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Utilizing Deep Neural Networks to Improve Intrusion Detection System(IDS)

Utilizing Deep Neural Networks to Improve Intrusion Detectio...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Saad, Ahmed Mahmoud Meekay, Mohammed Awad El Sayed, Mostafa Samy Mansoura University Mansour Electronics and Computer and Systems Engineering Dept. Cairo Egypt Mansoura University Mansour Electronics and Communications Dept. Cairo Egypt

ISBN: (纸本)9798350351408

Internet services are now accessible everywhere in the world due to the rapid advancement of networks and technology in recent decades. The development of information security solutions that can recognize novel threats is now critical due to the surge in piracy and the compromising of many modern systems. An intrusion detection system (IDS) is one of the most important information security technologies. It uses machine learning and deep learning algorithms to locate anomalies in the network. The main goal of this paper is to identify the unknown assault package by utilizing a novel intrusion detection method that offers excellent network performance. In this model, two techniques-binary classification and multiclass classification-that make use of a deep neural network algorithm are used to detect attacks. The suggested's outstanding accuracy. © 2024 IEEE.

关键词： Network intrusion

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Enhancing Cybersecurity: A Multilayered Approach to Phishing Website Detection Using Machine Learning

Enhancing Cybersecurity: A Multilayered Approach to Phishing...

引用

2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Lakshmi Narayanaa, T. Skandarsini, R.R. Ida, S. Jhansi Dept. of Computer Science and Business Systems RMK Engineering College Thiruvallur India

ISBN: (纸本)9798350305708

This paper employs machine learning techniques to combat the escalating threat of phishing attacks in the digital realm. The research builds a predictive model capable of differentiating between phishing and legitimate websites by examining a broad range of information retrieved from website URLs, including address bar-based properties, domain characteristics, and webpage content. To ascertain their effectiveness in this task, six well-known machine learning algorithms-Decision Tree, Random Forest, XGBoost, Deep Learning, Autoencoder Neural Network, and Support Vector Machines-are rigorously investigated. Notably, the Multilayer Perceptrons algorithm emerges as the standout performer, achieving an 86.4% accuracy in identifying phishing websites. This endeavor not only advances the field of cybersecurity but also empowers individuals to proactively safeguard themselves against the pervasive threat of phishing attacks in an increasingly interconnected digital landscape. © 2023 IEEE.

关键词： Cybersecurity Dataset Machine learning model Phishing attacks prediction

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Regression Analysis for Predicting Health Insurance

Regression Analysis for Predicting Health Insurance

引用

2023 IEEE International Conference on Communications, Information, Electronic and Energy systems, CIEES 2023

作者： Marinova, Ginka Todorova, Maya Technical University of Varna Dept. of Computer Systems and Technologies Varna Bulgaria Technical University of Varna Dept. of Software and Internet Technology Varna Bulgaria

ISBN: (纸本)9798350336917

In the field of health care, the use of data for medical insurance is a current area of research. In this report, regression models created using machine learning methods and algorithms for health insurance prediction are the object of investigation. A correlation analysis was performed on the input data, and a strong dependence was found for the features BMI and smoker. A comparative analysis was made for twenty-four models constructed using Decision Trees (DT), Support Vector Machine (SVM), Boosted, and Bagged algorithms. To evaluate the model metrics were used the coefficient of determination (R-Squared), Root Mean Square Error (RMSE) and Time for training. From the obtained experimental results, it is found that the model for the BMI feature with the Bagged algorithm has an accuracy of 0.94. The mean squared error for features Smoker and Blood Pressure of models created with the Bagged algorithm is 0.06. Models built with the Support Vector Method (SVM) require more training time than the others do. Algorithms from machine learning and statistical analysis are used to create regression models that can be useful both for health care providers and to improve the services provided. © 2023 IEEE.

关键词： Support vector machines

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Security and Threat Detection through Cloud-Based Wazuh Deployment 1

Security and Threat Detection through Cloud-Based Wazuh Depl...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Moiz, Syed Majid, Abdul Basit, Abdul Ebrahim, Mansoor Abro, Abdul Ahad Naeem, Mehwish Iqra University Dept. of Computer Science Karachi Pakistan Dept. of Cyber Security Karachi Pakistan Indus University Dept. of Computer Science Karachi Pakistan Iqra University Dept. of Software Engineering Karachi Pakistan University Libre de Bruxelles Brussels Belgium

