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Detection and Classification of DDoS Attacks in Cloud data Using Hybrid LSTM and RNN for Feature Selection

Detection and Classification of DDoS Attacks in Cloud Data U...

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Karthika, R.A. Sriramya, P. Rohini, A. SRMIST Dept. of Computational Intelligence Chennai India Saveetha School of Engineering SIMATS Dept. of Artificial Intelligence and Data Science Chennai India Presidency University Dept. of CSE Bengaluru India

ISBN: (纸本)9798350333244

Cyber threats are the main source of concern in the sector it is deployed. DDoS attacks are at the very top of the list. The rapid growth of cloud migration also expands the attack surface. Different forms of DDoS attacks exist, such as DoS and DDoS etc., There are several existing machine learning algorithms to detect DDoS attacks. There are also many hybrids of machine learning algorithms which are introduced for detecting DDoS attacks to increase the accurateness of detection. Here we use hybrid Long Short-Term Memory Network (LSTM) and Recurrent Neural Network (RNN) for feature selection and deliver an improved detection system. The proposed exploratory model of Multi-layer Perceptron (MLP) classifiers uses deep neural network algorithms. The final outcome shows higher accuracy and precision by dealing with a huge dataset that was gathered. The accuracy and precision of 98.85% and 92 % respectively for grouping in structure. © 2023 IEEE.

关键词： Denial-of-service attack

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VIR2EM: VIrtualization and Remotization for Resilient and Efficient Manufacturing: Project-Dissemination Paper

VIR2EM: VIrtualization and Remotization for Resilient and Ef...

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2023 Forum on Specification and Design Languages, FDL 2023

作者： Beghi, Alessandro Dall'Ora, Nicola Dalle Pezze, Davide Fummi, Franco Masiero, Chiara Spellini, Stefano Susto, Gian Antonio Tosoni, Francesco University of Padova Dept. of Information Engineering Padova Italy University of Verona Dept. of Engineering for Innovation Medicine Italy Statwolf Data Science S.r.l. Padova Italy

ISBN: (纸本)9798350307375

In this paper, we present the project 'VIR2EM: VIrtualization and Remotization for Resilient and Efficient Manufacturing' by providing details on its research themes and its scientific and technological output. The project, centered on virtualization and remotization in the industrial sector, was promoted by Regione Veneto in Italy, and it has seen the participation and collaboration of 3 universities, 1 public research entity, and 10 companies composed of end users of digital solutions and high knowledge-intensive service providers. The project aims to develop and use tools for the virtualization of processes, systems, resources, and remoting of operations in order to: (1) maximize the efficiency of manufacturing systems under normal operating conditions;(2) maintain operations in case of emergency situations;(3) facilitate the restart of operations downstream of emergency situations by ensuring flexibility and predictive capability. Each theoretical proposal has been validated in distinct industrial facilities by constructing ten different prototypes. copy;2023 IEEE. © 2023 IEEE Computer Society. All rights reserved.

关键词： Industry 4.0

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Advancing Android Security: Leveraging Stacking Ensemble and Bioinspired Feature Selection for Efficient Malware Detection 5

Advancing Android Security: Leveraging Stacking Ensemble and...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Jyothsna, V. Mokshitha, Peddesugari Khulud, Shaik Reddy, Lakshmireddy gari Premanath Reddy, Nare Jagannath Pydala, Bhasha Tirupati India Dept. of Information Technology Sree Vidyanikethan Engineering College Tirupati India Department of Data Science Mohan Babu University Tirupati India

ISBN: (纸本)9798350361155

Nowadays, the use of smartphones and related applications is increasing rapidly due to their "easy to use" features, the functionality of many applications, and the constant development of software and hardware of smart devices. Research in this field shows that 4.5 billion people will have mobile phones by 2024. The most used smartphone operating system among these devices is Android. Android accounts for 75.5% of the market. Because Android is so widely used, it is more vulnerable to viruses and malware, making it an ideal target for hackers. Android anti-malware technology, which can quickly detect and classify different malware to create a rapid response plan, has become popular in recent years. The usefulness of combining deep learning and machine learning to offer automated and self-learning services has been shown by numerous applications;nevertheless, the shortage of malware samples has been identified as a barrier to the development of deep learning-based solutions. To solve this problem, this work introduces new machine-learning techniques that can classify malware into different groups and identify malware attacks. The proposed method used the Drebin dataset divided into hazardous and non-hazardous substances. © 2024 IEEE.

