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Cauchy Grasshopper Optimization Algorithm with Deep learning Model for Cloud Enabled Cyber threat Detection System 7

Cauchy Grasshopper Optimization Algorithm with Deep Learning...

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7th international conference on intelligent Computing and Control Systems, ICICCS 2023

作者： Ravi, C.N. Karthik, T.S. Manikandan, K. Kalaivaani, P.C.D. Chopkar, Priyanka Nandkishor Srivastava, Aviral CMR Engineering College Computer Science & Engineering Department Telangana Hyderabad India Aditya College of Engineering & Technology Surampalem E.G.Dist. Andhra Pradesh 533437 India Government College of Engineering Thanjavur India Kongu Engineering College Computer Science and Engineering Perundurai India G H Raisoni Institute of Engineering & Technology Electronics & Telecommunication Engineering Nagpur India Amity University Rajasthan Amity University Rajasthan NH 11 C kant kalwar Jaipur300202 India

ISBN: (纸本)9798350397253

the tremendous growth of internet technology has drastically improved the large amount of connected devices. To secure network infrastructure from the damage that cyberattacks might cause, this has made an enormous attack surface that needs the deployment of practical and effective counter measures. In the contemporary era of active network transmission and throughput, Intrusion Detection System (IDS) plays a vital role in ensuring secure network resource and data from outside invasion. In recent times, IDS becomes an essential tool to enhance the efficiency and flexibility for unpredictable and unexpected invasions of the network. Deep learning (DL) is a well-known and essential method to resolve challenges and could learn rich features of massive information. therefore, the study focuses on the design and development of the Cauchy Grasshopper Optimization Algorithm with Deep learning for Cloud Enabled IDS (CGOA-DLCIDS) technique. the presented CGOA-DLCIDS method aims to recognize the presence of intrusions in the cloud platform. To achieve this, the CGOA-DLCIDS technique performs feature subset election by CGOA which reduces the feature subset and enhances the intrusion detection rate. Next, the CGOA-DLCIDS technique employs attention based long short-term memory (ALSTM) module for automated and accurate intrusion detection and classification. the simulations analysis of the CGOA-DLCIDS method on benchmark dataset highlighted the increasing results compared to recent IDS approaches. © 2023 IEEE.

关键词： Intrusion detection

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A Comprehensive Review on the Prediction of Diabetes Disease Using Machine learning 11

A Comprehensive Review on the Prediction of Diabetes Disease...

引用

11th international conference on intelligent Systems and Embedded Design, ISED 2023

作者： Shrivastava, Pragya Kumari, Aradhya Kumari, Shimpu Bajaj, Parveen Chandigarh University Dept.of Cse Chandigarh India

ISBN: (纸本)9798350318159

In this review, we delve into the use of machine learning techniques for predicting diabetes. the paperdiscusses various applications of these techniques and provides insights into their effectiveness. Prior prediction and management of diabetes can reduce the burden on healthcare systems and significantly improve patient outcomes. Machine learning models have demonstratedsignificant potential in analyzing diverse datasets, incorporating various risk factors, and providing accurate predictions. this delivers an overview of the various ML algorithms and ways employed for diabetes prediction. It discusses the selection of relevant features, data preprocessing techniques, and evaluation metrics commonly used in the context of diabetes prediction. Additionally, the paper examines the challenges and limitations associated with ML- based diabetes prediction, including issues related to data quality, interpretability, and generalization. Furthermore, the review highlights prominent studies and datasets in the field, showcasing the performance and comparative analyses of different ML models. By critically analyzing the existing literature, this paper aims to offer insights into the current state of diabetes prediction using ML and identify potential avenues for future research. © 2023 IEEE.

关键词： Support vector machines

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Research on Photovoltaic Arc Fault Diagnosis Model Based on Machine learning 11

Research on Photovoltaic Arc Fault Diagnosis Model Based on ...

引用

11th China international conference on Electricity Distribution, CICED 2024

作者： Li, Weijun Li, Tianyou College of Electrical Engineering and Automation Xiamen University of Technology Xiamen China

ISBN: (纸本)9798350368345

Photovoltaic power stations are prone to arc faults due to wind, sun, resulting in frequent occurrence of photovoltaic fires and economic losses to the photovoltaic. As a result, it is crucial to identify arc defects in solar power plants in order to guarantee their dependability and efficiency. this research proposes a multi-group string comparison method for photovoltaic fault detection. the photovoltaic system's arc fault model is established, the arc fault characteristics are obtained, and the characteristic quantities are extracted. the arc diagnosis model of photovoltaic power station is established, which can effectively identify series fault and parallel arc fault based on particle swarm optimization random forestrc. Its accuracy is higher than that of random forest recognition, the anti-interference of the diagnosis model is analyzed by the actual power station operation data. It is verified that the model is not interfered by the inverter Internal mechanisms and environmental factors. © 2024 IEEE.

