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An adaptive Particle Swarm Optimization to Solve Modern Security System 7

An adaptive Particle Swarm Optimization to Solve Modern Secu...

引用

7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Ahmed, Mohammed H. Hashim, Asaad Noori Hussein, Khalid A. Mustansiriyah University Dept. Computer Science College of Education Baghdad Iraq University of Kufa College of Computer Science and Math Dept. Computer Science Najaf Iraq

ISBN: (纸本)9798350383065

Nonlinear stream ciphers hold a vital position in ensuring confidentiality within contemporary cryptographic systems. In this context, we interduce a new approach known as the Adaptive Three Sub-Swarm PSO based on Regrouping Strategy (ATSPSO-RG) to enhance the effectiveness of deciphering nonlinear stream ciphers, particularly those based on the Bruer generator system. Our method significantly reduces the complexity of cipher-only attacks, making the extraction of the correct secret key more efficient. The proposed system dividing the population size into three distinct subswarm, each incorporating its evolving strategies, namely the single score, four score, and nine score strategies. In the single score strategy, we employed, the traditional PSO evolving process. In the four scores strategy, we used three additional scores in addition to the single score of the traditional PSO to update the velocity and position of particles. lastly, the nine score strategy employed five scores to update the velocity and position, as well as the four scores of the previous score. To counteract the issue of premature convergence, we employ a regrouping strategy that periodically reinitializes the population. Experimental results vividly demonstrate the success of the ATSPSO-RG method in deciphering the Bruer cipher, even when dealing with a varying number of Linear Feedback Shift Registers (LFSRs) and polynomial degrees, spanning up to 15, and secret initial values extending up to 39. The method proves its efficiency in cryptanalysis, offering a potent solution for nonlinear stream ciphers with substantial key sizes while ensuring a reasonable timeframe for analysis. The research outcomes underscore the efficacy of ATSPSO-RG, highlighting both its accuracy and time efficiency in deciphering nonlinear stream ciphers. © 2023 IEEE.

关键词： Efficiency

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Detection Systems for Distributed Denial-of-Service (DDoS) Attack Based on Time Series: A Review 21

Detection Systems for Distributed Denial-of-Service (DDoS) A...

引用

21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Adil Nafea, Ahmed Maad Hamdi, Mustafa Abdulhakeem, Baraa Saad Shakir, Ahmed Thair Alsumaidaie, Mustafa S. Ibrahim Shaban, Ali Muwafaq College of Computer Science and Information Technology University of Anbar Ramadi Dept. Artificail Inteligence Iraq College of Computer Science and IT University of Anbar Ramadi Dept. of Computer Science Iraq College of Computer Science and IT University of Anbar Ramadi Dept. of Information System Iraq College of Applied Sciences University of Anbar Ramadi Dept. of Biophysics Iraq College of Computer Science and Information Technology University of Anbar Ramadi Dept. Information System Iraq

ISBN: (纸本)9798350374131

The Distributed Denial-of-Service (DDoS) attacks are one of the most critical threats to the stability and security of the Internet. With the increasing number of devices connected to the Internet, the frequency and severity of DDoS attacks are also increasing. To mitigate the impact of DDoS attacks, intelligent detection systems are becoming increasingly important. This paper reviews the recent literature on intelligent techniques, including machine learning (ML), Deep Learning (DL), and artificial intelligence (AI), for detecting DDoS attacks. We will provide an overview of the existing research in the field and analyse the trends in using time series data analysis for DDoS attack detection. A taxonomy and conceptual framework for DDoS mitigation are presented. This study highlights the use of several intelligent techniques for detecting DDoS attacks and evaluates the performance utilizing real datasets and also discusses future research directions in this field. © 2024 IEEE.

关键词： Time series analysis

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Content Based Classification of Short Messages using Recurrent Neural Networks in NLP

Content Based Classification of Short Messages using Recurre...

引用

2024 International Conference on Artificial Intelligence, computer, Data sciences, and Applications, ACDSA 2024

作者： Bennet, Deepthi Tabitha Bennet, Preethi Samantha Thiagarajan, Priya Sundarakantham, K. Thiagarajar College of Engineering Dept. of Applied Mathematics and Computational Science Madurai India Thiagarajar College of Engineering Dept. of Computer Science and Business Systems Madurai India Thiagarajar College of Engineering Dept. of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350394528

Short message spam poses a significant threat for all mobile phone users, as it can act as an efficient tool for cyberattacks including spreading malware and phishing. Traditional anti-spam measures are only minimally effective against modern spammers. Intelligent analysis of the content to categorise the messages as Spam (unwanted and unsolicited) or Ham (useful messages) is therefore essential to safeguard the user from such attacks. Various artificial intelligence (AI) techniques are proving to be useful in the analysis of the content of such short messages to classify and filter spam. We have trained, validated and tested seven such AI techniques on the SMS spam collection dataset to identify the best model for designing and developing a content based classification system. Recurrent Neural Networks (RNN) have shown the highest performance metrics (Test Accuracy: 99.28%) and hence our proposed system includes a RNN model for classification. A web app of this system has also been deployed where a single SMS can be input and the designed system can classify it as Spam or Ham. The designed system is compared against existing systems and is found to be significantly better. © 2024 IEEE.