关键词： Smartphones

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Expert Patient Interaction Language Model (EPILM)

Expert Patient Interaction Language Model (EPILM)

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Pratham Gala Pooja Vartak Jay Vithlani Naman Chheda Dept. of Computer Science and Engineering (Data Science) Dwarkadas J Sanghvi College of Engineering Mumbai India Dept. of Computer Science and Engineering (Data Science) Mumbai India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Today’s youth encounter numerous mental health challenges, many of which can be effectively addressed through professional intervention. However, there is a notable reluctance among young individuals to seek professional mental health services. The integration of language models in providing mental health support offers a non-intrusive and accessible approach for young people to engage with professional assistance for their mental health concerns. Although existing language models like GPT 3.5, Falcon-7B model have been effective, they also present certain challenges like high dependability of data from sources like Reddit, computational obstacles hindering access, and a preference for certain models over superior alternatives have impeded advancements in this area. that require thorough examination. Issues related to biases and the model’s performance across diverse scenarios underscore the necessity for more intelligent strategies. The proposed method involves enhancing the Falcon-7B model through the application of quantization low rank adaptation (Q-LoRA). This technique reduces the computational requirements and simplifies the accessibility for individuals equipped with the necessary technology while providing the average bertSCORE 85%. By addressing these issues, paper’s objective is to refine mental health support systems to benefit a broader audience.

关键词： Training Adaptation models Quantization (signal) Social networking (online) Computational modeling Graphics processing units Mental health Chatbots data models Tuning

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A Hybrid Machine Learning and Deep Learning Approach for Diabetes Prediction with a Tkinter-Based User Interface

A Hybrid Machine Learning and Deep Learning Approach for Dia...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Kasetty, Sai Bhargav Margret, Issac Neha Madhuri, K. Sai Ojha, Rajesh Kumar Sudha, Nuthalapati Rajakumar, K. V.I.T University SCOPE Tamilnadu Vellore India Data Science BVRIT Narsapur Dept. of CSE Telangana Hyderabad India Silicon University Dept. of Computer Science & Engineering Odisha Bhubaneswar India Mahindra University School of Engineering Telangana Hyderabad India

ISBN: (纸本)9798350369175

Diabetes prediction is a critical task in healthcare that can significantly benefit from advancements in machine learning and deep learning technologies. This paper presents a hybrid approach combining deep learning and traditional machine learning models for diabetes prediction. The proposed system integrates a Tkinter-based Graphical User Interface (GUI) to make it easier for users to interact and make predictions. In our discussion, we will cover the design and implementation of the system, and evaluate the performance of different models using precision, F1 score, recall, and, accuracy to provide insights into their effectiveness. Results demonstrate the efficacy of the hybrid approach, with a focus on model accuracy and user experience. © 2024 IEEE.

关键词： Prediction models

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Unleashing the Potential of Women in Leadership Roles within the Evolving Landscape of Machine Learning and Technology

Unleashing the Potential of Women in Leadership Roles within...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Tiwari, Madhuri Giri, Neeraj Suman, Sanjay Kumar Chandanan, Amit Kumar Peneti, Subhashini Choudhury, Shibanjali GGITS Dept of Data Science MP Jabalpur India IES Institute of Technology and Management IES University Department of Computer Science and Engineering MP Bhopal462044 India St. Martin's Engineering College Dept. of ECE Telangana Secunderabad India Department of Computer Science and Engineering C G Bilaspur India MLR Institute of Technology Dept. of CSE Telangana Hyderabad India Techno College of Engineering Dept. of CSE Tripura Agartala India

ISBN: (纸本)9798331539948

The innovative Gender-Empowered Leadership Development (GELD) programme promotes gender equality in fast-paced technology and machine learning leadership positions. This was a US government initiative. BiasMitigator, Skillenhancer, and CultureCatalyst work together to eliminate gender prejudice, improve employee skills, and improve workplace morale. Programs collaborate to achieve these aims. CultureCatalyst promotes a pleasant environment, SkillEnhancer improves skills over time, and BiasMitigator eliminates gender prejudice across the firm. All three projects assist the organization achieve its aims. This study compares GELD to normal approaches to better understand the situation. This illustrates how intricate its procedure is. © 2024 IEEE.

关键词： BiasMitigator CultureCatalyst GenderEmpowered Leadership Development Inclusion Leadership Development Machine Learning SkillEnhancer Technology Traditional Methods Women in Leadership

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Internet of Animal Health Things (IoAHT) Framework for Clinical Mastitis Detection in Dairy Cows 1

Internet of Animal Health Things (IoAHT) Framework for Clini...

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1st International Conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2023

作者： Ankitha, K. Venugopala, P.S. Kunder, Harish Shetty, Ankitha NMAMIT Nitte Dept. of Artificial Intelligence and Data Science Karkala India Alvas Institute of Engineering and Technology Dept. of Artificial Intelligence and Machine Learning Moodbidri India