关键词： Random forests

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Priority Based Cyber Incident Handling: Work in Progress 11

Priority Based Cyber Incident Handling: Work in Progress

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11th international conference on Electrical engineering, Computer Science and Informatics, EECSI 2024

作者： Mulyati Stiawan, Deris Widhiarso, Wijang Budiarto, Rahmat Multi Data Palembang University & Faculty of Engineering Universitas Sriwijaya Department of Information System Palembang Indonesia Sriwijaya University Faculty of Computer Science Palembang Indonesia Multi Data Palembang University Department of Informatics Palembang Indonesia College of Computer Science & IT Albaha University Saudi Arabia

ISBN: (纸本)9798350355314

Effective and efficient cyber incident handling is crucial for maintaining the security of information systems and organizational data. this research aims to develop a priority-based cyber incident handling method by adopting the NIST 2.0 framework and utilizing machine learning classification algorithms. this method enables organizations to quickly and accurately identify and respond to incidents based on threat levels and impact, focusing resources on the most critical incidents. the research employs the Support Vector Machine (SVM) classification algorithm to determine incident priorities. the study is still developing for handling cyber incidents, followed by testing and evaluating the system prototype within organizations. Initial results indicate that the priority-based approach using the SVM classification algorithm achieves a True Positive (TP) Rate of 0.999 and a False Positive (FP) Rate of 0.008, with a correlation of 98.88%, resulting in excellent performance in identifying positive cases and a low error rate. © 2024 IEEE.

关键词： Cyber attacks

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F-BBT: An Industrial Risk Detection Model Based on Feature Fusion 11

F-BBT: An Industrial Risk Detection Model Based on Feature F...

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11th IEEE international conference on Behavioural and Social Computing, BESC 2024

作者： Zhang, Junwei Liu, Jiayu Xv, Dongxin Tan, Minqing Ma, Zhiyuan China Cybersecurity Review Certification and Market Regulation Big Data Center Beijing China Harbin Engineering University Harbin China

ISBN: (纸本)9798331531904

With the continuous improvement of modern indus-trial production capabilities, risk detection of industrial products has become more and more important. Industrial risk detection helps improve product quality and corporate competitiveness, and promote sustainable development. the development of detection technologies and systems is crucial to modern industrial production. However, as the types and scale of industrial products increase, the singularity of industrial product data increases geometrically. Traditional risk assessment methods are difficult to meet the needs of modern industrial safety supervision. In order to solve the above problems of product safety detection system. this paper proposes a deep learning industrial product risk assessment model: F-BBT Net. this model can more effectively predict and monitor potential safety issues,and then integrating and interacting features extracted from different existing models. Experimental results show that F-BBT Net has higher accuracy and reliability in risk identification, and the prediction accuracy is 75.82% higher than that of traditional methods. the model code and experimental code of the paper can be obtained from the website https://***/liuvvvvv1/F-BBT-model/tree/master. © 2024 IEEE.

关键词： attention mechanism feature fusion industrial products safety supervision

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2024 17th international conference on Computer and Electrical engineering

2024 17th International Conference on Computer and Electrica...

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2024 17th international conference on Computer and Electrical engineering, ICCEE 2024

the proceedings contain 11 papers. the topics discussed include: design of railway simulation training system based on WebGL;analysis of different test techniques of DC circuit breaker;MPDDF: multi perspective dual data flow model for time series forecasting;Improved LPAstar algorithm for power station cabinet wiring;disaster rescue drone based on YOLOv4 algorithm;a real-time cardiac output monitoring system based on photoplethysmography;trajectory tracking control based on deep reinforcement learning and ensemble random network distillation for robotic manipulator;UAV trajectory planning in NOMA-aided UAV-mounted RIS networks: a budgeted multi armed bandit approach;research on an intelligent perception data collection method;and symbol time compression for OFDM spectral efficiency in visible light communication.

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Investigating the Safety Effects of Degraded Wireless Performance on Connected Longitudinal Driver Assistance Functions 11

Investigating the Safety Effects of Degraded Wireless Perfor...

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11th international conference on Vehicle Technology and intelligent Transport Systems, VEHITS 2025

作者： Nagy, Roland Petho, Zsombor Kazár, Tamás Márton Turóczi, Tibor Török, Árpád Department of Automotive Technologies Faculty of Transportation Engineering and Vehicle Engineering Budapest University of Technology and Economics Műegyetem rkp. 3 BudapestH-1111 Hungary Jaguar Land Rover Hungary Ltd. Hungary

ISBN: (纸本)9789897587450

In the near future, Vehicle-to-Everything (V2X) based technologies will enable vehicles and other road users to exchange information with each other, even in cases where the applicability of other sensors is limited. this technology will be able to ensure the operation of advanced driver assistance systems, in cases where the other sensors are malfunctioning. In such situations, where only wireless communication can be relied upon, it is essential to be able to react to possible changes in network performance metrics. the objective of this paper is to address the aforementioned problem by characterizing the safety-risk associated with vehicle dynamic parameters and the factors influencing the network performance metrics in different scenarios. the network can be divided into seven distinct layers that are responsible for data transmission, and the research primarily focuses on the physical layer, with the objective of studying its impact on the packets sent. In the research, the Signal-to-Noise Ratio (SNR) is considered to be the primary network influencing parameter. this will facilitate the enhancement of not only the safety of transportation but also its reliability. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Accident prevention

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the Cynicism of Modern Cybercrime: Automating the Analysis of Surface Web Marketplaces 17

The Cynicism of Modern Cybercrime: Automating the Analysis o...