ISBN: (纸本)9798350373042

In today's interconnected business landscape, security is the cornerstone of all organizations, irrespective of their size or industry. This research emphasizes the critical need to safeguard organizational assets from cyber threats and highlights the pivotal role that firewalls play in fortifying network and cyber systems. The challenge lies in maintaining updated firewall defenses to prevent server downtime and the circumvention of security measures. To address these concerns, this study introduces a novel approach by integrating Cloud Wazuh, a user-friendly solution tailored for non-technical personnel, providing streamlined deployment and customizable security rules. Leveraging Wazuh suite of tools, particularly the Host-Based Intrusion Detection System (HIDS), the research detects and mitigates potential threats, including patterns associated with attacks such as Port Scanning, Denial of Service (DoS), and Metasploit. Additionally, Wazuh offers a range of features, from Security Analytics to File Integrity Monitoring, to comprehensively protect against cyber threats. While implementing Cloud Wazuh may entail initial costs, it proves cost-effective compared to maintaining a dedicated IT security team. This research underscores how Cloud Wazuh deployment serves as a pivotal tool for organizations looking to strengthen their security measures, safeguard their data, and ensure uninterrupted operations in an increasingly digital and interconnected world. © 2024 IEEE.

关键词： Cybersecurity

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Evaluation and Validation of Design Metrics in Machine Learning for Depression Detection 18

Evaluation and Validation of Design Metrics in Machine Learn...

引用

18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Ajmal, Sahar Usman, Muhammad Javed, Abqa National University of Computer and Emerging Sciences Dept. of Software Engineering Chiniot Faisalabad Pakistan National University of Computer and Emerging Sciences Dept. of Computer Science Chiniot Faisalabad Pakistan Univ. of Engineering and Technology Dept. of Computer Science Lahore Pakistan

ISBN: (纸本)9798350328172

Depression is a medical syndrome that is often neglected and considered not as significant as physical illness due to lack of awareness and knowledge, but it should be treated in early stages to avoid extreme cases such as suicide. Therefore, Machine Learning Algorithms (MLA) are used to detect depression primitively to avoid extreme hazards. In-ternational Organization for Standardization (ISO) standards are essential for designing metrics in MLA as they ensure global consistency, quality assurance, and objective benchmark, enhancing credibility and reliability of the model. Although there is no proper standard design level quality metric is defined for MLA. Therefore, in this study, ISO 9126 standard is used to make as a quality standard considering functionality parameter of MLA design. These metrics are used to evaluate at the design level of MLA such as evaluation of dataset, preprocessing, MLA, and results. We have provide comparative analysis of MLA with partial functionality compliance with full ISO functionality conformance. In this study, MLA with ISO have significance compliance which is promising, where the raw data added to MLA to get the output and or the other hand all the steps are performed. The results clearly show that proposed model of MLA design metrics are out-performed provides 28% better results in terms of evaluations. Furthermore, the proposed design quality metrics can be used as the standard for the MLA domain. © 2023 IEEE.

关键词： Quality assurance

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Forecasting Unemployment Recession Rates: A Logistic Regression Approach Utilizing Comprehensive Economic Indicators

Forecasting Unemployment Recession Rates: A Logistic Regress...

引用

2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Skandarsini, R.R. Lakshmi Narayanaa, T. Jhansi Ida, S. Dept. of Computer Science and Business Systems RMK ENGINEERING COLLEGE Thiruvallur India

ISBN: (纸本)9798350305708

This paper presents a predictive model using logistic regression to forecast unemployment recession rates, utilizing a range of variables such as GDP, inflation rates, employment rates, and trade activities. The model aims to provide accurate predictions of recession occurrence, utilizing historical data and rigorous cross-validation techniques. The model's significance lies in its potential applications in financial prudence and risk management, enabling job seekers to plan their personal finances and career trajectories, while companies can integrate these insights into strategic planning and supply chain optimization. The model's interpretability provides actionable insights by highlighting the significance and direction of each variable's impact on the predicted recession rate. The paper contributes to a deeper understanding of economic recessions and empowers stakeholders to navigate challenges with resilience and confidence. The model's deployment offers a practical tool for individuals and organizations to make informed choices during economic uncertainties, ultimately fostering prudent and sustainable economic strategies. © 2023 IEEE.

关键词： Economic recession Economic uncertainties Financial prudence GDP Machine learning Predictive model Recession

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Enhancing G.C.E. Ordinary Level Mathematics Through Adaptive Learning in Sri Lanka 6

Enhancing G.C.E. Ordinary Level Mathematics Through Adaptive...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Fernando, P.S.D. Amarasekara, H.P.D.E.R. Rathnakumara, M.D.B. Amarakoon, B.W. Rajapaksha, Samantha Krishara, Jenny Dept. of Information Technology Malabe Sri Lanka Dept. of Computer Systems Engineering Sri Lanka Institute of Information and Technology Malabe Sri Lanka

ISBN: (纸本)9798331517878

Proficiency in mathematics is essential for both academic and professional success, yet many students find the subject difficult, especially when preparing for important exams like Sri Lanka's General Certificate of Education (G.C.E.) Ordinary Level examination. In Sri Lanka, the students show poor pass rates in Ordinary Level mathematics examination. In order to improve mathematics education for O/L students, this research suggests an innovative mobile application that makes use of adaptive learning strategies. This application features components like a comprehensive knowledge bank that is curriculum-mapped, pre-assessment tools for identifying knowledge shortages, a weaknesses based lesson recommendation for personalized lesson suggestions, a custom developed chat assistant to interact with the knowledge bank, interactive 3D visualizations and justifications of complex concepts, and platforms for problem-solving, writing, and time management skill development. The suggested adaptive learning strategy makes use of multimedia tools and datadriven insights to tailor instruction to each student's unique needs, improving math comprehension and readiness for the O/L examination in Sri Lanka. © 2024 IEEE.

