Pub Date : 2022-10-21DOI: 10.1109/INMIC56986.2022.9972976
Anam Amjad, F. Azam, Muhammad Waseem Anwar
Industrial Internet of Things (IIoT) is an emerging domain, converting common objects into connecting objects with ubiquitous internet access to automate industry. Due to different vendors, supporting different infrastructures, a set of communication protocols such as Zigbee, 6LowPAN, Wireless Fidelity (Wi-Fi), Hyper Text Transfer Protocol (HTTP), etc. are introduced for IIoT. Thus, a closed ecosystem for smart devices is created. Particularly, when two or more industrial IoT applications are developed using different application-layer protocols such as Constrained Application Protocol (CoAP), Advanced Message Queuing Protocol (AMQP), or MQ Telemetry Transport (MQTT), devices are called heterogeneous devices and interoperability becomes a major challenge. In the existing literature, device-level interoperability using different application-layer protocols is enhanced with the help of intermediators at the network layer which includes servers, brokers, or gateways/adapters to route communication. However, these intermediators lead to several other issues such as dependency on network layer components, load balancing, single point of failure, and scalability. Therefore, the interoperability issue needs to be addressed at the application layer using a device intermediator instead of utilizing network layer components. For this purpose, Model Driven Engineering (MDE) is selected because less attention is paid to IIoT interoperable solutions development using MDE. To bridge this gap, a Model Driven Architecture (MDA) based approach is proposed that reduces the processing time and effort to develop these IIoT interoperable systems. For this purpose, (i) a metamodel, (ii) a UML profile, and (iii) transformation rules are developed to make heterogenous application-layer protocols interoperable using devices as intermediator. The initial feasibility of the proposed solution is demonstrated through a real-world case study i.e., a smart city. Results show that a complete solution for interoperability at the application layer for industrial IoT is provided using MDA. It will help the practitioners to automate industry 4.0 using model-driven based system development.
{"title":"Device Interoperability for Industrial IoT using Model-Driven Architecture","authors":"Anam Amjad, F. Azam, Muhammad Waseem Anwar","doi":"10.1109/INMIC56986.2022.9972976","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972976","url":null,"abstract":"Industrial Internet of Things (IIoT) is an emerging domain, converting common objects into connecting objects with ubiquitous internet access to automate industry. Due to different vendors, supporting different infrastructures, a set of communication protocols such as Zigbee, 6LowPAN, Wireless Fidelity (Wi-Fi), Hyper Text Transfer Protocol (HTTP), etc. are introduced for IIoT. Thus, a closed ecosystem for smart devices is created. Particularly, when two or more industrial IoT applications are developed using different application-layer protocols such as Constrained Application Protocol (CoAP), Advanced Message Queuing Protocol (AMQP), or MQ Telemetry Transport (MQTT), devices are called heterogeneous devices and interoperability becomes a major challenge. In the existing literature, device-level interoperability using different application-layer protocols is enhanced with the help of intermediators at the network layer which includes servers, brokers, or gateways/adapters to route communication. However, these intermediators lead to several other issues such as dependency on network layer components, load balancing, single point of failure, and scalability. Therefore, the interoperability issue needs to be addressed at the application layer using a device intermediator instead of utilizing network layer components. For this purpose, Model Driven Engineering (MDE) is selected because less attention is paid to IIoT interoperable solutions development using MDE. To bridge this gap, a Model Driven Architecture (MDA) based approach is proposed that reduces the processing time and effort to develop these IIoT interoperable systems. For this purpose, (i) a metamodel, (ii) a UML profile, and (iii) transformation rules are developed to make heterogenous application-layer protocols interoperable using devices as intermediator. The initial feasibility of the proposed solution is demonstrated through a real-world case study i.e., a smart city. Results show that a complete solution for interoperability at the application layer for industrial IoT is provided using MDA. It will help the practitioners to automate industry 4.0 using model-driven based system development.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116441996","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}
Pub Date : 2022-10-21DOI: 10.1109/INMIC56986.2022.9972932
Muhammad Nauman Asif, Muhammad Arshad Islam
To make the internet a more productive environment, it is vital to promote constructiveness in online discussion forums. Customers are regularly offered the chance to share their thoughts and experiences with a product on online marketplaces. Generally, online products have fewer constructive reviews, and some of them are unrelated to the product. Existing approaches focus on textual features to classify a product's constructiveness and ignore semantic and contextual information about the reviews. The directed graph model has been utilized in this study to represent information about the product. Also, the node and graph level features like average in-degree, out-degree, and clustering coefficients are used to model constructiveness in product evaluation to encourage the most informative reviews. Graph embedding techniques are used to depict each node as a vector into low-dimensional space and preserve the structure and properties of the graph as well. The topic modeling approach has been used to contextualize the reviews with the appropriate product. Additionally, we employed logistic regression, random forest, Gaussian naive Bayes, support vector machine (SVM), and Gradient Boosting Machine models trained on Amazon product reviews and constructive news corpus for constructiveness. These ML models outperform the baseline approach, achieving a 90% F1-Score.
