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arXiv 2024年

作者： Vaarandi, Risto Bahşi, Hayretdin Centre for Digital Forensics and Cyber Security Tallinn University of Technology Tallinn Estonia School of Informatics Computing and Cyber Systems Northern Arizona University United States

In modern IT systems and computer networks, realtime and offline event log analysis is a crucial part of cyber security monitoring. In particular, event log analysis techniques are essential for the timely detection of cyber attacks and for assisting security experts with the analysis of past security incidents. The detection of line patterns or templates from unstructured textual event logs has been identified as an important task of event log analysis since detected templates represent event types in the event log and prepare the logs for downstream online or offline security monitoring tasks. During the last two decades, a number of template mining algorithms have been proposed. However, many proposed algorithms rely on traditional data mining techniques, and the usage of Large Language Models (LLMs) has received less attention so far. Also, most approaches that harness LLMs are supervised, and unsupervised LLM-based template mining remains an understudied area. The current paper addresses this research gap and investigates the application of LLMs for unsupervised detection of templates from unstructured security event logs. © 2024, CC BY.

关键词： Cyber attacks

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A Software-Based Low-Latency Elliptic Curve Cryptography Design Over Prime Fields

A Software-Based Low-Latency Elliptic Curve Cryptography Des...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Tuy Tan Nguyen Tram Thi Bao Nguyen School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff AZ USA Faculty of Information Technology Van Hien University HCM City Vietnam

This paper presents an innovative approach that enhances arithmetic operations in elliptic curve cryptography (ECC) over prime fields. By strategically integrating an efficient point multiplication algorithm into ECC, we achieve a substantial acceleration in the speed and efficiency of encryption and decryption processes. This improvement is further amplified by concurrent parallel execution of arithmetic operations, resulting in a significant reduction in encryption and decryption processing times. Empirical evaluations conducted across diverse datasets unequivocally validate the effectiveness of the proposed algorithm, demonstrating a remarkable acceleration of up to 23% and 40% in encryption and decryption operations, respectively. This research not only contributes to the advancement of ECC but also holds profound implications for the broader cryptographic landscape, establishing a compelling foundation for secure data communication and digital privacy.

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Exploring Music Similarity through Siamese CNNs using Triplet Loss on Music Samples

Exploring Music Similarity through Siamese CNNs using Triple...

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International Research Conference on Smart computing and Systems Engineering (SCSE)

作者： Gibran Kasif Ganesha Thondilege School of Computer Science and Engineering University of Westminster London UK Department of Computing Informatics Institute of Technology Colombo Sri Lanka

In the rapidly evolving digital music landscape, identifying similarities between musical pieces is essential to help musicians avoid unintended copyright infringement and maintain the originality of their work. However, detecting such similarities remains a complex and computationally challenging problem. A novel approach to address this issue is a song similarity detection system that utilizes a Siamese Convolutional Neural Network (CNN) with Triplet Loss for effective audio input comparison. The model is trained on a custom dataset from WhoSampled, an extensive database of information on sampled music, cover songs, and remixes. The dataset comprises pairs of audio samples and interpolations, making it suitable for the Siamese CNN approach. Incorporating Triplet Loss enhances the model’s performance by learning discriminative features for improved comparison. The performance of this system is assessed using a confidence interval-based metric, achieving a 96.86% accuracy at a 99.7% confidence level in determining the similarity between music samples. The solution provides a helpful tool for musicians to actively compare their creations with existing songs, helping to reduce the likelihood of unintentional plagiarism and possible legal issues.

