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Evaluation of Machine Learning Algorithm and SMOTE for Insider Threat Detection 5th

Evaluation of Machine Learning Algorithm and SMOTE for Insid...

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5th International Symposium on Intelligent Computing Systems

作者： Ojo, Daniel Al-Mhiqani, Mohammed Al-Aqrabi, Hussain Al-Shehari, Taher Univ Huddersfield Dept Comp Sci Huddersfield HD1 3DH W Yorkshire England Higher Coll Technol Dept Comp Informat Sci POB 7947 Sharjah U Arab Emirates King Saud Univ Dept Self Dev Skill Comp Skills Riyadh 11362 Saudi Arabia

ISBN: (纸本)9783031829307;9783031829314

Insider threats represent a significant risk to organizational security, characterized by their covert nature and the complexity of detecting malicious activities within legitimate user behavior. Traditional detection systems often struggle with imbalanced datasets, where the prevalence of insider threats is minimal compared to normal behavior, leading to a high rate of false positives and undetected threats. This research evaluates the effectiveness of various machine learning (ML) algorithms in identifying insider threats, with a particular focus on the implementation of Synthetic Minority Over-sampling Technique (SMOTE) to address the challenge of data imbalance. By combining SMOTE with advanced ML techniques, this study aims to enhance the accuracy and robustness of insider threat detection systems. The algorithm with the best result is Random Forest which achieved 100% accuracy, recall which is 93% and F-score of 96%. The results of this study will inform the design of more resilient security measures, better equipped to detect and respond to insider threats in a wide range of organizational contexts.

关键词： Cyber security Insider threat Machine Learning Detection

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The Benefits and Challenges of Using Datathons as a Method of Learning Data Analytics 4th

The Benefits and Challenges of Using Datathons as a Method o...

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4th International Conference on Society 5.0

作者： Msweli, Nkosikhona Theoren Mawela, Tendani Univ Pretoria Dept Informat ZA-0028 Pretoria South Africa

ISBN: (纸本)9783031714115;9783031714122

The acquisition of data analytics skills is becoming increasingly crucial in today's data-driven world. At the same time datathons are gaining traction in the field of data science since these events enable participants to collaboratively solve practical problems and make decisions using real-world data sets. This study explored how datathons may support the learning of data analytics. The study, which was exploratory and qualitative, collected data via five focus group sessions that were conducted with datathon participants. The aim was to investigate participants' views on the perceived benefits, and challenges of using datathons to learn data analytics. The study results indicate that datathons provide a productive starting point for learning data analytics for individuals with no knowledge of data science. However, further learning opportunities and research are needed for an individual to fully grasp data analytics. For students in the field of technology, participating in datathons provides a unique opportunity to hone their data analytical skills in a competitive and collaborative environment. By working on real-world datasets and solving complex problems, participants indicated that they gained practical experience and learned valuable techniques for extracting insights from data. While datathons offer enriching experiences, there are some challenges with the intense, time-limited and competitive nature of these events. This study contributes to the understanding of the role of datathons in facilitating learning and skill development in data analytics and provides recommendations towards leveraging the potential of datathons as a learning tool in the field of data science.

关键词： datathon data analytics data science pedagogies transdisciplinary

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Dual Model Dynamics: Enhancing Depression Prediction Through the Integrated Use of Convolutional Neural Networks and Support Vector Machines in Data-Driven Methods 4th

Dual Model Dynamics: Enhancing Depression Prediction Through...

引用

4th International Conference on Artificial Intelligence and Knowledge Processing

作者： Jose, Alphonsa Baniya, Achal George, Angelina Kumar, K. Dinesh Amrita Vishwa Vidyapeetham Amrita Sch Comp Dept Comp Sci & Engn Bangalore Karnataka India

ISBN: (纸本)9783031734762;9783031734779

Depression being a prevalent mental health issue that significantly affects and impacts society, it calls for effective treatments that offer precise and timely prediction approaches. This paper introduces a unique hybrid model that combines Support Vector Machine (SVM) classification with Convolutional Neural Networks (CNNs). Due to potential risks associated with generating synthetic samples for medical datasets, the study evaluates the models both with and without the Synthetic Minority Over-sampling Technique (SMOTE). The study assessed K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), and SVM as baseline models, with SVM achieving the highest test accuracy among the three, recording 92.4% with SMOTE and 90% without SMOTE. The hybrid model, combining CNN and SVM with SMOTE augmentation, achieved a remarkable test accuracy of 93%, compared to 88.4% without SMOTE. These findings highlight the hybrid model's efficacy and stress how crucial it is to resolve class imbalance in medical datasets in order to improve forecast accuracy.

关键词： Deep Learning Feature Extraction Hybrid Methodology Machine Learning Mental Health

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Cognitive Ad Hoc Trust Routing for Enhanced Quality of Service 5th

Cognitive Ad Hoc Trust Routing for Enhanced Quality of Servi...

