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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Trisandhya, Pidugu Vashishtha, Srishti Hasija, Udit Dadhwal, Himanshu Qureshi, Reeba Bharati Vidyapeeth's College of Engineering Department of Applied Science New Delhi India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9798350375237

This study acquaints one with an encyclopaedic approach of the machine learning field which is distinctively designed for the identification of the risk imported by the two trenchant and widespread malwares, spyware and ransomware. With spyware and ransomware posing substantial threats to both individuals and businesses worldwide, recent reports underscore the urgency to solve these problems on an urgent basis. Check Point Research's findings reveal a 55.5% surge in attempted ransomware attacks compared to the previous year, locking the target over 1 in 10 organizations globally. Additionally, the Positive Technologies report for Q3 2023 highlights a 65% increase in spyware attacks on individuals compared to the prior quarter. This underscores the critical need for robust detection mechanisms. Our study aims to address this challenge by exploring multiple innovative approaches to malware detection. In our research, we found that Support Vector Machine (SVM) and XGBoost demonstrate the best performance, with accuracies of 99.7% and 99.6% respectively. We seek to further improve cybersecurity practices in the digital age by showcasing the scalability and efficacy of our suggested strategy in practical settings through experimental evaluation and analysis. © 2024 IEEE.

关键词： Malware

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Comparative Analysis of Memory-Based Collaborative Filtering and Deep Learning Models for Resolving Cold Start and Data Sparsity Issues in E-commerce Recommender Systems 3

Comparative Analysis of Memory-Based Collaborative Filtering...

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3rd IEEE World Conference on applied Intelligence and Computing, AIC 2024

作者： Shetty, Amrithkala M Manjaiah, D.H. Aljunid, Mohammed Fadhel Yogesh, K.M. Mangalore University Department of Computer Science Karnataka India Thamar University Computer and Informatics Center Thamar Yemen M S Ramaiah University of Applied Sciences Department of Computer Science and Engineering India

ISBN: (纸本)9798350384598

In today's digital landscape, where online activities and e-commerce play pivotal roles, Recommender Systems (RS) have become indispensable tools for enhancing user experiences by providing personalized recommendations. However, collaborative filtering (CF) algorithms often face challenges such as the cold start problem and data sparsity. This work addresses these challenges using memory-based CF approaches such as Singular Value Decomposition (SVD), SVD++, K-Nearest Neighbors (KNN) Baseline, and K-Nearest Neighbors (KNN) Basic. Hyperparameters for these algorithms are optimized using GridSearchCV with cross-validation values set to 5, 10, and 15. Additionally, Deep Learning (DL) models including LSTM, BiLSTM, Att+BiLSTM, User-Product Fusion (UPF), and Deep Collaborative Filtering (DCF) are explored. The BiLSTM model achieves a lower Root Mean Squared Error (RMSE) of 1.1224, while the DCF model achieves a Mean Absolute Error (MAE) of 0.6897. Among the evaluated models, SVD++ achieves an RMSE of 0.9881 and an MAE of 0.7003 with cross-validation set to 15, indicating superior predictive accuracy. The best performance is observed with cross-validation set to 15. These results highlight the effectiveness of optimized memory-based CF algorithms in overcoming the cold start and data sparsity issues, leading to more accurate and reliable recommendations. The findings suggest that memory-based CF algorithms, specifically SVD++, can significantly enhance the development of robust recommender systems, ultimately improving user experience in e-commerce and other online platforms. © 2024 IEEE.

关键词： Collaborative filtering

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Enhancing Data Security and Prioritization through Hop-Count Based Forwarding in Vehicular Named Data Networks

Enhancing Data Security and Prioritization through Hop-Count...

