In game theory, the hysteresis effect manifests itself in the fact that small differences in one or more parameters lead two systems to opposite stable equilibrium. The mathematical model (called smooth model) of hysteresis game writing by ordinary differential equations with the great parameter K is studied. Estimations of closeness of output functions for smooth and classical models through the continuity module of continuous input function are received. This error can be controlled by increasing the parameter K to a sufficiently large value. The result was conducted by studying of mathematical model and using programs Mathematica, Matlab or Maple. Experiments were carried out, and the obtained results were summarized and presented through evaluations and graphical interpretation.
在博弈论中，滞后效应表现为一个或多个参数的微小差异会导致两个系统达到相反的稳定平衡。本文研究了由常微分方程书写的滞后博弈数学模型（称为平滑模型），其参数为 K。通过连续输入函数的连续性模块，对平滑模型和经典模型的输出函数的接近性进行了估计。这种误差可以通过将参数 K 增加到足够大的值来控制。结果是通过研究数学模型和使用 Mathematica、Matlab 或 Maple 程序得出的。进行了实验，并通过评估和图形解释对获得的结果进行了总结和展示。
{"title":"Mathematical model written by ordinary differential equations for hysteresis games","authors":"Nguyen Hien, Pavel Rahman","doi":"10.59035/zwhg6398","DOIUrl":"https://doi.org/10.59035/zwhg6398","url":null,"abstract":"In game theory, the hysteresis effect manifests itself in the fact that small differences in one or more parameters lead two systems to opposite stable equilibrium. The mathematical model (called smooth model) of hysteresis game writing by ordinary differential equations with the great parameter K is studied. Estimations of closeness of output functions for smooth and classical models through the continuity module of continuous input function are received. This error can be controlled by increasing the parameter K to a sufficiently large value. The result was conducted by studying of mathematical model and using programs Mathematica, Matlab or Maple. Experiments were carried out, and the obtained results were summarized and presented through evaluations and graphical interpretation.","PeriodicalId":42317,"journal":{"name":"International Journal on Information Technologies and Security","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141279848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bandar Alshawi, Amr Munshi, Majid Alotaibi, Ryan Alturki, Nasser Allheeib
Abstract: The Internet and social media networks usage has increased nowadays and become a prominent medium of communicating. Email is one of the professional reliable methods of communication. Automatic classifications of spam emails have become an area of interest. In order to detect spam emails, this study utilizes a dataset, including spam and non-spam emails. Various techniques are applied to obtain higher accuracy using machine learning techniques. NLP is also utilized for improvising accuracy using embeddings. For that, this work utilizes the BERT model, to achieve satisfactory detection of spam emails. Further, the results are compared with state-of-the-art methods, including, KNN, LSTM and Bi-LSTM. The results obtained by Bi-LSTM and LSTM were 97.94% and 86.02%, respectively. The presented methodology is promising in detecting spam emails due to the higher accuracy achieved.
{"title":"Classification of SPAM mail utilizing machine learning and deep learning techniques","authors":"Bandar Alshawi, Amr Munshi, Majid Alotaibi, Ryan Alturki, Nasser Allheeib","doi":"10.59035/fpko7430","DOIUrl":"https://doi.org/10.59035/fpko7430","url":null,"abstract":"Abstract: The Internet and social media networks usage has increased nowadays and become a prominent medium of communicating. Email is one of the professional reliable methods of communication. Automatic classifications of spam emails have become an area of interest. In order to detect spam emails, this study utilizes a dataset, including spam and non-spam emails. Various techniques are applied to obtain higher accuracy using machine learning techniques. NLP is also utilized for improvising accuracy using embeddings. For that, this work utilizes the BERT model, to achieve satisfactory detection of spam emails. Further, the results are compared with state-of-the-art methods, including, KNN, LSTM and Bi-LSTM. The results obtained by Bi-LSTM and LSTM were 97.94% and 86.02%, respectively. The presented methodology is promising in detecting spam emails due to the higher accuracy achieved.","PeriodicalId":42317,"journal":{"name":"International Journal on Information Technologies and Security","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141280844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The work presents the possibility to use Kali Linux in the process of power electronic devices research, which has not been applied before. Several of the built-in tools of Kali Linux have been used for the purpose of the research. Nmap has been used for vulnerability testing - scanning for open ports and finding out specific, well-known security vulnerabilities by using specific scripts. Wireshark and Burp Suite have been used to find out if the exchanged information is secure or not. hping3 has been used to scan for open ports and generating different TCP DoS attacks, thus studying what is the response of the power electronic device when it is subjected to different TCP DoS attacks – can it be accessed or not, has it experienced any performance violation, etc. Based on the results of this research, it can be argued that Kali Linux is applicable and can be used during the process of power electronic device research.
