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Predictive Machine learning Models for DDoS Attack Mitigation 3

Predictive Machine Learning Models for DDoS Attack Mitigatio...

引用

3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Bansal, Jyoti Mehra, Vishal Sadim, Mohd Ies College of Technology Department of Electrical & Electronics Engineering Madhya Pradesh Bhopal462044 India Ies College of Technology Department of Electronics & Communication Engineering Madhya Pradesh Bhopal462044 India Ies University Department of Computer Science & Engineering Madhya Pradesh Bhopal462044 India

ISBN: (纸本)9798350343274

This research provides a comprehensive method for enhanced Distributed Denial of Service (DDoS) detection and mitigation by utilizing ensemble learning, namely the Random Forest, Gradient Boosting, and AdaBoost algorithms. This may be done by combining these three strategies of learning. These algorithms were chosen for their demonstrated capacity to analyze high-dimensional data, understand complex connections, and adapt to new forms of attack. The dataset is then divided into training, validation, and testing sets to assess the model's resilience. Predictions obtained because of independent training of each basic technique on the training dataset were labeled RFi(X), GBi(X), and ABi(X), respectively. The weights for this approach are determined by a procedure known as cross-validation. The ensemble forecast is used to establish a threshold, and when a DDoS attack reaches that level, mitigation is activated. One method for dealing with the problem is traffic rerouting. Other methods include rate limiting, temporary IP address bans, and the adoption of new machine learning models for more in-depth research. The ensemble learning model's efficiency is reviewed on a regular basis, and changes are made as needed to account for developing trends in DDoS assaults. The suggested technique has several advantages and benefits over more standard DDoS protection systems. These enhancements include higher rates of true positives, lower rates of false alarms, and increased accuracy and precision. This illustrates its agility in both responding to and defending against a wide range of DDoS attack routes. © 2023 IEEE.

关键词： Denial-of-service attack

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Remaining Cycle Time Prediction in Business Processes with TPOT Regression for automated Machine learning 4th

Remaining Cycle Time Prediction in Business Processes with T...

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4th international conference on Novel and intelligent Digital Systems, NiDS 2024

作者： Bousdekis, Alexandros Kaftantzis, Savvas Theodoropoulou, Georgia Miaoulis, Georgios Department of Informatics and Computer Engineering University of West Attica Athens Greece

ISBN: (纸本)9783031733437

Process mining allows the extraction of useful information from the event logs and historical data of business processes. Predictive business process monitoring is a sub-field of process mining and deals with runtime methods aimed at generating predictive models about various objectives related to a process instance by taking advantage of Machine learning (ML) and deep learning algorithms. However, despite the large amount of research works dealing with predictions about next activity prediction and remaining time of running process instances, the objective of cycle time prediction has been overseen although it can provide higher level insights targeted the strategic planning. At the same time, automated Machine learning (AutoML) has not been investigated in the predictive business process monitoring domain in order to automate the time-consuming process of designing and optimizing ML pipelines. Therefore, motivated by its promising results in other domains, we propose an approach for remaining cycle time prediction in business processes based on AutoML, and specifically on the Tree-Based Pipeline Optimization Tool (TPOT) method. The results demonstrate that the proposed approach achieves high accuracy, while it minimizes human intervention in the configuration of ML pipelines. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep learning

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Capacity estimation of lithium-ion batteries based on Transformer model 3

Capacity estimation of lithium-ion batteries based on Transf...

引用

3rd international conference on Electrical engineering and Mechatronics Technology, ICEEMT 2023

作者： Liu, Yanshuo Wang, Kai Qingdao University School of Electrical Engineering Weihai Innovation Research Institute Qingdao China

ISBN: (纸本)9798350303698

Accurately estimating the capacity of lithium-ion batteries (LIBs) is crucial for evaluating battery performance and optimizing energy management. This study proposes a method based on the Transformer model to predict the capacity of LIBs. Compared with traditional methods, this method achieves better contextual modeling capabilities through self-attention mechanism and global information interaction. Firstly, obtain the time series impedance data of the battery through impedance measurement. Then, these data are input into the Transformer model, and the relationships and dependencies between features are learned through the multi head attention mechanism. Experimental results show that the proposed Transformer model exhibits high accuracy and robustness in battery capacity prediction tasks. This study provides a new deep learning method for predicting the capacity of batteries, which helps to improve the energy storage and management efficiency of batteries. © 2023 IEEE.

关键词： Lithium-ion batteries

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Zero Trust Resilience Strategy for Linux Crypto Ransomware Obviation and Recuperation 3

Zero Trust Resilience Strategy for Linux Crypto Ransomware O...