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2024 IEEE International Conference on Vehicular Technology and Transportation Systems, ICVTTS 2024

作者： Nithin Rao, R. Gayathri Bai, S. M S Ramaiah University of Applied Sciences Department of Computer Science and Engineering Bengaluru India Maharani Lakshmi Ammanni College Autonomous Department of Computer Science Bengaluru India

ISBN: (数字)9798350355512

ISBN: (纸本)9798350355512

The paper introduces Application Oriented Secured Content Forwarding (ASCF) mechanism for Vehicular Named Data Networks (VNDN). ASCF enhances critical data delivery, segregates data by application importance, and ensures security. It employs a push-based mechanism for swift critical data dissemination and categorizes data into Non-Critical and Critical, optimizing forwarding based on importance. The Replay and Timing Attack Mitigation (RTAM) scheme identifies and counters malicious nodes. ASCF also utilizes a Hop Count based Interest Selection and Content Forwarding (HISCF) algorithm, reducing congestion. Results exhibit significant improvements in throughput, latency, and security over traditional VNDN. ASCF presents a robust framework for advanced content forwarding in VNDNs, promising efficient data dissemination for critical applications in future smart transportation systems. © 2024 IEEE.

关键词： Data communication systems

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A Machine Learning-Based Approach for Network Optimization in WSNs

A Machine Learning-Based Approach for Network Optimization i...

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International Conference on Data science, Computation and Security, IDSCS 2023

作者： Bhattad, Bharti Sethi, Kamal Kumar Pal Singh Rathore, Narendra Bhanodia, Praveen Mishra, Durgesh Kumar Computer Science Engineering Acropolis Institute of Technology and Research Indore India School of Computer Science and Information Technology Symbiosis University of Applied Sciences Madhya Pradesh Indore India

ISBN: (纸本)9789819709748

With the advancements in interconnected devices and automation, wireless sensor networks have gained substantial importance. Energy constraint is one of the main issues that wireless sensor networks (WSNs) must deal with. Moreover, to enhance the effectiveness, it is necessary to reduce the latency as much as possible. Wireless sensor networks can be optimized for latency and energy consumption using a variety of protocols, with their own pros and cons. This paper investigates different aspects related to wireless sensor networks along with the relevant approaches. The intent of this paper is to present a reinforcement learning-based approach for optimized routing in WSNs. The protocol is designed to cater to the constraints of limited energy and latency, with the evaluation of the proposed algorithm in terms of network lifetime and latency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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The Lawn Mowing Problem: From Algebra to Algorithms 31

The Lawn Mowing Problem: From Algebra to Algorithms

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31st Annual European Symposium on Algorithms, ESA 2023

作者： Fekete, Sándor P. Krupke, Dominik Perk, Michael Rieck, Christian Scheffer, Christian Department of Computer Science TU Braunschweig Germany Faculty of Electrical Engineering and Computer Science Bochum University of Applied Sciences Bochum Germany

ISBN: (纸本)9783959772952

For a given polygonal region P, the Lawn Mowing Problem (LMP) asks for a shortest tour T that gets within Euclidean distance 1/2 of every point in P;this is equivalent to computing a shortest tour for a unit-diameter cutter C that covers all of P. As a generalization of the Traveling Salesman Problem, the LMP is NP-hard;unlike the discrete TSP, however, the LMP has defied efforts to achieve exact solutions, due to its combination of combinatorial complexity with continuous geometry. We provide a number of new contributions that provide insights into the involved difficulties, as well as positive results that enable both theoretical and practical progress. (1) We show that the LMP is algebraically hard: it is not solvable by radicals over the field of rationals, even for the simple case in which P is a 2 × 2 square. This implies that it is impossible to compute exact optimal solutions under models of computation that rely on elementary arithmetic operations and the extraction of kth roots, and explains the perceived practical difficulty. (2) We exploit this algebraic analysis for the natural class of polygons with axis-parallel edges and integer vertices (i.e., polyominoes), highlighting the relevance of turn-cost minimization for Lawn Mowing tours, and leading to a general construction method for feasible tours. (3) We show that this construction method achieves theoretical worst-case guarantees that improve previous approximation factors for polyominoes. (4) We demonstrate the practical usefulness beyond polyominoes by performing an extensive practical study on a spectrum of more general benchmark polygons: We obtain solutions that are better than the previous best values by Fekete et al., for instance sizes up to 20 times larger. © Sándor P. Fekete, Dominik Krupke, Michael Perk, Christian Rieck, and Christian Scheffer.