这项工作提出了在电力电子设备研究过程中使用 Kali Linux 的可能性，而这在以前还没有应用过。Kali Linux 的几个内置工具已用于研究目的。Nmap 用于漏洞测试--使用特定脚本扫描开放端口并找出众所周知的特定安全漏洞。hping3 用于扫描开放端口并生成不同的 TCP DoS 攻击，从而研究电力电子设备在受到不同的 TCP DoS 攻击时的反应--能否被访问、是否出现任何性能故障等。根据这项研究的结果，可以认为 Kali Linux 是适用的，可以在电力电子设备研究过程中使用。
{"title":"Kali Linux – a simple and effective way to study the level of cyber security and penetration testing of power electronic devices","authors":"Ivan Nedyalkov, Georgi Georgiev","doi":"10.59035/jmfy4876","DOIUrl":"https://doi.org/10.59035/jmfy4876","url":null,"abstract":"The work presents the possibility to use Kali Linux in the process of power electronic devices research, which has not been applied before. Several of the built-in tools of Kali Linux have been used for the purpose of the research. Nmap has been used for vulnerability testing - scanning for open ports and finding out specific, well-known security vulnerabilities by using specific scripts. Wireshark and Burp Suite have been used to find out if the exchanged information is secure or not. hping3 has been used to scan for open ports and generating different TCP DoS attacks, thus studying what is the response of the power electronic device when it is subjected to different TCP DoS attacks – can it be accessed or not, has it experienced any performance violation, etc. Based on the results of this research, it can be argued that Kali Linux is applicable and can be used during the process of power electronic device research.","PeriodicalId":42317,"journal":{"name":"International Journal on Information Technologies and Security","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141277588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Field Programmable Gate Arrays (FPGAs) have garnered significant attention in the development and enhancement of target identification algorithms that employ YOLOv2 models and FPGAs, owing to their adaptability and user-friendliness. The Simulink HDL compiler was utilized to design, simulate, and implement our proposed design. In an effort to rectify this, this paper presents a comprehensive programming and design proposal. The implementation of the YOLOv2 algorithm for real-time vehicle detection on the Xilinx® Zynq-7000 System-on-a-chip is proposed in this work. Real-time testing of the synthesised hardware revealed that it can process Full HD video at a rate of 16 frames per second. On the Xilinx Zynq-7000 SOC, the estimated dynamic power consumption is less than 90 mW. When comparing the results of the proposed work to those of other simulations, it is observed that resource utilization is enhanced by around 204 k (75%) LUT, 305 (12%) DSP, and 224 k (41%) flip-flops at 200 MHz.