{"title":"Constructiveness-Based Product Review Scoring Using Machine Learning","authors":"Muhammad Nauman Asif, Muhammad Arshad Islam","doi":"10.1109/INMIC56986.2022.9972932","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972932","url":null,"abstract":"To make the internet a more productive environment, it is vital to promote constructiveness in online discussion forums. Customers are regularly offered the chance to share their thoughts and experiences with a product on online marketplaces. Generally, online products have fewer constructive reviews, and some of them are unrelated to the product. Existing approaches focus on textual features to classify a product's constructiveness and ignore semantic and contextual information about the reviews. The directed graph model has been utilized in this study to represent information about the product. Also, the node and graph level features like average in-degree, out-degree, and clustering coefficients are used to model constructiveness in product evaluation to encourage the most informative reviews. Graph embedding techniques are used to depict each node as a vector into low-dimensional space and preserve the structure and properties of the graph as well. The topic modeling approach has been used to contextualize the reviews with the appropriate product. Additionally, we employed logistic regression, random forest, Gaussian naive Bayes, support vector machine (SVM), and Gradient Boosting Machine models trained on Amazon product reviews and constructive news corpus for constructiveness. These ML models outperform the baseline approach, achieving a 90% F1-Score.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128170314","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}
Pub Date : 2022-10-21DOI: 10.1109/INMIC56986.2022.9972934
Saeed Ur Rehman Bhatti, Shujaat Hussain, Kifayat-Ullah Khan
Globalization, enhanced networking, and contemporary communication systems have resulted in a substantial increase in the number of resumes created. Processing this massive chunk manually is time-consuming. Multiple Criteria Decision Making (MCDM) techniques, such as “Analytic Hierarchy Process” (AHP), “The Technique for Order of Preference by Similarity to Ideal Solution” (TOPSIS), “VlseKriterijumska Optimizacija I Kompromisno Resenje” (VIKOR), and their state-of-the-art optimized variants, SlashRank, consider multiple factors and then rank the results accordingly to streamline the process. These algorithms primarily focus on reducing computational complexity, however, they ignore automated feature importance since they require manual features weights as an explicit input. In the end, this leads to a biased result and a decentralized method of hiring candidates. Our research addresses the human intervention observed in previously identified techniques and proposes a technique that automates candidate pruning, manual feature priority input, and generates new feature importance based on user feedback. Our approach can be decomposed into automated candidate pruning for the highest priority feature, candidate selection feedback, and trend-based feature weight generation to replicate actual recruitment feature priority fluctuations. Our algorithm demonstrates promising results in minimizing human biases and generating a dynamic trend of feature importance over time.