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SEAFL: Enhancing Efficiency in Semi-Asynchronous Federated Learning through Adaptive Aggregation and Selective Training

arXiv

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arXiv 2025年

作者： Islam, Md Sirajul Panta, Sanjeev Xu, Fei Yuan, Xu Chen, Li Tzeng, Nian-Feng School of Computing and Informatics University of Louisiana Lafayette United States School of Computer Science and Technology East China Normal University China Department of Computer and Information Sciences University of Delaware United States

Federated Learning (FL) is a promising distributed machine learning framework that allows collaborative learning of a global model across decentralized devices without uploading their local data. However, in real-world FL scenarios, the conventional synchronous FL mechanism suffers from inefficient training caused by slow-speed devices, commonly known as stragglers, especially in heterogeneous communication environments. Though asynchronous FL effectively tackles the efficiency challenge, it induces substantial system overheads and model degradation. Striking for a balance, semi-asynchronous FL has gained increasing attention, while still suffering from the open challenge of stale models, where newly arrived updates are calculated based on outdated weights that easily hurt the convergence of the global model. In this paper, we present SEAFL, a novel FL framework designed to mitigate both the straggler and the stale model challenges in semi-asynchronous FL. SEAFL dynamically assigns weights to uploaded models during aggregation based on their staleness and importance to the current global model. We theoretically analyze the convergence rate of SEAFL and further enhance the training efficiency with an extended variant that allows partial training on slower devices, enabling them to contribute to global aggregation while reducing excessive waiting times. We evaluate the effectiveness of SEAFL through extensive experiments on three benchmark datasets. The experimental results demonstrate that SEAFL outperforms its closest counterpart by up to ∼22% in terms of the wall-clock training time required to achieve target accuracy. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Beyond the Screen: A Blueprint for Student Resilience—Essential Features in Mental Health Application 4th

Beyond the Screen: A Blueprint for Student Resilience—Essen...

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4th International Conference on Advances in Computational Science and Engineering, ICACSE 2023

作者： Baharum, Aslina Ismail, Rozita Abdul Rahim, Emelia Halamy, Suhaida Mat Noor, Noorsidi Aizuddin Ramli, Nur Ainna Department of Computing and Information Systems School of Engineering and Technology Sunway University 5 Jalan Universiti Selangor Petaling Jaya Bandar Sunway47500 Malaysia College of Computing and Informatics Universiti Tenaga Nasional Jalan IKRAM-UNITEN Putrajaya Campus Selangor Kajang43000 Malaysia Universiti Teknologi MARA Cawangan Sarawak Sarawak Kota Samarahan94300 Malaysia School of Information Science College of Computing Informatics and Mathematics Universiti Teknologi MARA Cawangan Sarawak Sarawak Kota Samarahan94300 Malaysia Mass Appraisal Housing and Planning Research Group Centre for Real Estate Studies Real Estate Department Universiti Teknologi Malaysia Johor Johor Bahru81310 Malaysia University Sains Islam Malaysia Negeri Sembilan Bandar Baru Nilai71800 Malaysia

ISBN: (纸本)9789819729760

It is essential to have a mobile app with features that enhance mental health and well-being to promote improved mental health outcomes and make mental health resources available to students. This study aims to determine which mental health mobile app features students require for optimal mental health management. A questionnaire survey was distributed regarding mental health state in the previous and most recent months throughout the study and the primary and general features of mental health applications. In addition, an open-ended question was posed to provide further insight into students’ required features. According to the findings, most students agreed with prior studies reviewed and proposed mental health mobile app features. Responses to the open-ended questions indicate that students desire apps with gamification elements, customizability, and high privacy. For better mental health management, a mobile app for mental health should be resourceful and exciting so that students continue to use it over the long term. This study will inform app designers and developers on how to develop a mobile app for mental health that is suitable for students. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electronic health record

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Mixed-traffic Intersection Management using Traffic-load-responsive Reservation and V2X -enabled Speed Coordination

Mixed-traffic Intersection Management using Traffic-load-res...