关键词： Approximation algorithms

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Enhancing 6G mmWave Beam Prediction in V2I with Class Imbalance Mitigation 35

Enhancing 6G mmWave Beam Prediction in V2I with Class Imbala...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Makadia, Omikumar B. Patel, Dhaval K. Raval, Mehul S. Zaveri, Mukesh Merchant, S.N. Ahmedabad University School of Engineering and Applied Science India Svnit Department of Computer Science and Engineering Gujarat Surat India Iit Bombay Department of Electrical Engineering Powai Mumbai India

ISBN: (纸本)9798350362244

The increase in the use of autonomous vehicles has motivated a paradigm shift in the transportation domain as it redefines the boundaries of urban mobility by augmenting safety measures. This research paper explores an innovative approach to enhance 6G millimeter-wave (mmWave) beam prediction for vehicle-to-infrastructure (V2I) communications by using generative adversarial networks (GANs). By generating synthetic data samples effectively and balancing the real-world dataset, we improve the accuracy of beam prediction models significantly. Our proposed method of training random forests on synthetic data (RFGAN) to predict beam indices provides the solution for imbalanced class issues and significantly improves the predictive performance of mmWave beam selection, contributing to more reliable and efficient V2I communications. This work also performs comparative analysis with state-of-the-art models in top-K evaluation metrics, average power loss, and overhead savings related to adapting to the new approach. © 2024 IEEE.

关键词： Generative adversarial networks

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An Energy Efficient Clustering for Underwater Acoustic Sensor Networks using 6G Technology 5

An Energy Efficient Clustering for Underwater Acoustic Senso...

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5th IEEE International Conference on Recent Trends in computer science and Technology, ICRTCST 2024

作者： Siddiqui, Shams Tabrez Hashmi, Syed Ghyasuddin Ali, Mohammed Maqsood Syed, Ziauddin Qidwai, Khalid Ali Salim, Abu Jazan University College of Engineering and Computer Science Department of Computer Science Jazan Saudi Arabia Applied College Jazan University Department of Business Administration Jazan Saudi Arabia

ISBN: (纸本)9798350351378

These underwater Acoustic Sensor Networks (UWSNs) present unique challenges due to the harsh and dynamic underwater environment, making efficient communication protocols essential for their operation. This paper introduces an innovative Energy Efficient Clustering Routing Protocol (EECRP) specifically designed for UWSNs, leveraging the advancements of 6G technology. EECRP employs a distributed clustering mechanism and energy-efficient routing strategy to optimize energy consumption, prolong network lifetime, and enhance communication reliability. The protocol integrates with 6G technologies, such as terahertz communication and quantum communication security, to further enhance performance and security in UWSNs. Through simulation and comparative analysis, EECRP demonstrates superior performance compared to existing routing protocols in terms of energy efficiency, throughput, and scalability. EECRP's contributions lie in its novel approach to routing in UWSNs, emphasizing energy efficiency, sustainability, and integration with advanced technologies. This research paves the way for the adoption and deployment of EECRP in various underwater applications, facilitating scientific research, environmental monitoring, and technological innovation in underwater communication systems. © 2024 IEEE.

关键词： Scalability

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Millimeter- Wave Vehicle-to-Infrastructure Communications for Autonomous Vehicles: Location-Aided Beam Forecasting in 6G 16

Millimeter- Wave Vehicle-to-Infrastructure Communications fo...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Makadia, Omikumar B. Patel, Dhaval K. Shah, Kashish D. Raval, Mehul S. Zaveri, Mukesh Merchant, S.N. School of Engineering and Applied Science Ahmedabad University India Svnit Department of Computer Science and Engineering Gujarat Surat India Iit Bombay Department of Electrical Engineering Mumbai Powai India