全球化、增强的网络和现代通信系统导致了简历数量的大幅增加。手动处理这个庞大的块非常耗时。多标准决策(MCDM)技术，如“层次分析法”(AHP)，“理想解决方案相似性偏好排序技术”(TOPSIS)，“VlseKriterijumska Optimizacija I Kompromisno Resenje”(VIKOR)，以及它们最先进的优化变体SlashRank，考虑多个因素，然后相应地对结果进行排序，以简化过程。这些算法主要关注于降低计算复杂性，然而，它们忽略了自动特征的重要性，因为它们需要手动特征权重作为显式输入。最终，这会导致一个有偏见的结果和一个分散的招聘候选人的方法。我们的研究解决了在先前确定的技术中观察到的人为干预问题，并提出了一种技术，该技术可以自动修剪候选特征，手动输入特征优先级，并根据用户反馈生成新的特征重要性。我们的方法可以分解为最高优先级特征的自动候选修剪，候选人选择反馈和基于趋势的特征权重生成，以复制实际招聘特征优先级波动。我们的算法在最小化人类偏见和生成特征重要性随时间的动态趋势方面显示了有希望的结果。
{"title":"A Robust Algorithm for Candidate Pruning and Feature Weight Generation in E-Recruitment System","authors":"Saeed Ur Rehman Bhatti, Shujaat Hussain, Kifayat-Ullah Khan","doi":"10.1109/INMIC56986.2022.9972934","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972934","url":null,"abstract":"Globalization, enhanced networking, and contemporary communication systems have resulted in a substantial increase in the number of resumes created. Processing this massive chunk manually is time-consuming. Multiple Criteria Decision Making (MCDM) techniques, such as “Analytic Hierarchy Process” (AHP), “The Technique for Order of Preference by Similarity to Ideal Solution” (TOPSIS), “VlseKriterijumska Optimizacija I Kompromisno Resenje” (VIKOR), and their state-of-the-art optimized variants, SlashRank, consider multiple factors and then rank the results accordingly to streamline the process. These algorithms primarily focus on reducing computational complexity, however, they ignore automated feature importance since they require manual features weights as an explicit input. In the end, this leads to a biased result and a decentralized method of hiring candidates. Our research addresses the human intervention observed in previously identified techniques and proposes a technique that automates candidate pruning, manual feature priority input, and generates new feature importance based on user feedback. Our approach can be decomposed into automated candidate pruning for the highest priority feature, candidate selection feedback, and trend-based feature weight generation to replicate actual recruitment feature priority fluctuations. Our algorithm demonstrates promising results in minimizing human biases and generating a dynamic trend of feature importance over time.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121778571","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}
Pub Date : 2022-10-21DOI: 10.1109/INMIC56986.2022.9972882
Aisha Bibi, Muhammad Ismail Khan, Imdad Khan
A bilayer, ultrathin, dual-band chiral metasurface is designed and analyzed in this paper with linear polarization conversion and asymmetric transmission for Ku-band and 5G communications. The polarization conversion efficiency of the first band (11.8-13.5 GHz) is ultra-high having a value 0.95 at 12GHz and that of the second band (26.2-26.7 GHz) is 0.9 at 25.6 GHz. The proposed structure also exhibits linear polarization asymmetric transmission in both bands with asymmetric parameters above 90% and above 80% for the first and second bands, respectively. The structure is ultrathin with respect to lowest resonating frequency of 12 GHz having thickness of 0.032λ0. Moreover, the structure is also angularly stable upto 60° for first band and upto 30° for second band, making the structure robust for practical applications. Due to scalability of the design, the proposed structure finds wide range of applications, covering a large spectrum from microwave to 5G bands.
