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4th International Conference on Distributed Sensing and Intelligent systems, ICDSIS 2023

作者： Darwiesh, Abdelaziz Elhoseny, Mohamed El-Baz, A.H. Nour, Mohamed Abdallah Mathematics Dep Faculty of Science Damietta University New Damietta Egypt Information Systems Dep. College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Computer Science Dep. Faculty of Computers and Artificial Intelligence Damietta University New Damietta Egypt

ISBN: (纸本)9781839539954

This study presents a new approach to predict cryptocurrencies risks based on social media platform indicators. This method can help cryptocurrency investors in their future decision-making. It utilizes advanced techniques such machine learning and natural language processing. Furthermore, a case study on American cryptocurrency investors is provided to help them identify and assess the risks associated with their purchases. Moreover, performance metrics are computed to demonstrate the capabilities of the new approach. © The Institution of Engineering & Technology 2023.

关键词： Adversarial machine learning

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Multi Label Classification Based on Ensemble Learning: Investigative Study

Multi Label Classification Based on Ensemble Learning: Inves...

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information Technology (ACIT)

作者： Ola Nasir Raed Alazaidah Wael Hadi Ghassan Samara Essam Al Daoud Suhaila Abu Owaida Ali Elrashidi Esraa Abu Elsoud Haneen Alzoubi Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Data Science and AI Faculty of Information Technology Zarqa University Zarqa Jordan Department of Cyber Security Faculty of Information Technology Petra University Amman Jordan Department of Computer Science Faculty of Information Technolog Zarqa University Zarqa Jordan Department of Computer Science Faculty of Information Technology Al-al-Bayt University Mafraq Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia Department of Computer Science Faculty of IT Zarqa University Department of Computer Science Faculty of IT Applied Science Private University Amman Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

In the big data technology nowadays, activities associated with Multi-Label Classification (MLC) pose big and complex challenges, receiving terrific interest in diverse fields. Existing MLC algorithms suffer from low overall performance. This paper considers four well-known Problem Transformation Methods (PTMs). Two of these PTMs are based on the frequency of the labels (MFL and LFL) while the last two PTMs are based on the positive correlations among labels. These PTMs have been implemented and compared against four multi label datasets of various sizes to identify the best one that fits the nature of MLC datasets. Moreover, six popular ensemble learning models have been carried out and compared on the four datasets with respect to several evaluation metrics such as Accuracy, Hamming loss, and Exact Match and using SMO, DecisionStump, Random Forest, and Random Commission classifiers. The results revealed that MFL accomplished the quality performance amongst the four PTMs, and Stacking SVM is the best ensemble learning technique.

关键词： Support vector machines Measurement Correlation Accuracy Stacking Multi label classification Big Data Ensemble learning information technology Random forests

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R-IDPS: Real Time SDN-Based IDPS System for IoT Security

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computers, Materials & Continua 2022年第11期73卷 3099-3118页

作者： Noman Mazhar Rosli Saleh Reza Zaba Muhammad Zeeshan M.Muzaffar Hameed Nauman Khan Faculty of Computer Science and Information Technology University of MalayaKuala Lumpur50603Malaysia Centre for Research in Industry 4.0 University of MalayaKuala Lumpur50603Malaysia MIMOS Berhad National Applied R&D CentreKuala Lumpur57000Malaysia School of Electrical Engineering and Computer Science National University of Sciences and TechnologyIslamabad44000Pakistan