ISBN: (纸本)9798350383119

The rise of autonomous vehicles has generated substantial interest due to their potential to transform transportation, enhance safety, and reduce traffic congestion in urban areas. This research investigates strategies to facilitate communication among various elements in driving environments, such as vehicles, pedestrians, and emergency responders, primarily focusing on vehicle-to-infrastructure (V2I) communication. Furthermore, advancements in wireless technology, including 6G and millimetre waves, offer improved connectivity but face challenges in signal propagation. The study emphasizes the importance of beam-forming as a solution, exploring alternative, resource-efficient methods to predict optimal signal directions using user positions. Positive simulation results suggest real-world applicability, and the study addresses critical questions about utilizing future positions and their impact on overhead complexity. This research provides valuable insights into position-based approaches for enhancing wireless communication in autonomous driving scenarios. © 2024 IEEE.

关键词： 6G Autonomous vehicles beam prediction overhead complexity training overhead

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Deep RL-Based Smart Signaling Using Space-Time Vehicular Features Under C-V2X Scenario 16

Deep RL-Based Smart Signaling Using Space-Time Vehicular Fea...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Shah, Kashish D. Patel, Dhaval K. Raval, Mehul S. Zaveri, Mukesh Merchant, S.N. School of Engineering and Applied Science Ahmedabad University India Svnit Department of Computer Science and Engineering Gujarat Surat India LIT Bombay Department of Electrical Engineering Mumbai Powai India

ISBN: (纸本)9798350383119

As of recent, the majority of developing countries have faced traffic congestion issues. As a result, vehicular communication technologies such as DSRC and Cellular-V2X are standardized by the 3GPP to support the transportation community. The C-V2X standard proposed in 3GPP Re114 provides high data transmission facilities for V2V and V2I communication. In this paper, we attempt to solve the traffic congestion problem by considering the V2I communication facility as per the C-V2X standard. We propose an online learning strategy based on Deep Reinforcement Learning for smart signaling to avoid traffic congestion at the intersections. These are the key aspects to address the congestion issue. We conduct extensive near-real-time simulations in PTV VISSIM considering the Swastik Cross Road intersection at Ahmedabad, Gujarat, India, imported via OpenStreetMap. With the proposed smart signaling strategy, we found significant improvement in vehicles' average travel time and average space occupancy compared to existing traffic signaling methods. Uplifting this research and implementing such real-time strategies will significantly impact the current transportation system. © 2024 IEEE.

关键词： Cellular-V2X Deep Reinforcement Learning Intelligent Transportation Systems Smart Signaling Vehicular Communication

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LSTM-XGBoost: An Ensemble Model for Blood Demand Distribution Forecasting – A Case Study in Zakho City, Kurdistan Region, Iraq

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Operations Research Forum 2025年第1期6卷 1-22页

作者： Zebari, Rizgar R. Zebari, Gheyath M. Al-zebari, Adel Mohammed, Marwan Aziz Department of Computer Science College of Science Knowledge University Erbil 44001 Iraq Department of Information Technology Technical College of Informatics Akre University for Applied Sciences Kurdistan Akre Iraq Department of Computer Engineering College of Engineering Knowledge University Erbil 44001 Iraq

A safe and adequate blood supply is essential for healthcare systems to function effectively. Accurately forecasting blood demand plays a key role in efficient inventory management and resource allocation. Traditional forecasting methods, like moving averages and ARIMA models, often fall short due to the complexity of factors influencing blood demand. This study introduces an innovative hybrid ensemble model that combines Long Short-Term Memory (LSTM) networks with XGBoost, harnessing their combined strengths to enhance forecasting accuracy. By analyzing blood donation data from the Zakho Blood Bank (January 1, 2015—July 22, 2022), the model outperforms individual LSTM and XGBoost models, excelling in metrics such as Mean Square Error (MSE) and Mean Absolute Error (MAE). These findings underscore the potential of advanced machine learning techniques to improve healthcare supply chain management and ensure the timely availability of critical blood supplies. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Blood banking Blood demand forecasting Healthcare supply chain LSTM network Machine learning XGBoost

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