participants. Performance tests showed comparable performance in typing speed and accuracy with and without prostheses, and improved comfort with the prostheses. Gross finger dexterity and pinch force did not show improvement with the prostheses. The participants were able to utilize all fingers in keyboard typing and other iADLs instead of only the intact fingers, which indicates the potential for further functional improvement after habituation to the prostheses. In addition to improved comfort, the participants also reported reduced pain in sensitive sites, and aesthetic satisfaction with the prostheses. © 2024 IEEE.

关键词： computer keyboard substitutes (disability aids)

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Sorting the Digital Stream: Big Data-driven Insights into Email Classification for Spam and Ham Detection

Sorting the Digital Stream: Big Data-driven Insights into Em...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Shah, Syed Attique Arputham, Emil Anthony Ahmed, Awais Farah, Mohamed Ben Shah, Attal Aziz, Abdul Birmingham City University STEAMhouse School of Computing and Digital Technology BirminghamB47RQ United Kingdom University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan China Faculty of Ict Buitems Department of Computer Engineering Quetta Pakistan Universidad de Zaragoza Aragon Institute of Engineering Research Zaragoza50018 Spain

ISBN: (纸本)9798350324457

In contemporary email communication, the ever- expanding volume of digital correspondence has ushered in an era where big data plays a pivotal role in addressing the challenge of distinguishing between legitimate (ham) and unsolicited (spam) emails. The primary objective of this paper is the meticulous identification and establishment of criteria for the discrimination between ham and spam emails. To achieve this, the study harnesses data from three distinct datasets, aiming to identify common attributes shared across all emails, irrespective of their classification, while concurrently devising methodologies for precise spam detection. Central to this endeavor is the evaluation of the effectiveness of feature selection techniques, specifically Chi-Square and Pearson Correlation, in elevating the accuracy of email classification. The investigation extends to assessing how the combination of these feature selection techniques with the broader machine learning framework can be optimized. This optimization entails the application of diverse preprocessing techniques to the datasets, all designed to amplify the precision of email classification. Furthermore, this research scrutinizes the performance evaluation metrics employed in the assessment of email classifiers. By conducting comprehensive experiments, the study identifies optimal classifiers based on rigorous evaluation metrics. This contributes valuable insights to the toolkit of techniques for proficient email classification within the realm of big data analysis. © 2023 IEEE.

关键词： Artificial Neural Network Classification Data Pre-processing Decision Tree Email Spam Detection Feature Selection Machine Learning Naive Bayes

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An Overview of Telemedicine: Evolution, Application, Challenges and Future Direction

An Overview of Telemedicine: Evolution, Application, Challen...

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Electronics and Renewable systems (ICEARS), International Conference on

作者： Dhanshri Kawde Palash Gourshettiwar Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Telemedicine is of great importance as it increases the availability of health care for people living in remote or undeveloped areas. It reduces the cost of health care, allows for early diagnosis and treatment of chronic conditions, and saves time for both patients and healthcare providers. Telemedicine also ensures continuity of care in situations like pandemics while reducing reducing the need for face-to-face consultations can reduce the chance of catching contagious diseases.. This innovation will combine wearable devices and artificial intelligence with personalized care and mental health support. This way, healthcare services would be available and efficient for everyone around the world. However, technological challenges include those stemming from poor system architecture, poor connectivity limitation in diagnosis from a physical examination privacy issues among others and diverse legal regulations. Lack of access and integration into mainstream health systems are also among the causes of discontinuities in care. Overcoming such barriers is critical, and telemedicine needs to be easy to integrate into mainstream frameworks for better access, equity, and health outcome.

关键词： Technological innovation Telemedicine systems architecture Medical services Regulation Electronic healthcare Biomedical monitoring Artificial intelligence Wearable devices Diseases

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Modeling and Simulation of Two Axes Gimbal Using Fuzzy control

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computers, Materials & Continua 2022年第7期72卷 93-107页

作者： Ayman A.Aly Mohamed O.Elhabib Bassem F.Felemban B.Saleh Dac-Nhuong Le Department of Mechanical Engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia Control and Mechatronics Division School of Electrical EngineeringUTMJohor Bahru81300Malaysia Institute of Research and Development Duy Tan UniversityDanang550000Vietnam School of Computer Science Duy Tan UniversityDanang550000Vietnam

The application of the guided missile seeker is to provide stability to the sensor’s line of sight toward a target by isolating it from the missile motion and *** main objective of this paper is not only to present the physical modeling of two axes gimbal system but also to improve its performance through using fuzzy logic controlling *** paper is started by deriving the mathematical model for gimbals motion using Newton’s second law,followed by designing the mechanical parts of model using SOLIDWORKS and converted to xml file to connect dc motors and sensors using MATLAB/***,a Mamdani-type fuzzy and a Proportional-Integral-Derivative(PID)controllers were designed using MATLAB *** performance of both controllers was evaluated and tested for different types of input *** simulation results showed that self-tuning fuzzy controller provides better performance,since no overshoot,small steady-state error and small settling time compared to PID controller.