The advent of the latest technologies like the Internet of things(IoT)transforms the world from a manual to an automated way of ***,IoT sector open numerous security *** traditional networks,intrusion detection and prevention systems(IDPS)have been the key player in the market to ensure *** challenges to the conventional IDPS are implementation cost,computing power,processing delay,and ***,online machine learning model training has been an *** these challenges still question the IoT network *** has been a lot of research for IoT based detection systems to secure the IoT devices such as centralized and distributed architecture-based detection *** centralized system has issues like a single point of failure and load balancing while distributed system design has scalability and heterogeneity *** this study,we design and develop an agent-based hybrid prevention system based on software-defined networking(SDN)*** system uses lite weight agents with the ability to scaleup for bigger networks and is feasible for heterogeneous IoT *** baseline profile for the IoT devices has been developed by analyzing network flows from all the IoT *** profile helps in extracting IoT device *** features help in the development of our dataset that we use for anomaly *** anomaly detection,support vector machine has been used to detect internet control message protocol(ICMP)flood and transmission control protocol synchronize(TCP SYN)flood *** proposed system based on machine learning model is fully capable of online and offline *** than detection accuracy,the system can fully mitigate the attacks using the software-defined technology SDN *** major goal of the research is to analyze the accuracy of the hybrid agent-based intrusion detection systems as compared to conventional centralized only solutions,especially under the flood attac

关键词： Machine learning Internet of things software defined networking distributed denial of service attacks

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Automated Deep Learning Driven Crop Classification on Hyperspectral Remote Sensing Images

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computers, Materials & Continua 2023年第2期74卷 3167-3181页

作者： Mesfer Al Duhayyim Hadeel Alsolai Siwar Ben Haj Hassine Jaber SAlzahrani Ahmed SSalama Abdelwahed Motwakel Ishfaq Yaseen Abu Sarwar Zamani Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent *** remote sensing contains acquisition of digital images from several narrow,contiguous spectral bands throughout the visible,Thermal Infrared(TIR),Near Infrared(NIR),and Mid-Infrared(MIR)regions of the electromagnetic *** order to the application of agricultural regions,remote sensing approaches are studied and executed to their benefit of continuous and ***,hyperspectral images(HSI)are considered the precise for agriculture as they can offer chemical and physical data on *** this motivation,this article presents a novel Hurricane Optimization Algorithm with Deep Transfer Learning Driven Crop Classification(HOADTL-CC)model onHyperspectralRemote Sensing *** presentedHOADTL-CC model focuses on the identification and categorization of crops on hyperspectral remote sensing *** accomplish this,the presentedHOADTL-CC model involves the design ofHOAwith capsule network(CapsNet)model for generating a set of useful feature ***,Elman neural network(ENN)model is applied to allot proper class labels into the input ***,glowworm swarm optimization(GSO)algorithm is exploited to fine tune the ENNparameters involved in this *** experimental result scrutiny of the HOADTL-CC method can be tested with the help of benchmark dataset and the results are assessed under distinct *** comparative studies stated the enhanced performance of the HOADTL-CC model over recent approaches with maximum accuracy of 99.51%.

关键词： Hyperspectral images remote sensing deep learning hurricane optimization algorithm crop classification parameter tuning

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Human Activity Recognition for People with Knee Abnormality Using Surface Electromyography and Knee Angle Sensors 8

Human Activity Recognition for People with Knee Abnormality ...

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Joint 8th International Conference on Digital Arts, Media and Technology and 6th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2023

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Faculty of Engineering Department of Computer Engineering Nakorn Pathom Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350396546

With the onset of technological advancements, biosensors are being effectively used in a variety of contexts, including the diagnosis of diseases, the promotion of their prevention and rehabilitation, the monitoring of patient health, and human activity recognition (HAR). Recently, HAR research results have been obtained and applied in many commercial applications such as fall detection using smart watch sensors, activity classification using smart home sensors. However, previous HAR models utilizing wearable sensors primarily utilized data from healthy individuals, and such models are frequently inaccurate when applied to individuals with medical mobility impairments. In this work, surface electromyography and goniometer sensors were used to categorize various types of rehabilitation activities based on HAR models developed for individuals with knee abnormalities. To achieve the goal of our study, a deep residual network called ResNeXt was proposed to detect human activities with high performance. To investigate the classification performance of deep learning models, a 5-fold cross-validation technique was applied for both training and testing. Based on the results of our study, the combination of features extracted from the goniometer and electromyograph signals resulted in the highest F1-score (93.31%) and the best accuracy (93.52%). © 2023 IEEE.