{"title":"Dual-band Chiral Metasurface with Linear Asymmetric Transmission and Orthogonal Polarization Conversion over a wide Incidence Angle for Ku-band and 5G Applications","authors":"Aisha Bibi, Muhammad Ismail Khan, Imdad Khan","doi":"10.1109/INMIC56986.2022.9972882","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972882","url":null,"abstract":"A bilayer, ultrathin, dual-band chiral metasurface is designed and analyzed in this paper with linear polarization conversion and asymmetric transmission for Ku-band and 5G communications. The polarization conversion efficiency of the first band (11.8-13.5 GHz) is ultra-high having a value 0.95 at 12GHz and that of the second band (26.2-26.7 GHz) is 0.9 at 25.6 GHz. The proposed structure also exhibits linear polarization asymmetric transmission in both bands with asymmetric parameters above 90% and above 80% for the first and second bands, respectively. The structure is ultrathin with respect to lowest resonating frequency of 12 GHz having thickness of 0.032λ0. Moreover, the structure is also angularly stable upto 60° for first band and upto 30° for second band, making the structure robust for practical applications. Due to scalability of the design, the proposed structure finds wide range of applications, covering a large spectrum from microwave to 5G bands.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"90 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133523235","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}
Pub Date : 2022-10-21DOI: 10.1109/INMIC56986.2022.9972980
Fariha Maqbool, Haroon Mahmood, Hasan Ali Khattak
Industrial sectors are constantly under pressure to produce higher-quality goods while maximizing yield. Machine maintenance is a critical component of manufacturing, accounting for a significant portion of total production costs. Corrective, preventive, and conditional maintenance strategies only make a negligible contribution to cost and downtime reduction. With the fifth industrial revolution, industrialists can now use sensors and cyber-physical systems to perform predictive maintenance on manufacturing operations. This strategy eliminates unnecessary maintenance and minimizes downtime by continuously collecting and analyzing data to predict time to failure. Numerous approaches to fault prediction have been proposed for predictive maintenance, but most of them are prohibitively expensive due to the massive number of features in manufacturing machines. The purpose of this work is to develop a technique for reliably predicting machine problems with the fewest possible features. To select features, we used SVR-based Recursive Feature Elimination (SVR-RFE) and Random Forest Regressor (RFR), while to predict, we used Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). Our experiments on the 2018 PHM Challenge Dataset demonstrate that the proposed strategy outperforms prior approaches and reduces the mean absolute percentage error (SMAPE).
{"title":"An Efficient Fault-Prediction Mechanism for Improving Yield in Industry 5.0","authors":"Fariha Maqbool, Haroon Mahmood, Hasan Ali Khattak","doi":"10.1109/INMIC56986.2022.9972980","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972980","url":null,"abstract":"Industrial sectors are constantly under pressure to produce higher-quality goods while maximizing yield. Machine maintenance is a critical component of manufacturing, accounting for a significant portion of total production costs. Corrective, preventive, and conditional maintenance strategies only make a negligible contribution to cost and downtime reduction. With the fifth industrial revolution, industrialists can now use sensors and cyber-physical systems to perform predictive maintenance on manufacturing operations. This strategy eliminates unnecessary maintenance and minimizes downtime by continuously collecting and analyzing data to predict time to failure. Numerous approaches to fault prediction have been proposed for predictive maintenance, but most of them are prohibitively expensive due to the massive number of features in manufacturing machines. The purpose of this work is to develop a technique for reliably predicting machine problems with the fewest possible features. To select features, we used SVR-based Recursive Feature Elimination (SVR-RFE) and Random Forest Regressor (RFR), while to predict, we used Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). Our experiments on the 2018 PHM Challenge Dataset demonstrate that the proposed strategy outperforms prior approaches and reduces the mean absolute percentage error (SMAPE).","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128786242","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}
Pub Date : 2022-10-21DOI: 10.1109/INMIC56986.2022.9972973
Afzal Yasmeen, Asim Muhammad, Khan Kifayat Ullah
With increased dependency on computers, the threat of cyber-attacks becomes more prevalent. Cyber threat intelligence gathers reports from previous threats and helps to identify potential future attacks. The challenge for threat intelligence is overloaded threat feeds from various sources with structural heterogeneity. Currently, most of the sources share same type of data in heterogeneous format with different identifiers. In this paper, an architecture has been proposed for data aggregation from heterogeneous sources. The architecture is based on a three tier model that maps the heterogeneous sources' feeds into the target Threat Intelligence Platform (TIP). In this model, each layer has its own set of tasks and works in a step-by-step pattern, the output of one layer is input to the next layer. The working of this model is entirely dependent on the XML broker for dynamic mapping of sources. The objective is to have a unified system that can transform data from heterogeneous sources into a unified form that can assist the TIP in further statistics generation for analysis. This architecture has been implemented over six heterogeneous sources and performed data aggregation.
