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Cybersecurity Revolution via Large Language Models and Explainable AI 17

Cybersecurity Revolution via Large Language Models and Expla...

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17th International Conference on Security of Information and Networks, SIN 2024

作者： Ghazal, Taher M. Janjua, Jamshaid Iqbal Abushiba, Walid Ahmad, Munir Ihsan, Anuam Al-Dmour, Nidal A. College of Arts & Science Applied Science University Manama Bahrain Center for Cyber Security Faculty of Information Science and Technology Selangor Malaysia Lahore Pakistan College of Engineering Applied Science University Bahrain College of Informatics Korea University Seoul02841 Korea Republic of College of Engineering Mutah University Department of Computer Engineering Jordan

ISBN: (纸本)9798331509736

Integrating Groundbreaking advancements in AI, like language models, interpretable AI, and machine learning, opens up a world of exciting new possibilities. The Evolving face of cybersecurity and Modern cyber threats are complex and well crafted;hence, conventional cybersecurity mechanisms show difficulty in staying relevant. LLMs, especially based on Transformer architecture will noticeably increase the accuracy and speed of detecting threats. Transparency and trust are increased by XAI approaches like SHAP and LIME, which offer facts about ML model predictions. This paper explores the literature that demonstrates the integration between XAI and LLMs in cybersecurity, exemplifying how this trinity of models has the potential to help attenuate errors producing reduced false positives and improve how we detect threats. Thinking about the possibilities the challenges including performance Explainability trade-offs, the need for common evaluation metrics, and the black-box nature of AI Models, remain in place. Solving these will help to enhance AI-driven solutions in cybersecurity. © 2024 IEEE.

关键词： Cybersecurity Explainable AI (XAI) Large Language Models (LLMs) Machine Learning (ML) Reliability Threat Detection Transformer Architecture Transparency

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Back Translation-EDA and Transformer for Hate Speech Classification in Indonesian

Back Translation-EDA and Transformer for Hate Speech Classif...

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2023 International Conference on informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Desiani, Anita Adrezo, Muhammad Kresnawati, Endang Sri Ermatita Akbar, Muhammad Hasibuan, Muhammad Said Universitas Sriwijaya Mathematics Inderalaya Indonesia Universitas Pembangunan Nasional Veteran Jakarta Computer Science Faculty Jakarta Indonesia Universitas Sriwijaya Informatic Engineering Inderalaya Indonesia Institut Informatika Dan Bisnis Darmajaya Informatics Engineering Palembang Indonesia

ISBN: (纸本)9798350307306

Hate speech is speech or writing carried out by individuals or groups with the aim of pitting parties against each other for various purposes. The impact of hate speech is very dangerous. Machine learning can help with early detection of hate speech, especially on social media. Unfortunately, data providing hate speech in Indonesian is still limited. This study combines augmentation and classification techniques. The augmentation techniques used are EDA and back translation. By combining these two techniques, hate speech data in Indonesia can be produced with many variations. In classification, this research applies machine learning using the transformer method with the Leaky ReLU function in the hidden layer. The purpose of using the Leaky ReLU function is to avoid neuron death because it has a negative value. The performance results in this research using the proposed method obtained an accuracy of 85.21%, precision of 83.09%, recall of 88.78%, and f1-score of 85.84%. These results show the effectiveness of the proposed method approach with good performance for classifying hate speech in Indonesian on social media such as Twitter. © 2023 IEEE.

关键词： Social networking (online)

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Operational Road Map in North Sulawesi and Gorontalo Power Grid Considering the Intermittent Photovoltaic Penetration 5

Operational Road Map in North Sulawesi and Gorontalo Power G...