关键词： Gimbal system self-tuning fuzzy proportional-integral-derivative(PID)control cross coupling

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Comparison between alternative droop control strategy, modified droop method and control algorithm technique for parallel-connected converters

AIMS Electronics and Electrical Engineering

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AIMS Electronics and Electrical engineering 2021年第1期5卷 1-23页

作者： Shebani, Muamer M. Tariq Iqbal, M. Quaicoe, John E. Department of Electrical and Computer Engineering Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John’sNL Canada

Most of the active current sharing methods are based on a communication network. The communication link is also used with the improved droop control methods to achieve a precise load current sharing and regulate the voltage at the common DC bus. Conversely, the conventional droop method that is considered a decentralized method becomes more attractive for controlling parallel-connected converters in DC microgrids. The conventional droop methods’ main drawbacks are associated with the unequal load current sharing and voltage deviation at the common DC bus. In this paper, the modified droop method as a conventional droop method is augmented with a virtual droop and adaptive voltage control gains to improve the load current sharing and the voltage regulation, respectively. In contrast with other improved droop approaches, the control approach proposed in the paper does not require a communication link to exchange information between parallel modules. Instead, it uses the converters’ theoretical load regulation characteristics to estimate the voltage set point for each converter locally. The proposed virtual resistive gain manipulates the modified droop method to regulate each module’s droop gain, which ensures equal current sharing. The proposed method also eliminates the tradeoff between current sharing difference and voltage regulation by implementing the adaptive voltage control, which compares the estimated voltage at the point of common coupling with the rated bus value and adjusts the droop gains based on the compared values to ensure a constant voltage at various load conditions. The load current sharing and voltage restoration improvements of the proposed method versus the modified droop method and the control algorithm technique are observed in this paper. The proposed method’s effectiveness is demonstrated by MATLAB/Simulink simulation and validated by an experimental prototype. © 2021 the Author(s), licensee AIMS Press.

关键词： DC-DC converters

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LUMOS-DM: Landscape-Based Multimodal Scene Retrieval Enhanced by Diffusion Model 30th

LUMOS-DM: Landscape-Based Multimodal Scene Retrieval Enhance...

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Huynh, Viet-Tham Nguyen, Trong-Thuan Nguyen, Quang-Thuc Tran, Mai-Khiem Nguyen, Tam V. Tran, Minh-Triet Software Engineering Laboratory and Faculty of Information Technology University of Science VNU-HCM Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam Department of Computer Science University of Dayton Dayton United States

ISBN: (纸本)9783031533013

Information retrieval is vital in our daily lives, with applications ranging from job searches to academic research. In today’s data-driven world, efficient and accurate retrieval systems are crucial. Our research focuses on video data, using a system called LUMOS-DM: Landscape-based Multimodal Scene Retrieval Enhanced by Diffusion Model. This system leverages Vision Transformer and Diffusion Models, taking user-generated sketch images and text queries as input to generate images for video retrieval. Initial testing on a dataset of 100 h of global landscape videos achieved an 18.78% at Top-20 accuracy rate and 36.45% at Top-100 accuracy rate. Additionally, video retrieval has various applications, including generating data for advertising and marketing. We use a multi-modal approach, combining sketch and text descriptions to enhance video content retrieval, catering to a wide range of user needs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Diffusion

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Augmented Node Placement Model in t-WSN Through Multiobjective Approach

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computers, Materials & Continua 2021年第12期69卷 3629-3644页

作者： Kalaipriyan Thirugnansambandam Debnath Bhattacharyya Jaroslav Frnda Dinesh Kumar Anguraj Jan Nedoma School of Computer Science and Engineering VIT UniversityChennai CampusTamilnaduIndia Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramGunturIndia Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and CommunicationsUniversity of Zilina01026 ZilinaSlovakia Department of Telecommunications Faculty of Electrical Engineering and Computer ScienceVSB-Technical University of Ostrava70833 Ostrava-PorubaCzech Republic