关键词： Recurrent neural networks

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Data driven Approaches for Demand Forecasting in Supply Chain for Business Decisions 5

Data driven Approaches for Demand Forecasting in Supply Chai...

引用

5th International Conference on Advancements in Computational sciences, ICACS 2024

作者： Javed, Salman Akhtar, Rizwan Pak-Austria Fachhochschule Institute of Applied Sciences and Technology Dept. It and Computer Science Haripur Pakistan

ISBN: (纸本)9798350394764

Demand forecasting is viewed as a complex challenge for industries as it relies heavily on various factors. The Supply Chain Management (SCM) integrates processes vital for corporate environments across industries. This paper presents a comprehensive data-driven framework for sales demand forecasting in SCM aiming to enhance accuracy of forecasts. Using Rossmann Stores data case study the sales are predicted up to 6 weeks ahead with a comparative analysis conducted for long-term predictions. Multi-dimensional data complexity is addressed by identifying relevant features and selecting appropriate algorithms. The proposed framework utilizes four data-driven machine learning algorithms: Extreme Learning Machine (ELM), Decision Tree (DT), Random Forests (RF), and Extreme Gradient Boosting (XGBoost) aiming to construct a high-precision sales demand forecasting model. The experimental validation using Rossmann Stores data demonstrates the effectiveness of the proposed framework, highlighting its significance in improving the accuracy of forecasts in SCM and enhancing decision-making processes. © 2024 IEEE.

关键词： Forecasting

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Semi-Stable Periodic Orbits of the Deterministic Chaotic Systems Designed by means of Genetic Programming 13

Semi-Stable Periodic Orbits of the Deterministic Chaotic Sys...

引用

13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Matousek, Radomil Hulka, Tomas Lozi, René Pierre Kudela, Jakub Brno University of Technology Dept. of Applied Computer Science Brno Czech Republic Nice France

ISBN: (纸本)9798350308365

The aim of this paper is to show the possibility of generating general semi-stable periodic orbits using genetic programming (GP). This concept is a GP design of a perturbation sequence that forces a defined dynamical system to behave periodically. Recall that periodic orbits in deterministic chaotic systems are trajectories along which the system moves at regular intervals. Despite the chaotic nature of these systems, periodic orbits represent the repetition of certain states of the system over time. In chaotic systems, these orbits are usually surrounded by complex, irregular trajectories, but are themselves defined by regularity and predictability. We should add that periodic orbits are important to chaos theory because they provide a basis for understanding the internal structure of chaotic systems. Although chaos is defined by unpredictability based on initial conditions and the complexity, these periodic orbits represent islands of predictability that can be analyzed and modeled. GP and its symbolic regression capability is an ideal tool for finding both stable periodic orbits defined by stable points and general periodic orbits that rebuild attractive periodic states for the system. The objective function is also crucial for finding periodic orbits using GP. This function has been designed to achieve stable regions as well as the possibility of choosing the degree of the orbital. The test problem will consist of four systems of deterministic chaos, the so-called chaotic maps - the logistic map, the Henon map, the Lozi map and the Burgers map. © 2024 IEEE.

关键词： Genetic programming

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IoT-Enabled Remote-Controlled Raft for Enhanced Water Quality Assessment 20

IoT-Enabled Remote-Controlled Raft for Enhanced Water Qualit...

引用

20th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2024

作者： Surasak, Thattapon Kitchat, Kotcharat Jiteurtragool, Nattagit King Mongkut's University Of Technology North Bangkok Faculty Of Applied Science Dept. Of Computer And Information Science Bangkok Thailand National Central University Dept. Of Computer Science And Information Engineering Taoyuan Taiwan

ISBN: (纸本)9798350382310

Water is vital for all life, serving as the foundation for plants and aquatic creatures and promoting abundance among all living beings. This study introduces an innovative approach by combining the Internet of Things (IoT) with advanced sensing techniques to assess water quality. The IoT-powered water quality testing raft has been developed to significantly enhance the monitoring capabilities. Equipped with sensors measuring key parameters such as turbidity, pH, and temperature, the raft provides a comprehensive understanding of water quality. Its GPS module enables real-time tracking and maintains location history, ensuring precise sampling. The collected data undergoes robust processing by a Raspberry Pi, ensuring integrity before being showcased on the Thingsboard. The system's uniqueness lies in its capacity to promptly issue alerts via LINE Notify upon detecting water quality abnormalities, ensuring timely intervention. This IoT-powered raft represents a paradigm shift in water quality assessment, characterised by its precision, remote capabilities, and user-focused approach. Practical applications have been demonstrated through real-world testing in a private lake in Cha-choengsao province, Thailand. The seamless integration of IoT into environmental monitoring marks a significant advancement in ensuring water quality and ecosystem health. © 2024 IEEE.

关键词： Internet of things

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Thermal Model Validation of a Power Electronics Module for Digital Twin Application

Thermal Model Validation of a Power Electronics Module for D...