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5th International Conference on Power engineering and Renewable Energy, ICPERE 2022

作者： Pratama, Arif Majiid Nusa Hashfi, Muhammad Irfan Munthe, Romario Oktavelen Ginting Banjar-Nahor, Kevin Marojahan Hariyanto, Nanang Jakarta Indonesia Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia Universitas Indonesia Faculty of Computer Science Jakarta Indonesia

ISBN: (纸本)9781665455480

In 2019, the North Sulawesi and Gorontalo power system welcomed the gradual entry of photovoltaics into the high voltage grid. The first phase of the solar power plant in North Sulawesi was Likupang PV Plant at 15 MWp and Isimu PV plant at 10 MWp. The North Sulawesi and Gorontalo system is characterized by a daytime peak load of 330 MW. This way, the initial penetration of photovoltaics into the system at this stage is 7.58%. However, the system's nadir stiffness is only within a range of 10-16 MW/Hz. Therefore, the expected frequency fluctuations from intermittent photovoltaics could reach higher than 1 Hz, so it is necessary to re-engineer the operation of the electric power system. This paper presents several stages that PLN as the grid operator needs to develop, starting from the readiness of the SCADA system, adjusting the defense scheme, improving the operation of the system, and PV forecasting. Implementing the photovoltaic curtailment can be an alternative when there is a very high intermittence which causes vulnerability to the system during operation. Developments in the operation scheme and technology for photovoltaics are still carried out in stages, such as using photovoltaics curtails as a reserve margin to energy storage technologies such as the Battery Energy Storage System (BESS). The aim of the roadmap is to increase system reliability and maximize Photovoltaic utilization © 2022 IEEE.

关键词： Stiffness

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Road Damage Detection and Alert Application Using Smartphone's Built-In Sensors 24

Road Damage Detection and Alert Application Using Smartphone...

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24th International Electronics Symposium, IES 2022

作者： Puspita, Dicha Nada Basuki, Dwi Kurnia Dewantara, Bima Sena Bayu Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia Tokyo Metropolitan University Faculty of Systems Design Tokyo Japan

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489713

Road damage is truly inconvenient for drivers. It possibly results in accidents. Drivers may get a warning to avoid it if the damaged location has been detected. Road damage detection can be done using the built-in sensors on a smartphone. This research was conducted using the Machine Learning method. The final results were road damage detection and warning applications. TensorFlow Lite was needed for visual detection, while motional detection utilized an accelerometer. A warning application was synced with Firebase Cloud Messaging to send notifications 25 meters to the damage point. This allows the driver to avoid such damage. From the test results, the accuracy of visual detection reached 85.03% and motional detection reached 73.71%. In the warning application, it successfully sent notifications with 64.28% accuracy. © 2022 IEEE.

关键词： Accelerometers

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DynoGraph: Dynamic Graph Construction for Nonlinear Dimensionality Reduction 24

DynoGraph: Dynamic Graph Construction for Nonlinear Dimensio...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Qian, Li Plant, Claudia Qin, Yalan Qian, Jing Bohm, Christian Institute of Informatics Ludwig Maximilian University of Munich Munich Germany University of Vienna Faculty of Computer Science Vienna Austria ds:Univie University of Vienna Faculty of Computer Science Vienna Austria School of Communication and Information Engineering Shanghai University Shanghai China China Railway Hi-Tech Industry Corporation Limited Carbon Neutral R&D Center Beijing China

ISBN: (纸本)9798331506681

Most well-known graph-based dimensionality re-duction algorithms, such as t-SNE and UMAP, use a two-step approach: first to construct a graph out of the high-dimensional data and then to embed the graph into the low-dimensional space. The main challenges of these algorithms include how to construct a good graph and how to maintain the similarity structure of the high-dimensional data in the low-dimensional space. This study proposes DynoGraph, a novel algorithm called Dynamic Graph Construction for Nonlinear Dimensionality Reduction, to address these two challenges. First, we develop an adaptive neighborhood graph construction method that accurately captures the intrinsic geometry of the high-dimensional data. Second, for the first time, we introduce a dynamic graph modification process during dimensionality reduction, ensuring that the data structure in the low-dimensional space faithfully reflects the high-dimensional data. For vertex pairs that are connected by edges in high-dimensional space exhibit far apart in low-dimensional space, additional edges are inserted to strengthen the connection between them. Conversely, for vertex pairs that are not connected in high-dimensional space exhibit close together in the low-dimensional space, edges are deleted to reduce the connection between them. These adjustments help to update their positions in subsequent embeddings, aligning them toward the high-dimensional data. Extensive experiments have demonstrated the superiority of DynoGraph against various comparative algorithms in tasks such as visualization, classification and clustering. © 2024 IEEE.