In Wireless Sensor Network(WSN),coverage and connectivity are the vital challenges in the target-based *** linear objective is to find the positions to cover the complete target nodes and connectivity between each sensor for data forwarding towards the base station given a grid with target points and a potential sensor placement *** this paper,a multiobjective problem on target-based WSN(t-WSN)is derived,which minimizes the number of deployed nodes,and maximizes the cost of coverage and sensing *** Evolutionary-based Non-Dominated Sorting Genetic Algorithm-II(NSGA-II)is incorporated to tackle this multiobjective problem *** problems are intended to solve different objectives of a problem ***-inspired algorithms address the NP-hard problem most effectively in recent *** NSGA-II,the Non-Dominated sorting preserves the better solution in different objectives simultaneously using dominance *** the diversity maintenance phase,density estimation and crowd comparison are the two components that balance the exploration and exploitation phase of the *** of NSGA-II on this multiobjective problem is evaluated in terms of performance indicators Overall Non-dominated Vector Generation(ONGV)and Spacing(SP).The simulation results show the proposed method performs outperforms the existing algorithms in different aspects of the model.

关键词： Focused wireless sensor network m-coverage k-connectivity problem non-dominated sorting NSGA-II

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Performance Evaluation of Oversampling Methods on Deep Learning-Based Skin Cancer Classification

Performance Evaluation of Oversampling Methods on Deep Learn...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Supriyanto, Catur Salam, Abu Zeniarja, Junta Wijaya, Adi Universitas Dian Nuswantoro Bachelor Program in Informatics Engineering Faculty of Computer Science Semarang Indonesia Universitas Indonesia Maju Department of Health Information Management Jakarta Indonesia

ISBN: (纸本)9798350339215

The field of dermatology faces considerable challenges when it comes to early detection of skin cancer. Our study focused on using different datasets, including original data, augmented data, and SMOTE oversampled data, to identify skin cancer. Our dataset consisted of images of skin lesions from the MNIST Skin Cancer dataset (HAM 10000), including samples of both cancerous and benign cases in the dataset. We employed data augmentation to expand the dataset's size and increase the diversity of skin lesion features. Furthermore, to tackle class imbalance in the dataset, we applied the SMOTE oversampling technique to generate synthetic samples for the under-represented group. With the original, augmented, and SMOTE oversampled datasets, we trained a Convolutional Neural Network (CNN) model. The performance of the model was evaluated using accuracy, recall, precision, and F1-score. The comparison between the results obtained from the original data, augmented data, and SMOTE oversampling data clearly revealed distinctions in performance. Our findings clearly demonstrate that employing data augmentation and SMOTE oversampling can significantly enhance the efficacy of skin cancer detection. © 2023 IEEE.

关键词： Neural network models

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Improved Alzheimer's Disease Early Prediction using Machine Learning Method : A Comprehensive Diagnostic Approach with Random Forest Classifier

Improved Alzheimer's Disease Early Prediction using Machine ...

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Soft Computing for Security Applications (ICSCSA), International Conference on

作者： Madiha Munawar Monika Singh T Kishor Kumar Reddy C Marlia Mohd Hanafiah Department of Computer Science and Engineering Stanley College of Engineering and Technology for Women Hyderabad India Faculty of Science and Technology Universiti Kebangsaan Malaysia Malaysia

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Alzheimer's Disease (AD) often affects the elder persons and is the prevalent kind of Dementia. AD has a huge expense, especially when it comes to the treatment. AD is a main reason the older generations die whereas when Alzheimer Disease is detected early there will be minimal deaths and accurate diagnosing likely occurs in most cases. Using anonymized Oasis longitudinal MRI data, the study trains several machine learning algorithms to identify AD patients. Analyzing the test result includes utilizing different learning strategies or algorithms e.g., Ensemble Random Forest (RF), Hierarchical Decision Tree (DT), Logistic Regression (LR), Convolutional Neural Network (CNN), Deep Neural Network binary classifier (DNN), soft voting classifier (SVC), transfer learning approaches (TLA), Support Vector Machine (SVM) and boosting AdaBoost in reference of their Accuracy, Error Rate, AUC (area under the curve), Time Taken and Recall for each method. The obtained results concluded that the proposed Random Forest classifier achieves higher accuracy (92.84%), Error Rate (7.16%), AUC (95%) (Area Under Curve) and Recall (89%). The Random Forest classifier also achieves higher Time Taken that is 483.654 sec.

关键词： Support vector machines Deep learning Machine learning algorithms Accuracy Error analysis Magnetic resonance imaging Prediction algorithms Classification algorithms Alzheimer's disease Random forests

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