ISBN: (纸本)9798350306637

The Earth serves as a shared habitat not just for humans but also for an array of animal species and organisms. Throughout history, animals have played a crucial role in preserving the natural ecosystem balance and meeting diverse human needs. They contribute significantly to various sectors, such as Agriculture, Dairy, Poultry, and animal husbandry, thus bolstering a country's economy. Human responsibility lies in ensuring the well-being of these animals, which is overseen by Veterinary science. In India, a major milk and milk product producer, the dairy industry actively contributes to the nation's economy. Among the primary milk sources-cows, buffaloes, goats, and sheep-cows hold a revered place in different faiths. Particularly in the dairy industry, cows are vital for milk production. However, monitoring cow health poses challenges. Clinical Mastitis, an inflammation affecting cows' mammary glands, hampers milk production. Automatic milking machines are commonly used, but identifying and segregating mastitis-infected cows from healthy ones remains difficult. The contagious nature of the disease increases the risk of widespread infection among cows. To combat this issue, we propose a solution called the Internet of Animal Health Things (IoAHT), based on the Internet of Things (IoT). IoAHT employs IoT technology for animal health, aiming to accurately identify cases of clinical mastitis in cows. The implementation of IoAHT has the potential to revolutionize health monitoring in the dairy industry, safeguarding cow well-being and ensuring sustainable milk production. © 2023 IEEE.

关键词： Internet of things

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Cirrhosis Prediction in Chronic Liver Disease Patients Using Machine Learning Techniques 3

Cirrhosis Prediction in Chronic Liver Disease Patients Using...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Athish, V.P. Sree Nandha, S.S. Rajeswari, D. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Dept. of Data Science and Business Systems Tamilnadu Kattankulathur603203 India

ISBN: (纸本)9798350322842

Liver failure is a serious medical condition that can have life-threatening consequences. The liver is responsible for filtering blood and performing many other important functions. With the rise of machine learning in healthcare, there is an opportunity to use past datasets to predict liver failure early on. The main focus of this research work is to explore the application of supervised machine learning algorithms in predicting liver failure. The study involved pre-processing techniques such as univariate and bivariate analysis, and data visualization to gain better insights into the features of the dataset. Using these techniques, a multi-class classification model was built with machine learning algorithms, and the performance metrics of accuracy, F1 score, and recall were used to compare the algorithms. The results show that machine learning algorithms can accurately predict liver failure and can be an effective tool in healthcare. This study demonstrates the potential of machine learning in predicting liver failure and can contribute to the development of future models in the field. The results of this research study indicates that the proposed algorithm achieved an accuracy of 94.48%. The proposed work will be beneficial to researchers in this field and inspire them to build more advanced and accurate models. © 2023 IEEE.

关键词： Support vector machines

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Stochastic Large-scale Machine Learning Algorithms with Distributed Features and Observations

Stochastic Large-scale Machine Learning Algorithms with Dist...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Fang, Biyi Klabjan, Diego Vo, Truong Northwestern University Dept. of Engineering and Applied Mathematics EvanstonIL60208 United States Northwestern University Dept. of Industrial Engineering and Management Sciences EvanstonIL60208 United States Northwestern University Dept. of Machine Learning and Data Science EvanstonIL60208 United States

ISBN: (纸本)9798350362480

As the size of modern datasets exceeds the disk and memory capacities of a single computer, machine learning practitioners have resorted to parallel and distributed computing. Given that optimization is one of the pillars of machine learning and predictive modeling, distributed optimization methods have recently garnered ample attention, in particular when either observations or features are distributed, but not both. We propose a general stochastic algorithm where observations, features, and gradient components can be sampled in a double distributed setting, i.e., with both features and observations distributed. Very technical analyses establish convergence properties of the algorithm under different conditions on the learning rate (diminishing to zero or constant). Computational experiments in Spark demonstrate a superior performance of our algorithm versus a benchmark in early iterations of the algorithm, which is due to the stochastic components of the algorithm. © 2024 IEEE.

关键词： Contrastive Learning

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An Overview of Protocols-Based Security Threats and Countermeasures in WLAN 4

An Overview of Protocols-Based Security Threats and Counterm...

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4th International Conference for Emerging Technology, INCET 2023

作者： Ojha, Nitish Kumar Baray, Elyas Indian Institute of Information Technology Dept. of Information Technology Allahabad211012 India Sharda University Knowledge Park - III Dept. of Computer Science and Engineering Greater Noida201310 India

ISBN: (纸本)9798350335750

WLAN security protocols are the most important section for wireless networks which controls all the security-related issues by securing the network with some pre-defined rules made by wireless-protected access organizations, further which makes it the main target of hacking and threats by interested parties and to be safe from such threats. There should be countermeasures for such threats where there will be a solution for any threat encountered and to mention. WIFI or wireless local area Network is a cost-effective easy deployment Network that makes users take advantage of vast data shared on the internet and by the network without the need for any cable for connections. Here in this overview paper by reviewing many researchers' techniques and works on the WLAN security topic, there are shared threats to WLAN and the ways of taking precautions against those threats as countermeasures and for which the tables are used. For comparison of techniques used by authors and some famous and highly dangerous threats are also defined further. WPA3's new security protocol is also defined with its newly found vulnerabilities and countermeasures except there is a conclusion on the downgrade attack problem with WPA3. Further, there are also countermeasures for that as well, nowadays people think the network is secured with standards defined by the IEEE organization but some know this is not the case so it makes it a more important area for research. © 2023 IEEE.

关键词： Wireless local area networks (WLAN)

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