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5th International Conference on Computing science, Communication and Security

作者： Neelima, N. Pratap, P. Syam Kiran, P. Satya JNTUK Velagapudi Ramakrishna Siddhartha Engn Coll Vijayawada Andhra Pradesh India

ISBN: (纸本)9783031751691;9783031751707

Because of the dynamic nature and lack of infrastructure of MANETs, routing in such networks is vulnerable to several attacks, and standard fixed policy routing algorithms are inefficient in tackling them. Reinforcement learning methods and appropriate models based on trust are assuring for dealing with the issues and variable behaviour of rogue network nodes. In this research, we present a cognition layer that interacts with the network layer in parallel and consists of two phases: pathfinding (routing) and trust *** first phase uses ML techniques, while the second is focused on trust assessment. Regarding three performance indicators, CTR, our technique is evaluated against a well-known protocol, TQR. The simulation findings demonstrate improved end-to-end latency and communication.

关键词： MANETS CTR TQR Routing

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Digitalising the Supply Chain for Enhanced Efficiency and Customer Satisfaction 5th

Digitalising the Supply Chain for Enhanced Efficiency and Cu...

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5th 5th Olympus - International Conference on Supply Chain

作者： Balaska, Vasiliki Symeonidis, Symeon Antoniou, Sarantis Fotiadis, Thomas Chatzoglou, Prodromos Gasteratos, Antonios Democritus Univ Thrace Dept Prod & Management Engn Xanthi Greece

ISBN: (纸本)9783031693502;9783031693519

In the rapidly evolving landscape of global supply chains, integrating digital technologies is a key factor in enhancing operational efficiency, ensuring product traceability, and capturing consumer sentiment. This research delves into the transformative potential of blockchain, Non-Fungible Tokens (NFTs), and advanced sentiment analysis in redefining supply chain management practices. By harnessing these technologies, the study proposes a novel framework to achieve real-time visibility, end-to-end traceability, and a deeper understanding of consumer preferences. The methodology encompasses the deployment of digital passports and NFTs for securing product information, alongside utilising natural language processing techniques to analyse customer feedback across various platforms. Through case studies in food safety, pharmaceuticals, and retail, the paper shows how integrating digital strategies mitigates risks, boosts transparency, and enhances customer engagement. The findings reveal significant improvements in supply chain resilience, operational transparency, and customer satisfaction. The research outlines challenges such as data security, technological complexity, and workforce upskilling for adopting digital tools. Conclusively, this paper underscores the critical importance of digital innovation in supply chains, advocating for further research and development to overcome existing barriers. The future focuses on digitalised supply chains enhancing efficiency and value creation via customer insights and sustainable methods.

关键词： supply chain digitalisation blockchain traceability NFT digital passports sentiment analysis

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Application of Prediction Algorithms in a Web System to Optimize Ornamental Fish Sales at Granlevein Farm, Churute 10th

Application of Prediction Algorithms in a Web System to Opti...

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10th International Conference on Technologies and Innovation

作者： Delgado-Vera, Carlota Yerovi Ricaurte, Elke Vergara-Lozano, Vanessa Ona Reyes, Thayri Zamora Perez, Angie Ruiz-Sanchez, Monica Univ Agraria Ecuador Fac Ciencias Agrarias Ave 25 Julio Guayaquil Ecuador Univ Estatal Milagro Cdla Univ Dr Romulo Minchala Murillo Fac Ciencias Educ Km 15 Via Milagro Virgen Fatima Guayas Ecuador

ISBN: (纸本)9783031757013;9783031757020

information and communication technology has revolutionized numerous sectors, including commerce, where prediction and data analysis play a crucial role in optimizing operations and maximizing sales. In this context, the article focuses on demonstrating the implementation of a web and mobile system to predict the sales of ornamental fish at the "GRANLEVEIN" farm, where various algorithms were compared in order to determine the most accurate prediction method. The methodology employed was Extreme Programming (XP), which aided in the planning phases and flexible identification of requirements to design the software. Regarding data science-oriented analysis, the Hefesto 2.0 methodology was used, and technologies such as JavaScript, HTML, PHP, Bootstrap, and the *** library were employed to build the Data Warehouse. The multiple linear regression model showed a 75% accuracy, standing out as the most suitable tool for this purpose compared to other algorithms like Naive Bayes and Random Forest. The significant variables included in the model were the type of fish, the selling price, promotions, and customer demographics, ensuring that the main influences on sales behavior were captured. Finally, it is concluded that the employed algorithm achieved greater accuracy concerning the farm's input information, which helped improve the sales of ornamental fish for forecasting and business performance.