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International Conference on Intelligent Transportation

作者： Nicholaus D. Yosodipuro Ehsan Javanmardi Jin Nakazato Yasumasa Tamura Xavier Défago Manabu Tsukada Department of Creative Informatics Graduate School of Information Science and Technology The University of Tokyo Tokyo Japan Department of Computer Science School of Computing Tokyo Institute of Technology Tokyo Japan

Vehicle-to-everything (V2X) communication enables connected autonomous vehicles (CAVs) to share information and generate optimal decisions. The networking abilities of CAVs have led to the development of unsignalized autonomous intersection management (AIM) methods that leverage CAVs to significantly improve traffic flows. However, AIM methods assume 100% CAV market penetration, which is currently unrealistic owing to the gradual adoption of CAVs. Therefore, CAVs must share road usage with nonconnected vehicles (NCVs). Thus, we propose a mixed-traffic intersection management method that considers NCVs while ensuring high traffic flow, called traffic-load-responsive reservation for intersection management (TLRRIM). In TLRRIM, the roadside unit (RSU) first classifies vehicles and groups them into clusters before selecting a reservation cluster to cross an intersection. The reservation cluster selection considers both traffic load and crossing urgency. In addition, the RSU utilizes V2X-enabled speed coordination (VESC) for CAVs within the reservation cluster to further improve traffic flow, while utilizing traffic lights to guide NCVs. Simulation-based experiments using OpenCDA and CARLA showed that TLRRIM can increase throughput and reduce waiting time by up to 89.63% and 60.71 %, respectively, compared with the fixed-time signaling method. Moreover, adding VESC can increase throughput by 12.21 % and reduce waiting time by 10.80%, further enhancing traffic flow.

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Color-Name Aware Optimization to Enhance the Perception of Transparent Overlapped Charts

arXiv

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arXiv 2024年

作者： Lu, Kecheng Zhu, Lihang Wang, Yunhai Zeng, Qiong Song, Weitao Reda, Khairi The School of Information Renmin University of China China The School of Computer Science and Technology Shandong University China The School of Optics and Photonics Zhengzhou Research Institute Beijing Institute of Technology China The Luddy School of Informatics & Computing Indiana University Indianapolis United States

Transparency is commonly utilized in visualizations to overlay color-coded histograms or sets, thereby facilitating the visual comparison of categorical data. However, these charts often suffer from significant overlap between objects, resulting in substantial color interactions. Existing color blending models struggle in these scenarios, frequently leading to ambiguous color mappings and the introduction of false colors. To address these challenges, we propose an automated approach for generating optimal color encodings to enhance the perception of translucent charts. Our method harnesses color nameability to maximize the association between composite colors and their respective class labels. We introduce a color-name aware (CNA) optimization framework that generates maximally coherent color assignments and transparency settings while ensuring perceptual discriminability for all segments in the visualization. We demonstrate the effectiveness of our technique through crowdsourced experiments with composite histograms, showing how our technique can significantly outperform both standard and visualization-specific color blending models. Furthermore, we illustrate how our approach can be generalized to other visualizations, including parallel coordinates and Venn diagrams. We provide an open-source implementation of our technique as a web-based tool. Copyright © 2024, The Authors. All rights reserved.

关键词： Simulated annealing

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An Adaptive Update Algorithm for Fingerprint Database Based on Validity Detection 6

An Adaptive Update Algorithm for Fingerprint Database Based ...

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6th International Conference on Automation, Control and Robots, ICACR 2022

作者： Zhang, Chenxin Chen, Xin Xu, Xueting Peng, Ao Xiamen University The National Joint Engineering Center of Navigation and Location-Based-Service Technology Xiamen China Xi'an Jiaotong-Liverpool University School of Ai and Advanced Computing Suzhou China Xiamen University School of Informatics Xiamen China

ISBN: (数字)9781665498739

ISBN: (纸本)9781665498739

Maintaining the accuracy of fingerprint-based indoor positioning requires ensuring the validity of the fingerprint database. The main task of fingerprint database maintenance is to detect and update invalid data in a timely manner. In this paper, we address the problem of invalid fingerprint data with the fingerprint matching effectiveness evaluation method and the adaptive update algorithm of fingerprint database. The experimental design and field data collection and analysis were conducted in two typical experimental scenarios: an indoor basketball court and an indoor school building. Experiments with real measurements show that the proposed algorithm improves the performance of updating the fingerprint database in real time and robustness. © 2022 IEEE.