{"title":"Efficient Real time Zynq 7000 FPGA deployment of optimized YOLOv2 deep leaning model for target detection, based on HDL Coder Methodology","authors":"J. Slimane","doi":"10.59035/zbte3810","DOIUrl":"https://doi.org/10.59035/zbte3810","url":null,"abstract":"Field Programmable Gate Arrays (FPGAs) have garnered significant attention in the development and enhancement of target identification algorithms that employ YOLOv2 models and FPGAs, owing to their adaptability and user-friendliness. The Simulink HDL compiler was utilized to design, simulate, and implement our proposed design. In an effort to rectify this, this paper presents a comprehensive programming and design proposal. The implementation of the YOLOv2 algorithm for real-time vehicle detection on the Xilinx® Zynq-7000 System-on-a-chip is proposed in this work. Real-time testing of the synthesised hardware revealed that it can process Full HD video at a rate of 16 frames per second. On the Xilinx Zynq-7000 SOC, the estimated dynamic power consumption is less than 90 mW. When comparing the results of the proposed work to those of other simulations, it is observed that resource utilization is enhanced by around 204 k (75%) LUT, 305 (12%) DSP, and 224 k (41%) flip-flops at 200 MHz.","PeriodicalId":42317,"journal":{"name":"International Journal on Information Technologies and Security","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141281517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes LTH-MAC (Low-Traffic Aware Hybrid MAC), a novel MAC protocol designed to improve energy efficiency and message delivery reliability in Wireless Sensor Networks (WSNs). LTH-MAC achieves this through innovative techniques like flexible timeslots, channel selection, collision-avoiding, parallel transmissions, and efficient backoff schemes. These optimizations lead to reduced idle listening, minimized collisions, and simplified synchronization. Simulations using OPNET environment demonstrate that LTH-MAC significantly reduces energy consumption, especially under light traffic loads. Additionally, LTH-MAC provides lower end-to-end latency and higher message delivery reliability compared to ECoMAC. These advancements position LTH-MAC as a compelling solution for WSN applications demanding efficient and reliable communication.
{"title":"Low-Traffic Aware Hybrid MAC (LTH-MAC) Protocol for Wireless Sensor Networks","authors":"Hafedh Zayani","doi":"10.59035/shzm1009","DOIUrl":"https://doi.org/10.59035/shzm1009","url":null,"abstract":"This paper proposes LTH-MAC (Low-Traffic Aware Hybrid MAC), a novel MAC protocol designed to improve energy efficiency and message delivery reliability in Wireless Sensor Networks (WSNs). LTH-MAC achieves this through innovative techniques like flexible timeslots, channel selection, collision-avoiding, parallel transmissions, and efficient backoff schemes. These optimizations lead to reduced idle listening, minimized collisions, and simplified synchronization. Simulations using OPNET environment demonstrate that LTH-MAC significantly reduces energy consumption, especially under light traffic loads. Additionally, LTH-MAC provides lower end-to-end latency and higher message delivery reliability compared to ECoMAC. These advancements position LTH-MAC as a compelling solution for WSN applications demanding efficient and reliable communication.","PeriodicalId":42317,"journal":{"name":"International Journal on Information Technologies and Security","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141274446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The world is entering a new era of intelligent parking, with researchers exploring innovative solutions to enhance the parking experience. These solutions aim to simplify parking challenges for drivers, reducing stress caused by traffic congestion and the search for available spaces. This study presents an intelligent parking system driven by artificial intelligence (AI). It addresses inefficient and improper parking, providing drivers with detailed information about available spaces and improving their management. This approach empowers drivers with insights into parking availability, reducing time and fuel consumption. The system streamlines parking operations, ensures accurate spot availability information, and enhances overall efficiency and user satisfaction. The effectiveness of this proposed system is evidenced by a range of outcomes, including significant reductions in the time spent searching for parking and decreased fuel consumption along comparable travel routes.