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3rd IEEE international conference on intelligent Technologies, CONIT 2023

作者： Anand, C.S. Shanker, Ravi Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara India

ISBN: (纸本)9798350338607

The utilization of crypto ransomware has emerged as a potent and progressively perilous armament in the cyber criminal's repertoire, inflicting severe and extensive damages on both individuals and enterprises. Despite the conventional view of Windows-based systems as the most targeted by ransomware attacks, there has been a noticeable shift in focus towards Linux-based systems, posing a new security challenge. Linux, a widely adopted open-source operating system renowned for its robust security and dependability, has been particularly favored by critical infrastructures, government entities, and commercial enterprises. Nevertheless, the recent emergence of ransomware targeting Linux systems has exposed the platform's susceptibility to cyber threats, leading to heightened concerns about the security of these systems. This research article proposes a zero-trust resilience strategy (ZTRS) for Linux based servers and personal computers to effectively mitigate the latest crypto ransomware variants including Hive, Conti, Darkside, REvil and Red Alert. ZTRS adopts a comprehensive and advanced strategy that has been carefully designed to efficiently hinder the fundamental requirements that Linux ransomware relies on to execute its operations. It implements a range of preemptive measures to prevent any efforts by the ransomware to obtain root privileges, and to obstruct the extraction of data that could enable multi-level extortion. The tactics employed by ZTRS are highly effective in safeguarding against the devastating consequences of Linux ransomware attacks. © 2023 IEEE.

关键词： Cybersecurity

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Enhancing Machine learning Approaches for Early Detection of Depression Levels for Vietnamese Students 23th

Enhancing Machine Learning Approaches for Early Detection of...

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23rd international conference on intelligent Systems Design and Applications, ISDA 2023

作者： Nguyen, Tu Anh Hoang Nguyen, Quang-Dieu Pham, Phu Nguyen, Loan T. T. School of Computer Science and Engineering International University Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam Faculty of Information Technology HUTECH University Ho Chi Minh City Viet Nam

ISBN: (纸本)9783031647758

The rise of Industry 4.0 in Vietnam has led to an increase in depression cases among the country’s youth, highlighting the need for innovative early detection measures. Depression, a severe psychological disorder, is a global concern, but most studies are based on wealthy countries’ medical data. This project aims to explore depression in Vietnamese individuals aged 15–25 using various assessment instruments, seeking to develop an effective early diagnosis and support system that considers cultural variations. The study underscores the importance of culturally sensitive, data-driven approaches to reduce the depression burden, aiding policymakers, educators, and healthcare practitioners in improving mental health among Vietnamese teenagers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Machine learning

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Dam control system based on DDPG algorithm 3

Dam control system based on DDPG algorithm

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2024 3rd international conference on Energy Utilization and Automation, ICEUA 2024

作者： Zhang, Zifei Liu, Lei Wang, Zichun School of Aeronautic Science and Engineering Beihang University Beijing Beijing100080 China School of Reliability and Systems Engineering Beihang University Beijing Beijing100080 China

Dams, regarded as a form of hydraulic engineering, are purposed to intercept river or canal flow to either elevate water levels or regulate discharge rates. The discharge of a dam is subject to influence by various factors, including precipitation, temperature, and regulatory policies. Nevertheless, in real-world scenarios, the precise value of the expected dam flow rate is challenging to ascertain through simple arithmetic operations. In this study, a novel dam flow model has been formulated. Through the utilization of the DDPG algorithm and deep learning techniques applied to data from the current month, the parameters of the dam control differential equations have been determined, and the transfer function after Laplace transformation has been established. Consequently, only the current month's fluctuations in the water level of Lake Huron need to be determined to derive the corresponding dam flow rate for that month. © Published under licence by IOP Publishing Ltd.

关键词： Water levels

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A Systematic Review on Skin Cancer Classification and Novel Approaches in Deep learning 3rd

A Systematic Review on Skin Cancer Classification and Novel ...