关键词： Database systems

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Machine Learning Model for Predicting Community Integrity Towards Solid Waste Management and Environmental Care 5

Machine Learning Model for Predicting Community Integrity To...

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5th International Conference on Artificial Intelligence and Data Sciences, AiDAS 2024

作者： Zulkipli, Faridah Jamian, Nurul Husna Masrom, Suraya Khalid, Khairul Irwan Nopiah, Zulkifli Mohd Kerdprasop, Nittaya College of Computing Informatics and Mathematics Universiti Teknologi Mara Perak Branch Tapah CampusTapah Road Malaysia Block J MKN Embassy Techzone Jalan Teknokrat 2 Cyberjaya Malaysia Universiti Kebangsaan Malaysia Faculty of Engineering and Built Environment Selangor Malaysia School of Computer Engineering Suranaree University of Technology Thailand

ISBN: (纸本)9798331528553

Solid waste management and environmental conservation are global concerns that play a crucial role in both developed and developing countries. This worldwide issue makes a substantial contribution to the achievement of sustainable development goals and, as a result, helps to create a brighter future for a sustainable environment. The primary objective of this project is to create a machine learning model that can accurately assess and monitor the level of community integrity in relation to the management of solid waste and environmental conservation. Community integrity was used as a dependent variable (predictor variable). The independent factors considered in this study include gender, cumulative grade point average (CGPA), residence status, awareness, practice, attitude, perception, and knowledge. Five machine learning algorithms were utilized to determine the optimal prediction model for assessing community integrity in solid waste management and environmental care. The models used were Generalized Linear Model, Random Forest, Decision Tree, Gradient Boosted Tree, and Support Vector Model. The Generalized Linear Model machine learning algorithm was identified as the most appropriate model, based on its ability to predict community integrity in solid waste management and environmental care, as indicated by its lowest Root Mean Square Error (RMSE). © 2024 IEEE.

关键词： Adversarial machine learning

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Training Learning Weights of Elman Neural Network Using Salp Swarm Optimization Algorithm 27

Training Learning Weights of Elman Neural Network Using Salp...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Mahdi, Qaedah Ali Musaeed Naji Ali, Maria Atta, Muhammad Nouman Khan, Abdullah Lashari, Saima Anwar Ramli, Dzati Athiar Institute of Computer Science and Information Technology The University of Agriculture Peshawar Pakistan College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia School of Electrical and Electronic Engineering University Sains Malaysia USM Engineering Campus Pulau Pinang Nibong Tebal14300 Malaysia

Elman Neural Network (ENN) is one of the most common type of recurrent neural network (RNN). It is frequently applied to numerous applications such as classification and predication. However, the ENN model, suffers from serious flaws like network stagnancy and delayed convergence during training. To enhance the effectiveness of the ENN and address the mention problems, numerous studies have been conducted to speed up the learning process of ENN. Many optimization techniques are being used and each algorithm has its own characteristics in terms of efficiency. Some of the algorithms inspire from nature, while others come from the swarm's combined behaviour. Different numbers of optimization methods, including Particle Swarm Optimization (PSO), Bat Algorithm, and Cuckoo Search (CS) are applied to improve the ENN's learning process. As a result, this study suggested using Salp Swarm algorithm (SSA) to address these issues and speed up the ENN algorithm's learning process. The SSA, which is based on the typical behaviour of Salp swarms, is proposed in this research as a novel optimization technique. Due to its effectiveness and Salp intelligent behaviour, a novel Salp algorithm improves the learning process of the artificial neural network (ANN), and ENN models. Therefore this study proposed Salp swarm artificial neural network (SSANN) and Salp swarm Elman neural network (SSElmanNN). Performance of the suggested models is evaluated against ANN, back propagation neural network (BPNN), along with ENN in term of accuracy and Mean Square Error (MSE). Two datasets such as IRIS and Credit Card are used for simulation. The simulation results demonstrate that the suggested models outperform the other algorithms used in this study in terms of MSE and accuracy. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： and Credit Card ANN BPNN ENN Iris MSE SSA

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