{"title":"A smart parking system combining IoT and AI to address improper parking","authors":"Mostapha Laaouafy, Fatima Lakrami, O. Labouidya","doi":"10.59035/zmry7124","DOIUrl":"https://doi.org/10.59035/zmry7124","url":null,"abstract":"The world is entering a new era of intelligent parking, with researchers exploring innovative solutions to enhance the parking experience. These solutions aim to simplify parking challenges for drivers, reducing stress caused by traffic congestion and the search for available spaces. This study presents an intelligent parking system driven by artificial intelligence (AI). It addresses inefficient and improper parking, providing drivers with detailed information about available spaces and improving their management. This approach empowers drivers with insights into parking availability, reducing time and fuel consumption. The system streamlines parking operations, ensures accurate spot availability information, and enhances overall efficiency and user satisfaction. The effectiveness of this proposed system is evidenced by a range of outcomes, including significant reductions in the time spent searching for parking and decreased fuel consumption along comparable travel routes.","PeriodicalId":42317,"journal":{"name":"International Journal on Information Technologies and Security","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141275585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A neurological condition affecting both the brain and behaviour is identified as autism spectrum disorder (ASD). Due to the absence of a reliable medical test for detecting autism, diagnoses rely on historical evidence. Essential in assessing the degree of autism are models like Convolutional Neural Networks (CNNs) and Random Forest (RF). In order to reduce the amount of diagnostic tests required for autism diagnosis, this research work presents a new hybrid model that combines the strengths of RF and CNNs, providing healthcare solutions. It is noteworthy that this model properly predicted the severity of autism with an astounding accuracy rate of 99.15% when applied to historical data gathered from the Kaggle Repository.
{"title":"Enhancing autism severity prediction: A fusion of convolutional neural networks and random forest model","authors":"R. Ramya, S. Arokiaraj","doi":"10.59035/vnwf2548","DOIUrl":"https://doi.org/10.59035/vnwf2548","url":null,"abstract":"A neurological condition affecting both the brain and behaviour is identified as autism spectrum disorder (ASD). Due to the absence of a reliable medical test for detecting autism, diagnoses rely on historical evidence. Essential in assessing the degree of autism are models like Convolutional Neural Networks (CNNs) and Random Forest (RF). In order to reduce the amount of diagnostic tests required for autism diagnosis, this research work presents a new hybrid model that combines the strengths of RF and CNNs, providing healthcare solutions. It is noteworthy that this model properly predicted the severity of autism with an astounding accuracy rate of 99.15% when applied to historical data gathered from the Kaggle Repository.","PeriodicalId":42317,"journal":{"name":"International Journal on Information Technologies and Security","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141279346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Osamah Raheem, I. Aksenov, Yu. R. Redkin, A. Gorshkov, S. Sorokin, I. Atlasov, O. Kravets
The article continues to review the approach to designing the architecture of a distributed information monitoring system and quality management of communication services provided by the infrastructures of the Internet of Things and the industrial Internet of Things, based on solutions that support machine-to-machine and human-machine interaction. The development of a neural network model of an intelligent monitoring agent based on a recurrent neural network with a long chain of short-term memory elements is proposed. The matrix structure of the LSTM network memory cell is proposed, which takes into account the spatio-temporal correlation of load parameters associated with the time lag of its propagation and is a matrix of connectivity of LSTM network hyperparameters and accumulated values of load parameters of monitoring nodes in the vicinity of a controlled monitoring node, taking into account the characteristics of the time series of propagation of load in stationarity moments.
{"title":"Development of a neural network model of an intelligent monitoring agent based on a recurrent neural network with a long chain of short-term memory elements","authors":"Osamah Raheem, I. Aksenov, Yu. R. Redkin, A. Gorshkov, S. Sorokin, I. Atlasov, O. Kravets","doi":"10.59035/obhp8561","DOIUrl":"https://doi.org/10.59035/obhp8561","url":null,"abstract":"The article continues to review the approach to designing the architecture of a distributed information monitoring system and quality management of communication services provided by the infrastructures of the Internet of Things and the industrial Internet of Things, based on solutions that support machine-to-machine and human-machine interaction. The development of a neural network model of an intelligent monitoring agent based on a recurrent neural network with a long chain of short-term memory elements is proposed. The matrix structure of the LSTM network memory cell is proposed, which takes into account the spatio-temporal correlation of load parameters associated with the time lag of its propagation and is a matrix of connectivity of LSTM network hyperparameters and accumulated values of load parameters of monitoring nodes in the vicinity of a controlled monitoring node, taking into account the characteristics of the time series of propagation of load in stationarity moments.","PeriodicalId":42317,"journal":{"name":"International Journal on Information Technologies and Security","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141274963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, stream cipher systems that have been traditionally designed using linear feedback shift register have been almost entirely compromised by algebraic attack methods. Thus, identifying a method to establish concepts for new-generation ciphers, prevent existing security problems, and design new stream cipher systems that consider both security and performance has become a crucial concern in the field of cryptography. In 2004, the European Union initiated the eSTREAM project to emulate the Advanced Encryption Standard used in the United States. The project consisted of 48 participating stream cipher candidates. Through open selection, review, and runoff voting, the results were announced in May 2008. This research investigated one of the finalists of the eSTREAM competition: The Rabbit stream cipher. Additionally, stream cipher attack methods have been extensively studied in recent years, especially those for distinguishing attacks. Thus, contributions of this article is explored the design concepts of the core algorithms in the new-generation stream cipher systems for determining the corresponding mathematical principles and practical approaches to contribute to the study of stream cipher systems.