{"title":"A Robust Architecture for Aggregation of Heterogeneous Data for Threat Intelligence Platforms","authors":"Afzal Yasmeen, Asim Muhammad, Khan Kifayat Ullah","doi":"10.1109/INMIC56986.2022.9972973","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972973","url":null,"abstract":"With increased dependency on computers, the threat of cyber-attacks becomes more prevalent. Cyber threat intelligence gathers reports from previous threats and helps to identify potential future attacks. The challenge for threat intelligence is overloaded threat feeds from various sources with structural heterogeneity. Currently, most of the sources share same type of data in heterogeneous format with different identifiers. In this paper, an architecture has been proposed for data aggregation from heterogeneous sources. The architecture is based on a three tier model that maps the heterogeneous sources' feeds into the target Threat Intelligence Platform (TIP). In this model, each layer has its own set of tasks and works in a step-by-step pattern, the output of one layer is input to the next layer. The working of this model is entirely dependent on the XML broker for dynamic mapping of sources. The objective is to have a unified system that can transform data from heterogeneous sources into a unified form that can assist the TIP in further statistics generation for analysis. This architecture has been implemented over six heterogeneous sources and performed data aggregation.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129832914","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}
Pub Date : 2022-10-21DOI: 10.1109/INMIC56986.2022.9972941
Muhammad Shaharyar Yaqub, Haroon Mahmood, Ibrahim Nadir, G. Shah
Internet of Things (IoT) market is growing exponentially and automated smart solutions are revolutionizing a diverse range of areas with innovative technologies. The most critical and vital part of an IoT system that cannot be overlooked at any cost is its security. The security standards for IoT devices are not mature enough to provide foolproof security and there is still a long journey for manufacturers to incorporate stealth in devices. The most vulnerable component of an IoT system is the firmware which controls all the functionality of the device. If subverted by an attacker, the firmware of the IoT device can prove to be a critical attack surface for obtaining enough information to annihilate an IoT device. In this paper, we propose a twofold strategy to critically analyze the security of an IoT firmware. We will first use the STRIDE threat model to identify the security parameters that attackers could exploit to launch attacks. We will then use reverse engineering to examine and evaluate the security of a wide range of firmware being used in the latest and most commonly used IoT devices based on the identified security parameters. The same parameters can then derive security expectations for a secure IoT firmware. The proposed approach provides a powerful strategy to comprehensively analyze an IoT system's security. Our experimental results show that more than 50 percent of the firmware are exposing critical information that can be used to launch attacks. We believe that our findings will also help establish recommendations for developing secure and resilient firmware.
{"title":"An Ensemble Approach for IoT Firmware Strength Analysis using STRIDE Threat Modeling and Reverse Engineering","authors":"Muhammad Shaharyar Yaqub, Haroon Mahmood, Ibrahim Nadir, G. Shah","doi":"10.1109/INMIC56986.2022.9972941","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972941","url":null,"abstract":"Internet of Things (IoT) market is growing exponentially and automated smart solutions are revolutionizing a diverse range of areas with innovative technologies. The most critical and vital part of an IoT system that cannot be overlooked at any cost is its security. The security standards for IoT devices are not mature enough to provide foolproof security and there is still a long journey for manufacturers to incorporate stealth in devices. The most vulnerable component of an IoT system is the firmware which controls all the functionality of the device. If subverted by an attacker, the firmware of the IoT device can prove to be a critical attack surface for obtaining enough information to annihilate an IoT device. In this paper, we propose a twofold strategy to critically analyze the security of an IoT firmware. We will first use the STRIDE threat model to identify the security parameters that attackers could exploit to launch attacks. We will then use reverse engineering to examine and evaluate the security of a wide range of firmware being used in the latest and most commonly used IoT devices based on the identified security parameters. The same parameters can then derive security expectations for a secure IoT firmware. The proposed approach provides a powerful strategy to comprehensively analyze an IoT system's security. Our experimental results show that more than 50 percent of the firmware are exposing critical information that can be used to launch attacks. We believe that our findings will also help establish recommendations for developing secure and resilient firmware.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"2007 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128587246","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}
Pub Date : 2022-10-21DOI: 10.1109/INMIC56986.2022.9972963
Qammar Un nisa, M. A. Shah
An online data storage and retrieval system known as a cloud computing environment makes it easier for users to access data from virtually anywhere at any time. However, according to the CIA triad, data kept on the cloud is vulnerable to data breaches. Data integrity and authentication can be compromised since end users and third parties are both permitted access to the data. With the help of cryptographic algorithms like elliptic curve cryptography (ECC), numerous methods and protocols have been developed to ensure the security and integrity of data. A popular symmetric key block cypher method is the data encryption standard (DES). Up until it was proven unsafe, the security of DES was a sensitive and resolved topic. In this research, we present a method for protecting data transmission among users in cloud computing that, when combined with ECC, can address the security issue with DES. We present a hybrid approach that combines two cryptographic methods. We propose a solution to reduce the key size issue. In comparison to existing encryption systems, our system ensures data confidentiality and authentication integrity. By retaining more space, our plan reduces computational complexity.