关键词： Deep learning

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Towards a User-Friendly Web Application Firewall

Towards a User-Friendly Web Application Firewall

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IEEE International Conference on Intelligent Computing and information systems (ICICIS)

作者： Fatima Omar Dalia Ahmed Omar ElNakib Mohamed Ahmed Nada Farhan Hanan Hindy Mahmoud Abdel-Hamid Yehia Badawi Cyber Security Department Faculty of Computer and Information Science Ain Shams University Cairo Egypt Computer Systems Department Faculty of Computer and Information Science Ain Shams University Cairo Egypt Cyber Security Department Faculty of Computer and Information Science Ain Shams University Giza Egypt Computer Science Department Faculty of Computer and Information Science Ain Shams University Cairo Egypt

Web Application Firewall (WAF) systems play a vital role in protecting web applications from various security vulnerabilities. However, existing WAF solutions are often costly and complex to configure, limiting their accessibility to smaller organizations. This paper aims to develop an affordable and user-friendly WAF framework that mitigates the most common security vulnerabilities identified in the Open Web Application Security Project (OWASP) Top 10. The proposed WAF framework provides protection against attacks such as Cross-Site Scripting (XSS), Structured Query Language Injection (SQLi), Operating System (OS) Command Injection, File Upload, Denial of Service (DoS), Cross Site Request Forgery (CSRF), Path Traversal, and XML External Entity (XXE) attacks. The framework offers an easy-to-use interface with features for easy configuration of mitigation settings. Users can monitor web traffic and receive real-time alerts to quickly identify and respond to potential security threats. Proxy techniques are used to intercept and handle user requests, sanitizing them using the mitigation techniques to prevent the inclusion of malicious code or unauthorized data. Additionally, two machine learning models were implemented. The first model detects malicious requests, and the second model classifies attacks based on their type. Mitigation techniques are employed to filter requests and prevent specific attack types. Regular expressions (Regex) are used to remove malicious code related to XSS, SQL injection, OS Command Injection, File Upload, CSRF, XXE, and Path Traversal attacks. Rate limiting is implemented to mitigate DoS attacks.

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Decision Tree-Based IoT Botnet Attack Detection

Decision Tree-Based IoT Botnet Attack Detection

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Cyber Resilience (ICCR), International Conference on

作者： Hazem Al-Najjar Nadia Al-Rousan Department of Computer Science College of Information Technology University of Petra Amman Jordan Department of Computer Engineering Faculty of Electrical Engineering and Information Systems German Jordanian University

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

This paper investigated the predictive capabilities of three decision tree models for IoT botnet attack prediction using packet information while minimizing the number of predictors. The study employed three decision tree models (C5, CHAID, and Random Forest) and two additional models (Logistic Regression and Bayesian Network) for comparison purposes. The IoT botnet attack dataset comprised various devices, mainly two types of attacks (Gafgyt and Mirai) and 23 feature engineering variables. Simulation results from all models showed that Accuracy, Precision, Recall, and False Omission Rate (FOR) values are approximately one, with an F1 score of around 0.5. CHAID and C5 models outperform other models in predicting IoT botnet attacks, as they are developed using only four and two variables, respectively. These results demonstrated that decision tree models with fewer variables can perform better than models that utilize all predictors.

关键词： Training Logistic regression Botnet Predictive models Bayes methods Internet of Things Decision trees

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Sociotechnical Transformation in the Decade of Healthy Ageing to empower the Silver Economy: Bridging the Silver Divide through Social and Digital Inclusion 24

Sociotechnical Transformation in the Decade of Healthy Agein...