关键词： Dimensionality reduction

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Analyzing Machine Learning Techniques for Air Passenger Numbers Forecasting

Analyzing Machine Learning Techniques for Air Passenger Numb...

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International Joint Conference on computer Science and Software engineering (JCSSE)

作者： Pornsiri Khumla Kamthorn Sarawan Jantima Polpinij Sarayut Gonwirat Phoori Chantima Worapot Sommool Department of Logistics Engineering and Transportation Technology Faculty of Engineering and Industrial Technology Kalasin University Kalasin Thailand Department of Computer Engineering Faculty of Engineering and Industrial Technology Kalasin University Kalasin Thailand Department of Computer Science Faculty of Informatics Mahasarakham University Mahasarakham Thailand

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

The imperative of forecasting air passenger numbers is underscored by its utility in strategic planning and operational optimization within the aviation sector. As a pivotal technique for predicting continuous data, machine learning methodologies offer a sophisticated avenue for enhancing forecast accuracy. This study embarks on a detailed examination of machine learning approaches to predict air passenger volumes in Thailand, employing a suite of models: Random Forest, XGBoost, Gradient Boosting, LightGBM and CatBoost. Comparative analysis reveals the Gradient Boosting model as the preeminent performer, demonstrating superior forecasting capabilities with an RMSE of 8,081 and a MAPE of 2.57%. These findings underscore the potential of machine learning techniques in refining the precision of air passenger number forecasts, offering significant implications for the planning and management of aviation operations.

关键词： Systematics Reviews Atmospheric modeling Refining Strategic planning Predictive models Boosting

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Leveraging Artificial Intelligence for Active Cyber Defense Against to Advanced Persistent Threats to Homeland Security

Leveraging Artificial Intelligence for Active Cyber Defense ...

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International Symposium on Networks, computers and Communications (ISNCC)

作者： Recep ÖZbay Uraz Yavanoğlu Dept. Of Computer Forensics Graduate School of Informatics Gazi University Ankara Turkey Dept. of Computer Engineering Faculty of Engineering Gazi University Ankara Turkey

ISBN: (数字)9798350364910

ISBN: (纸本)9798350364927

Increasing cyber threats have rendered traditional security solutions such as firewalls and intrusion-detection/prevention systems insufficient in providing adequate protection against advanced persistent threats to homeland security. To enhance protection, the implementation of active cyber defense is recommended, offering alternative yet effective measures against these threats. This paper examines the phases of the cyber kill chain, one of the cyber defense techniques, which has been enhanced to analyze and understand advanced persistent threats. It explains the use of deception, slowdown, and counterattacks for active cyber defense. Additionally, the utilization of artificial intelligence in active cyber defense techniques is discussed. Finally, the paper elucidates how these techniques can be employed within the scope of active cyber defense to enhance security.

关键词： computers Firewalls (computing) US Department of Homeland Security Security Protection Artificial intelligence

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Classification of Medicinal Wild Plants Using Radial Basis Function Neural Network with Least Mean Square 2

Classification of Medicinal Wild Plants Using Radial Basis F...