{"title":"A Hybrid Model for Cloud Data Security Using ECC-DES","authors":"Qammar Un nisa, M. A. Shah","doi":"10.1109/INMIC56986.2022.9972963","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972963","url":null,"abstract":"An online data storage and retrieval system known as a cloud computing environment makes it easier for users to access data from virtually anywhere at any time. However, according to the CIA triad, data kept on the cloud is vulnerable to data breaches. Data integrity and authentication can be compromised since end users and third parties are both permitted access to the data. With the help of cryptographic algorithms like elliptic curve cryptography (ECC), numerous methods and protocols have been developed to ensure the security and integrity of data. A popular symmetric key block cypher method is the data encryption standard (DES). Up until it was proven unsafe, the security of DES was a sensitive and resolved topic. In this research, we present a method for protecting data transmission among users in cloud computing that, when combined with ECC, can address the security issue with DES. We present a hybrid approach that combines two cryptographic methods. We propose a solution to reduce the key size issue. In comparison to existing encryption systems, our system ensures data confidentiality and authentication integrity. By retaining more space, our plan reduces computational complexity.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"149 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125880810","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}
Pub Date : 2022-10-21DOI: 10.1109/INMIC56986.2022.9972972
Muhammad Wisal, A. Mustafa, Umair Arshad
Urdu is the official language of Pakistan and a familiar language in the South Asian countries. It is spoken as the first language by nearly 70 million people and as a second language by more than 100 million people, mainly in Pakistan and India. Most of the textual communication is not pure Roman Urdu. There are words of actual English in between those Roman Urdu sentences. It is necessary to have a translator that can translate these code-mixed sentences into Urdu because the purpose of any language is to communicate. It can be difficult for a machine to understand the shift of languages in between a sentence. In the past, researchers have worked on Urdu transliteration and rule-based translation. However, a pure translation of mixed Roman Urdu to Urdu with such accuracy is novel. In this research, we have introduced Mixed Language (Roman Urdu and English) to the Urdu translator. A deep learning pre-trained model “g2p_multilingual_byT5_small” is fine-tuned with a newly created corpus of Mixed Roman Urdu sentences and their translations in pure Urdu. With a BLEU score of 66.73, It can translate text messages, paragraphs, or any descriptions from Roman Urdu to Urdu. We have carried out this research using Python programming language and the model training on Google Colab.
{"title":"CMRUTU: Code Mixed Roman Urdu (Roman Urdu and English) to Urdu Translator","authors":"Muhammad Wisal, A. Mustafa, Umair Arshad","doi":"10.1109/INMIC56986.2022.9972972","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972972","url":null,"abstract":"Urdu is the official language of Pakistan and a familiar language in the South Asian countries. It is spoken as the first language by nearly 70 million people and as a second language by more than 100 million people, mainly in Pakistan and India. Most of the textual communication is not pure Roman Urdu. There are words of actual English in between those Roman Urdu sentences. It is necessary to have a translator that can translate these code-mixed sentences into Urdu because the purpose of any language is to communicate. It can be difficult for a machine to understand the shift of languages in between a sentence. In the past, researchers have worked on Urdu transliteration and rule-based translation. However, a pure translation of mixed Roman Urdu to Urdu with such accuracy is novel. In this research, we have introduced Mixed Language (Roman Urdu and English) to the Urdu translator. A deep learning pre-trained model “g2p_multilingual_byT5_small” is fine-tuned with a newly created corpus of Mixed Roman Urdu sentences and their translations in pure Urdu. With a BLEU score of 66.73, It can translate text messages, paragraphs, or any descriptions from Roman Urdu to Urdu. We have carried out this research using Python programming language and the model training on Google Colab.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121872142","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}
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