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25th Annual International Conference on Digital Government Research, DGO 2024

作者： Nikiforova, Anastasija Tseng, Hsien-Lee Butt, Sidra Azmat Daheim, Dirk Liu, Li-Chuan University of Tartu Faculty of Science and Technology Institute of Computer Science Tartu Estonia National University of Tainan Department of Public Administration and Management Tainan Taiwan Tallinn University of Technology Information Systems Group Tallinn Estonia National Taitung University Department of Public and Cultural Affairs Taitung Taiwan

ISBN: (纸本)9798400709883

The current decade has been marked as the Decade of Healthy Ageing, where increased participation, social and digital inclusion for the silver population within and for the Silver Economy has become imperative. This panel discusses current activities in the regions where the largest numbers of older people live - Asia and Europe seeking to gain a diverse perspective on current development in the area from two leaders in the field of ICT and digital government – Taiwan and Estonia (with a more diverse view of the Baltic Sea region). The panel discusses the feasibility and effectiveness of these developments, identifies the obstacles these projects face, and examine their limitations. It also explores alternative solutions to address these gaps and the potential for adopting similar projects in other regions, thereby contributing to the guiding principles of"The plan for a Decade of Healthy Ageing 2021-2030" and the Sustainable Development Goals (SDGs). © 2024 Copyright held by the owner/author(s).

关键词： Silver

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Maize Crop Yield Prediction Using Machine Learning Regression Approach 5th

Maize Crop Yield Prediction Using Machine Learning Regressio...

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5th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Bedane, Tarikwa Tesfa Kebede, Kidistie Bizuneh Mohapatra, Sudhir Kumar Das, Tapan Kumar Tripathy, Asis Kumar Department of Software Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia Agricultural Transformation Institute Addis Ababa Ethiopia Faculty of Emerging Technologies Sri Sri University Cuttack India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819735228

Given maize’s significant role as a staple crop, it becomes imperative to carry out precise crop yield prediction to ensure food security. This research employs machine learning algorithms to analyze historical data related to maize crop yields, with the aim of developing a predictive model. The outcome of this study can serve as a valuable tool for pre-harvest planning and measuring crop production per unit of land area. The dataset utilized in this research spans from 2010 to 2021 and was sourced from Ethiopia’s Central Statistical Agency. Three machine learning regression algorithms—Logistic Regression, XGBoost, and Random Forest—were employed to design the predictive model. After data preparation and preprocessing, a total of 436,358 records were used for model development. The models were further trained with reduced features using principal component analysis (PCA). The experimental results demonstrate that the Random Forest algorithm, trained with a reduced number of features through PCA, yielded superior performance, with a lower error rate and a high R2 score of 0.98. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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An Automated Platform for Gathering and Managing Open-Source Cyber Threat Intelligence 2

An Automated Platform for Gathering and Managing Open-Source...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Al-Dmour, Nidal A. Kamrul Hasan, Mohammad Ajmal, Masood Ali, Muhammad Naseer, Iftikhar Ali, Atif Hamadi, Hussam Al Ali, Nasir Mutah University College of Engineering Department of Computer Engineering Jordan Center for Cyber Security Malaysia Riphah International University Islamabad Pakistan University of Central Punjab Pakistan The Superior University Faculty of Computer Science & Information Technology Lahore Pakistan Cyberjaya Malaysia College of Engineering and IT University of Dubai United Arab Emirates School of Computer Science NCBA&E Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan

ISBN: (纸本)9798350335644

The community has begun paying more attention to source OSCTI Cyber Threat Intelligence to stay informed about the rapidly changing cyber threat landscape. Numerous reports from the OSCTI frequently provide information about dangers. However, current OSCTI gathering and management tools have mainly concentrated on individual minor compromise indicators, despite the urgent need for high-quality OSCTI. The relationship between higher-level notions (including the strategies, methods, and processes) and the connections between them, which hold crucial information about dangerous behaviors and are crucial to revealing the full dangerous situation, have been disregarded. Therefore, we present SecurityKG, an automated OSCTI collection and administration system. SecurityKG collects OSCTI to extract high-fidelity knowledge about threat behaviours to address the void. Using a mixture of AI and NLP approaches, a security know-how graph is then constructed from a wide variety of sources. To facilitate knowledge graph exploration, SecurityKG provides a user interface (UI) that supports multiple forms of interactivity. © 2023 IEEE.

关键词： User interfaces

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