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17th IEEE international Congress on intelligent and Service-Oriented Systems engineering (CISOSE)

作者： Lykousas, Nikolaos Koutsokostas, Vasilios Casino, Fran Patsakis, Constantinos Data Centr Bucharest Romania Univ Piraeus Dept Informat Piraeus Greece Univ Rovira I Virgili Dept Comp Engn & Math Taragonna Spain

ISBN: (纸本)9798350322392

Cybercrime is continuously growing in numbers and becoming more sophisticated. Currently, there are various monetisation and money laundering methods, creating a huge, underground economy worldwide. A clear indicator of these activities is online marketplaces which allow cybercriminals to trade their stolen assets and services. While traditionally, these marketplaces are available through the dark web, several of them have emerged in the surface web. In this work, we perform a longitudinal analysis of a surface web marketplace. the information was collected through targeted web scrapping that allowed us to identify hundreds of merchants' profiles for the most widely used surface web marketplaces. In this regard, we discuss the products traded in these markets, their prices, their availability, and the exchange currency. this analysis is performed in an automated way through a machine learning-based pipeline, allowing us to quickly and accurately extract the needed information. the outcomes of our analysis evince that illegal practices are leveraged in surface marketplaces and that there are no effective mechanisms towards their takedown at the time of writing.

关键词： Cybercrime Marketplaces data Leaks Machine learning Topic Modelling

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Short Paper: Revealing the Self: Brainwave-Based Human Trait Identification 24

Short Paper: Revealing the Self: Brainwave-Based Human Trait...

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11th international conference on Networking, Systems and Security, NSysS 2024

作者： Islam, Md Mirajul Uddin, Md Nahiyan Hasana, Maoyejatun Pandit, Debojit Rahman, Nafis Mahmud Chellappan, Sriram Azam, Sami Al Islam, A.B.M. Alim Bangladesh University of Engineering and Technology Dhaka Bangladesh Sunbeams Dhaka Bangladesh University of South Florida Tampa United States Charles Darwin University Darwin Australia

ISBN: (纸本)9798400711589

People exhibit unique emotional responses. In the same scenario, the emotional reactions of two individuals can be either similar or vastly different. For instance, consider one person’s reaction to an invitation to smoke versus another person’s response to a query about their sleep quality. the identification of these individual traits through the observation of common physical parameters opens the door to a wide range of applications, including psychological analysis, criminology, disease prediction, addiction control, and more. While there has been previous research in the fields of psychometrics, inertial sensors, computer vision, and audio analysis, this paper introduces a novel technique for identifying human traits in real time using brainwave data. To achieve this, we begin with an extensive study of brainwave data collected from 80 participants using a portable EEG headset. We also conduct a statistical analysis of the collected data utilizing box plots. Our analysis uncovers several new insights, leading us to a groundbreaking unified approach for identifying diverse human traits by leveraging machine learning techniques on EEG data. Our analysis demonstrates that this proposed solution achieves high accuracy. Moreover, we explore two deep-learning models to compare the performance of our solution. Consequently, we have developed an integrated, real-time trait identification solution using EEG data, based on the insights from our analysis. To validate our approach, we conducted a rigoroususer evaluation with an additional 20 participants. the outcomes of this evaluation illustrate both high accuracy and favorable user ratings, emphasizing the robust potential of our proposed method to serve as a versatile solution for human trait identification. © 2024 Copyright held by the owner/author(s).

关键词： Brainwaves

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Ellipsoidal distribution-free set 11

Ellipsoidal distribution-free set

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11th international Research conference Information Technology and Implementation - Workshop: intelligent Systems and Security, IT and I-WS 2024: ISS

作者： Klyushin, Dmitriy Tymoshenko, Andrii Taras Shevchenko National University of Kyiv 60 Volodymyrska Street Kyiv01033 Ukraine

this paper introduces a distribution-free approach based on the Hill's assumption and the Petunin ellipsoids. Several distributions are used to generate points and build ellipsoids, which are then used to check if test points with same distribution are created inside largest ellipsoid. As a result, a new prediction set is constructed in the form of Petunin ellipsoid, while confidence level refers to the number of points. the method described here works effectively for chosen distributions. Moreover, statistical analysis of the quantity of points inside is performed. this method is a useful tool for solving many urgent problems of machine learning, e.g. generalization of training samples, effective cross-validation etc. © 2024 Copyright for this paper by its authors.

关键词： data mining outlier detection Petunin ellipsoid prediction set

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