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2nd International Conference on Electronic and Electrical engineering and Intelligent System, ICE3IS 2022

作者： Borman, Rohmat Indra Rossi, Farli Alamsyah, Dedy Nuraini, Rini Jusman, Yessi Fauziyah Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia Universitas Muhammadiyah Tangerang Faculty of Engineering Tangerang Indonesia Universitas Nasional Jakarta Faculty of Communication Technology and Informatics Selatan Indonesia Universitas Muhammadiyah Yogyakarta Faculty of Engineering Department of Electrical Engineering Yogyakarta Indonesia University of Science and Technology Beijing Faculty of Engineering Beijing China

ISBN: (纸本)9781665465410

Wild plants are considered as plants whose growth interferes with other plants. However, in its development it turns out that wild plants contain ingredients for medicines. However, information and knowledge about wild plant species, especially plants with herbaceous vegetation, needs to be carried out with various technologies, including through digital image processing. This study aims to develop a classification model for medicinal plant species using color and texture extraction using the Radial Basis Function Neural Network (RBFNN) algorithm with a combination of the Least Mean Square (LMS) algorithm. Color feature extraction is obtained by calculating the RGB average value and texture feature extraction using a Gabor filter obtained from the mean, entropy, and variance parameters. The result of feature extraction becomes input for RBFNN with LMS. RBFNN has three layers that have feedforward properties that can assist in solving classification or pattern recognition problems. The LMS algorithm is used for learning or updating neural network weights. Based on the test results by calculating precision, recall, and accuracy, it gets a precision value of 92.50%, recall gets a value of 91.74, accuracy gets a value of 92.08. These results indicate that the developed model can classify wild plant species with medicinal properties well. © 2022 IEEE.

关键词： Gabor filters

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Development of Digital Signature Features Using MVVM Architecture (Case Study of the Moshelp Application by PT Emos Global Digital)

Development of Digital Signature Features Using MVVM Archite...

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International Conference on informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Muchamad Reza Pahlawan Widya Cholil Muhammad Adrezo Didit Widiyanto Kharisma Wiati Gusti Hsing-Chung Chen Informatics Faculty of Computer Science Universitas Pembangunan Nasional Veteran Jakarta Jakarta Indonesia Computer Science and Information Engineering at Asia University Taiwan

ISBN: (数字)9798331533243

ISBN: (纸本)9798331533250

EMOS Global Digital is currently enhancing security across various sectors to meet security certification requirements, including securing digital project submission processes. However, the existing system faces threats to document integrity. A solution to this issue is to update the problematic feature with a more secure one. Specifically, the feature in question is project approval, which can be updated to use digital signatures. Currently, Moshelp utilizes the Model-View-ViewModel (MVVM) architecture, which separates the visual elements from the information flow, allowing for parallel and efficient development. Previous research also shows that this architecture improves application performance and facilitates code modification and maintenance. With this update, the approval, previously represented only by a date, will be changed to a quick response code that verifies the content of a document, thus enhancing document integrity in the project approval process.

关键词： Visualization Codes Software architecture Multimedia systems computer architecture QR codes Maintenance informatics Digital signatures Faces

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Challenges of Integrating Blockchain Technology into Educational Institutions

Challenges of Integrating Blockchain Technology into Educati...

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International Symposium on Networks, computers and Communications (ISNCC)

作者： Abbas Hussein Ali Uraz YavanoğLu Dept. Of Computer Science Graduate School of Informatics Gazi University Ankara Turkey Dept. of Computer Engineering Faculty of Engineering Gazi University Ankara Turkey

ISBN: (数字)9798350364910

ISBN: (纸本)9798350364927

The latest advancements in blockchain technology have significantly influenced several sectors, such as banking, healthcare, and supply chain networks. Because of its distinct attributes, like decentralization, trustworthiness, and security, this technology has recently been employed in the field of education. While the blockchain is bright, several problems, such as legal, immutability, and scalability questions, continue to hinder its adoption in education. In this paper, we look at educational application system in terms of blockchain-based solutions that have been created for educational purposes, as well as the potential challenges of blockchain technology in the field of education.

关键词： computers Law Scalability Education Supply chains Medical services Blockchains Stakeholders Security Guidelines

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