引用

3rd international conference on intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Narmada, Vunnam Vardhan, K. Asish Department of Computer Science Engineering Mallareddy University Telangana Hyderabad India

ISBN: (纸本)9789819783540

In the current global context, people face the risk of losing their lives to various fatal diseases. One significant concern is the potential impact of cellular network radiation and mobile phone usage on health, particularly in relation to cancer. Cancer is characterized by abnormal and uncontrolled tissue growth within the body, with the possibility of spreading to other parts beyond its point of origin. Skin cancer stands out as one of the most perilous illnesses globally. Accurate identification of skin lesions in the early stages can significantly assist clinical judgment by offering precise disease diagnoses, potentially enhancing the likelihood of a cure before cancer spreads. Nevertheless, the unbalanced and finite availability of skin disease images utilized for training presents difficulties in automating skin disease classification. Additionally, ensuring the model’s adaptability across different domains and its robustness are crucial hurdles. Recent advancements have witnessed the widespread application of deep learning-based methods in skin cancer classification to address these challenges and achieve promising outcomes. Despite these efforts, comprehensive reviews encompassing the forefront issues in skin cancer classification remain scarce. This paper aims to fill this gap by providing a thorough overview of the latest algorithms for skin cancer classification based on deep learning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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Identification of the Problem of Neural Network Stability in Breast Cancer Classification by Histological Micrographs 1

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3rd international conference on Novel and intelligent Digital Systems, NiDS 2023

作者： Sasov, Dmitry Orlova, Yulia Donsckaia, Anastasia Zubkov, Alexander Kuznetsova, Anna Noskin, Victor Software Engineering Department Volgograd State Technical University Lenin Ave 28 Volgograd400005 Russia

ISBN: (数字)9783031440977

ISBN: (纸本)9783031440960

This paper discusses the actual neural network architectures used to classify breast cancer according to histological micropreparations. A comparative analysis of models and frequently used datasets was conducted. The main methods of augmentation and data preprocessing were also identified. The main goal of the study was to test the trained models on data other than the training set. ResNet 152, DenseNet121, Inception_resnet_v2 models, and the transfer learning approach were chosen for training. As a result of the testing, it was concluded that training on data without color normalization of images and with standard augmentation (rotation and flipping) makes the models vulnerable to changes in the input data. In further studies, we plan to improve the models’ stability to changes in the color gamut of images using various methods of data preprocessing. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diseases

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Design of English Translation Model for intelligent Recognition of Fu Tea Culture Based on Improved GLR Algorithm 3rd

Design of English Translation Model for Intelligent Recognit...

引用

3rd EAI international conference on Application of Big data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Wang, Cuiying Yu, Xiaozhe Sui, Pengying Xianyang Normal University Shaanxi Xianyang712000 China No 1 Middle School of Dalian Economic and Technological Development Zone Liaoning Dalian116000 China

ISBN: (纸本)9783031631351

This paper focuses on the design of an English translation model for intelligent recognition of Fu tea culture based on the improved GLR algorithm, the particularity of the connotation of Chinese Fu tea culture, the main translation strategies of Fu tea culture, and focuses on the application of literal translation, free translation and translation compensation in the English translation of Fu tea, It aims to show the material and spiritual cultural connotation of Chinese Fu tea and promote the smooth spread and exchange of national culture. The model can be designed by the following steps: (1) the input text is divided into sentences and phrases, and then each sentence or phrase is further divided into words;(2) Arranging words into a tree structure to form a word frequency index;(3) Create a dictionary file containing all words and their definitions;(4) Using this dictionary, the probability of occurrence of each word in each sentence or phrase is calculated based on its frequency in the entire text file. This probability value will be used as a parameter to determine whether the word should appear in the sentence/phrase. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Trees (mathematics)

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Comparison and Analysis of Ultrasound Diagnosis Networks for Thyroid Nodules Based on Different Computer Vision Task Types 3

Comparison and Analysis of Ultrasound Diagnosis Networks for...

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3rd international conference on Electronic Communication and Artificial Intelligence, IWECAI 2022

作者： Zheng, Zhiqiang Chen, Jiarui Weng, Zhi Zhang, Yingxia College of Electronic Information Engineering Inner Mongolia University Huhhot China The Affiliated Hospital of Inner Mongolia Medical University Ultrasound Department Huhhot China

ISBN: (纸本)9781665479974

This paper reviews the research of deep learning algorithms in the classification of benign and malignant thyroid nodules in ultrasound diagnosis, then summarizes the current research progress on the invasiveness of thyroid nodules. Based on the analysis of the existing classification algorithms, segmentation algorithms, and the characteristics of thyroid ultrasound image data, the paper clarifies limitations and reasons of deep learning in thyroid nodule diagnosis, then proposes the solutions includes the introduction of the clinical experience, pre-training of ultrasound data. The importance of invasive prediction of thyroid nodules and the shortcomings of deep learning in invasive prediction are pointed out, and the prospect of deep learning in the field of invasive prediction of thyroid nodules is prospected. © 2022 IEEE.

关键词： Forecasting

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