引用

2024 Energy Conversion Congress and Expo Europe, ECCE Europe 2024

作者： Zoller, Svenja Cabre-Piqueras, Claudia Schilder, Boris Filba-Martinez, Alber Acker, Lukas Frankfurt University of Applied Science Faculty of Computer Science and Engineering Frankfurt Germany Power Systems Dept. Barcelona Spain

ISBN: (纸本)9798350364446

Thermal stress is a critical failure factor for power electronics components. Hence, when developing a precise digital twin for a power system, it is imperative that thermal models provide a reliable portrayal of the component physics. In that regard, manufacturers offer simplified thermal models that enable designers and users to estimate its behaviour. Nevertheless, the accuracy of these models remains unknown, thus compromising the overall certainty of the digital twin. Therefore, it is of interest to discern whether these simplified models are sufficient for the precision required by the digital twin, and to assess the need of developing a more accurate model. Hence, this paper focuses on validating the theoretical thermal model of a power electronics switch module setup by using the deviation of the module mean time to failure (MTTF) as the acceptance parameter. To achieve this, a novel experimental validation methodology is proposed to retrieve an experimental thermal model and compare it to the theoretical one. This validation is performed considering the discrete thermal impedances and introducing the study of the coupling effects within the module. © 2024 IEEE.

关键词： Insulated gate bipolar transistors (IGBT)

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Human Body Action Recognition based on 3D Feature Fusion over RGB Scenes 11

Human Body Action Recognition based on 3D Feature Fusion ove...

引用

11th International Conference on Information Technology, ICIT 2023

作者： Nsour, Heba Mustafa, Zaid Al-Sendah, Maria Al-Balqa Applied University Dept. of Computer Science Albalqa Jordan Al-Balqa Applied University Dept. of Computer Information Systems Albalqa Jordan Kuwait

ISBN: (纸本)9798350320060

Human body action recognition (HBAR) is an important area of research in machine learning and image processing due to its vast range of applications. Similarly, estimating various components of human anatomy from RGB scenes are essential to human action tracking. The present study involves the implementation of human key body point detection for pose estimation. During abstracted silhouettes and vital human points, the proposed system has extracted two main features, such as 3D features and distance features. Once the relevant attributes have been incorporated, executing precise tasks and strategies is critical to achieving maximum features. Therefore, we used the t- SNE-based data refinement technique for optimal feature selection. Finally, for training, a classification model random forest is utilized. The presented system is verified on a recognized benchmark dataset, i.e., the UCF sports database. The experimental settings have revealed that our proposed system has attained better performance and outperformed other present state-of-the-art methods regarding mean recognition rate. © 2023 IEEE.

关键词： Stochastic systems

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Revealing Modern Stream Ciphers Security: A Comprehensive Review

Revealing Modern Stream Ciphers Security: A Comprehensive Re...

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2023 International Conference on Engineering, applied and Nano sciences, ICEANS 2023

作者： Albawi, Mohammed Hashim, Asaad N. Hussein, Khalid A. College of Education Technical Mustansiriyah University Dept. Computer Science Baghdad Iraq College of Computer Science and Math University of Kufa Dept. Computer Science Najaf Iraq College of Education Mustansiriyah University Dept. Computer Science Baghdad Iraq

ISBN: (纸本)9798350341973

Stream ciphers are essential cryptographic primitives widely employed in various applications for efficient data protection. This paper presents a comprehensive review of attacks on the Grain, Trivium, and SNOW stream ciphers, which are considered prominent in modern cryptography. Through an extensive analysis of existing research and publications, this work systematically categorizes and evaluates known attacks, shedding light on the vulnerabilities and weaknesses exhibited by these ciphers. By examining a diverse range of attack techniques, including algebraic, statistical, and differential attacks, this work provides insights into the security landscape of Grain, Trivium, and SNOW. Furthermore, it conducts a comparative analysis of the findings across the three ciphers, identifying common patterns and vulnerabilities. The outcomes of our study have profound implications for the practical deployment and usage of these stream ciphers, emphasizing the ongoing need for research and the development of robust countermeasures. By gaining a comprehensive understanding of the weaknesses exposed by these attacks, cryptographic practitioners can make informed decisions concerning the implementation and security of these stream ciphers. © 2023 IEEE.

关键词： Cryptanalysis Cryptology Grain family LFSR SNOW Stream cipher Trivium

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A New Projective Point in Convex Polygon Test with O(log N) Complexity 17

A New Projective Point in Convex Polygon Test with O(log N) ...

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17th IEEE International Scientific Conference on Informatics, INFORMATICS 2024

作者： Skala, Vaclav University of West Bohemia Faculty of Applied Sciences Dept. of Computer Science and Engineering Pilsen Czech Republic

ISBN: (纸本)9798350387674

This contribution describes a novel and fully projective algorithm for a point-in-convex polygon test with computational complexity of O(log N) in E2. The polygon vertices and tested points can be given in projective space, i.e., with the homogeneous coordinate w ≠ 0, without conversion to Euclidean space. It is independent of the polygon orientation. It uses vector-vector operations and is therefore aimed for implementation using SSE or AVX instructions. The algorithm is simple and robust, easy to implement. © 2024 IEEE.

关键词： Vectors

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