{"title":"The security analysis on the rabbit stream cipher","authors":"Kai Chain","doi":"10.59035/swyf4934","DOIUrl":"https://doi.org/10.59035/swyf4934","url":null,"abstract":"In recent years, stream cipher systems that have been traditionally designed using linear feedback shift register have been almost entirely compromised by algebraic attack methods. Thus, identifying a method to establish concepts for new-generation ciphers, prevent existing security problems, and design new stream cipher systems that consider both security and performance has become a crucial concern in the field of cryptography. In 2004, the European Union initiated the eSTREAM project to emulate the Advanced Encryption Standard used in the United States. The project consisted of 48 participating stream cipher candidates. Through open selection, review, and runoff voting, the results were announced in May 2008. This research investigated one of the finalists of the eSTREAM competition: The Rabbit stream cipher. Additionally, stream cipher attack methods have been extensively studied in recent years, especially those for distinguishing attacks. Thus, contributions of this article is explored the design concepts of the core algorithms in the new-generation stream cipher systems for determining the corresponding mathematical principles and practical approaches to contribute to the study of stream cipher systems.","PeriodicalId":42317,"journal":{"name":"International Journal on Information Technologies and Security","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141279471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With widespread uses in areas as diverse as traffic analysis and medical imaging, picture segmentation is a basic problem in computer vision. Instance segmentation, which combines object recognition with segmentation, is a powerful tool for item identification and exact delineation. Using the Tomato Leaf disease dataset as an example, this research delves into the topic of segmentation training by capitalizing on the simplicity of enhanced YOLOv8-Seg models. Tomato leaf disease are the focus of this instance-segmentation dataset, which seeks to resolve the pressing problem of agricultural difficulties. One instance segmentation networks, YOLOv8n-Seg is presented and compared in this article for the purpose of Tomato leaf disease identification. The models are tested in difficult situations to see how well they can detect and separate garbage occurrences. Results show that enhanced YOLOv8-Seg is useful for agriculture by accurately segmenting instances of tomato leaf disease detection.
{"title":"Enhancing real-time instance segmentation for plant disease detection with improved YOLOv8-Seg algorithm","authors":"Mohamed Ammar","doi":"10.59035/bcnl3199","DOIUrl":"https://doi.org/10.59035/bcnl3199","url":null,"abstract":"With widespread uses in areas as diverse as traffic analysis and medical imaging, picture segmentation is a basic problem in computer vision. Instance segmentation, which combines object recognition with segmentation, is a powerful tool for item identification and exact delineation. Using the Tomato Leaf disease dataset as an example, this research delves into the topic of segmentation training by capitalizing on the simplicity of enhanced YOLOv8-Seg models. Tomato leaf disease are the focus of this instance-segmentation dataset, which seeks to resolve the pressing problem of agricultural difficulties. One instance segmentation networks, YOLOv8n-Seg is presented and compared in this article for the purpose of Tomato leaf disease identification. The models are tested in difficult situations to see how well they can detect and separate garbage occurrences. Results show that enhanced YOLOv8-Seg is useful for agriculture by accurately segmenting instances of tomato leaf disease detection.","PeriodicalId":42317,"journal":{"name":"International Journal on Information Technologies and Security","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141281149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：