关键词： Multiple Linear Regression Algorithm Supervised Learning Farm Sales Prediction Ornamental Fish

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Fault Diagnosis of Photovoltaic Modules Based on Feature Extraction 5th

Fault Diagnosis of Photovoltaic Modules Based on Feature Ext...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Wang, Xueqi Cao, Lixia Shandong Jianzhu Univ Sch Informat & Elect Engn Shandong Key Lab Intelligent Bldg Technol Jinan 250101 Peoples R China

ISBN: (纸本)9789819770069;9789819770076

In order to realize accurate diagnosis of large-scale PV fault data, a fault diagnosis method based on feature extraction was proposed to realize accurate diagnosis of different types of faults. By analyzing the output characteristic curves of different faults, the characteristic quantities reflecting different fault characteristics were extracted. PCA (Principal Component Analysis) was used to downsize and initially detect the fault data, and the CatBoost algorithm was used to classify the fault data, and the fault diagnosis model based on PCA-CatBoost algorithm was established. Through the MATLAB simulation experiment analysis, it was verified that the algorithm model can effectively improve diagnostic accuracy.

关键词： PV array Principal Component Analysis CatBoost Fault diagnosis

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Optimizing Disaster Preparedness: A Zigbee and Digimesh-Based Approach for Illuminated Evacuation Routes 1

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Ruete Zuniga, David Caroca Navarro, Alejandro Flores-Bahamonde, Freddy Reyes Rogget, Marcelo Calderon Henriquez, Maria Soledad Salinas Retamal, Sergio Vasquez Ramirez, Cesar Caselli Benavente, Nicolas Univ Andres Bello Vina Del Mar Chile

ISBN: (数字)9783031832079

ISBN: (纸本)9783031832062;9783031832079

Natural disasters are increasingly frequent, affecting numerous countries and regions. This study introduces a technological tool aimed at improving energy efficiency and aiding in tsunami evacuations and disaster drills. In addition to complementing existing early warning systems, the tool enhances evacuation processes in coastal areas and serves as a simulation resource for community training. The research shows that integrating specific technological elements into existing street lighting effectively creates an evacuation route indicator, supplementing physical signage and phone alerts. The solution utilizes a mesh network architecture with end devices connected via the Zigbee protocol and Digimesh topology. Key results include reduced channel interference, minimized data loss, and extended battery life through retransmissions. Signal strength was maintained between -77 dBm and -89 dBm, indicating very good to excellent performance. Sleep mode activation was achieved within 0.077 s, making the system suitable for disaster scenarios. These outcomes were validated in a real-world pilot study.

关键词： Internet of Things Natural Disasters Public Lighting Smart Cities

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Forecasting Multivariate Time Series with Trend and Seasonality: A Random Forest Approach 5th

Forecasting Multivariate Time Series with Trend and Seasonal...

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5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Marzak, Zahira Benabbou, Rajaa Mouatassim, Salma Benhra, Jamal Hassan II Univ Casablanca Dept Ind & Logist Engn GIL ENSEM Lab Adv Res Ind & Logist Engn LARILE OSIL TeamENSEM Casablanca Morocco

ISBN: (纸本)9783031807749;9783031807756

Random Forest algorithm, a powerful machine learning technique recognized for its flexibility and high predictive accuracy has been used in different applications across various domains. This paper presents a comparative analysis of the performance of Random Forest in forecasting multivariate time series data exhibiting trend and seasonality, a task that poses a significant challenge since the traditional methods often used to forecast time series can fail to accurately model these complex characteristics, leading to poor forecasting performance and unreliable prediction. In this study, different optimization techniques are used to enhance the performance of the RF models, such as feature engineering, using various features (temporal, lagged, rolling window, and interaction features), and parameter tuning (Manual, Random Search, and Grid Search). The models are evaluated using three accuracy metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) with lower values indicating better performances, the results are then compared to traditional forecasting methods. The proposed forecasting framework demonstrated the capability of the RF model to capture trends and seasonality in multivariate time series and provide competitive results.

关键词： Multivariate time series Forecasting Trend Seasonality Random forest

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Right Turn: A Cybersecurity Serious Game with a Scenario-Based Assessment 1

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15th Indian Conference on Human-computer Interaction Design and Research

作者： Jayakrishnan, Gokul Banahatti, Vijayanand Lodha, Sachin Tata Consultancy Serv Ltd TCS Res Pune Maharashtra India

ISBN: (数字)9783031808326

ISBN: (纸本)9783031808319;9783031808326

Security awareness programs and assessments often demonstrate high performance from the participants. Despite this, in real-life, security (cybersecurity) attacks often succeed because of some human error or poor judgment. We postulate that one reason for this difference is because the assessment methods grade the participants' awareness higher than it actually is. Unlike in question-based assessments, real life often presents scenarios involving several sub-decisions, each requiring careful attention. We attempted to assess the security awareness of enterprise employees by focusing on certain real-life scenarios and developed a security-oriented serious game called 'Right Turn'. The participants' game responses were assessed not just on a question-answer basis, but also on the overall correctness of the scenarios having multiple sub-decisions. Our exploratory study with 5,932 participants from an enterprise setting indicates that despite having good correctness in 'answering questions', the participants showed inferior performance in overall 'scenarios' within the game. In this paper, we provide a unique perspective on gauging the performance of security serious games.

关键词： Serious Game Scenario-based Security awareness Enterprise

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