关键词： Students

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Deep Learning-Based Image Analysis for Detecting Nutrient Deficiencies of Tomato Plants 5

Deep Learning-Based Image Analysis for Detecting Nutrient De...

引用

5th International Conference on Advancements in Computing, ICAC 2023

作者： Nirasha Jayasiri, K.C. Chandrasiri, Sanjeevi Rupasinghe, Shalini Karunanayake, K.O. Rivindu Uthayachandran, Abineshh Zihara, M.I. Fathima Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Sri Lanka

ISBN: (纸本)9798350358131

Tomato is one of the most popular crops worldwide. The success of a tomato crop is highly dependent on the health of the plants. Nutrient deficiency surveillance is typically conducted through visual inspections, which can be challenging for inexperienced farmers and home gardeners to work accurately. There is a growing need for a quick, reliable, and accurate nutrient deficiency identification system to address this issue. The proposed method is based on image processing and deep learning technologies that are highly effective for image classification tasks. Two models were trained using a dataset collected from tomato plants in Sri Lanka and evaluated using Mask Region-based Convolutional Neural Network (Mask R-CNN) and, You Only Look Once (YOLO) for deficiency classification, and results obtained 92% and 98% accuracy, respectively. Deficiency dispersion level expressed as a percentage using Mask R-CNN and followed by image processing techniques. Overall, this proposed system offers a convenient and accessible tool for farmers and home gardeners to monitor and maintain the health of their tomato plants, enabling them to achieve optimal yields and ensure profitable returns. © 2023 IEEE.

关键词： Object detection

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Towards an integration management maturity 49

Towards an integration management maturity

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49th Euromicro Conference on software engineering and Advanced Applications, SEAA 2023

作者： Hyrynsalmi, Sonja M. Olsson, Helena Holmstrom Bosch, Jan Lut University Dept. Software Engineering Lahti Finland Malmö University Dept. Computer Science and Media Technology Malmö Sweden Chalmers University of Technology Dept. Computer Science and Engineering Göteborg Sweden

ISBN: (纸本)9798350342352

As industries continue to digitize at a rapid pace, the number of integrations, the connections between different digital systems, is increasing. As a consequence, integration management is becoming increasingly difficult, leading to scalability, stability, and system outage problems that directly affect company performance. For that reason the modern integration management platforms have emerged to offer efficient management of the growing number of integrations, as well as a new way to handle, enrich, and utilize data both inside and outside *** research involved interviews with 20 software vendor professionals and 20 clients who had recently undergone an integration platform project. The study identified key drivers behind integration management projects and recognized different maturity levels in integration management. The results revealed that more mature companies are utilizing modern integration platforms to support their digital transformation. The study also highlighted the growing importance of data management and utilization in integration *** address these findings, a maturity model was developed to help companies evaluate their current integration management status and find ways to progress in their integration management. © 2023 IEEE.

关键词： Integration

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Towards a Data Business Maturity Model for software-intensive Embedded System Companies 29

Towards a Data Business Maturity Model for Software-intensiv...

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29th International Conference on engineering, Technology, and Innovation, ICE 2023

作者： Hyrynsalmi, Sami Olsson, Helena Holmström Bosch, Jan Lut University Dept. Software Engineering Lahti Finland Malmö University Dept. Computer Science and Media Technology Malmö Sweden Chalmers University of Technology Dept. Computer Science and Engineering Göteborg Sweden

ISBN: (纸本)9798350315172

Data has been quickly becoming as the fuel, the new oil, of growth and prosperity of companies in the modern age. With useful data and sufficient tools, companies have the ability to enhance their current products, presents new innovations and services as well as generate new revenue streams with a secondary customer base. While there are ongoing efforts to develop machine learning and data science techniques, little attention has been paid to understanding and characterizing data-related business activities in software-intensive *** multiple-case study examines four large international embedded system companies to explore how they are utilizing data and how they have proceeded in their journey in the data business. This study identifies six distinct stages, each with unique challenges, that seems to be common for embedded system companies in their data business. As the result, this study presents an initial data business maturity model for software-intensive embedded system companies. Additionally, this research provides a foundation for future efforts to support software-intensive embedded system companies in establishing data businesses. © 2023 IEEE.

关键词： Embedded systems

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