Pub Date : 2022-12-02DOI: 10.1109/ICAST55766.2022.10039480
Saheel Patil, Akshay Pashte, Satyam Rai, Sejal Shah
Cancer is a fatal disease recognized and researched about, around the globe. Researchers and scientists have been investing their time and imparting their expertise, and knowledge for the advancements of traditional methods and treatments to tackle it. Recent surveys reveal that the mortality rate among the female populous, over the world, is also one of the results of breast cancer. The definition of breast cancer can be described as an uncontrolled aggressive growth of old cells which thereby aid the formation of a pernicious mass in the tissue of a breast. Gradually, this may result in the formation of a tumor of malignant nature. Deep learning, considered a sub-field of Machine Learning, enables experts to analyze, model, and study complicated or rather complex scientific data over a comprehensive list of medical applications. This study aims to create a user-friendly, adept system to perform the classification of breast tumors of malignant or benign nature. The proposed system is divided into two halves or stages. The initial stage is the pre-processing and analysis of the acquired dataset which also involves training of the neural network. The next and final stage is the classification of breast tumors by utilizing the created model and loading it onto an API through which users can upload tissue images and check what type of breast cancer the tissue contains. This would eliminate the time spent on studying every particular data using traditional clinical methods. This project would help support the radiologists in training, research, and diagnostic aspects and overall support the entire process of cancer diagnosis and treatment.
{"title":"Breast Cancer Classification by Implementation of Deep-Learning with Dataset Analysis","authors":"Saheel Patil, Akshay Pashte, Satyam Rai, Sejal Shah","doi":"10.1109/ICAST55766.2022.10039480","DOIUrl":"https://doi.org/10.1109/ICAST55766.2022.10039480","url":null,"abstract":"Cancer is a fatal disease recognized and researched about, around the globe. Researchers and scientists have been investing their time and imparting their expertise, and knowledge for the advancements of traditional methods and treatments to tackle it. Recent surveys reveal that the mortality rate among the female populous, over the world, is also one of the results of breast cancer. The definition of breast cancer can be described as an uncontrolled aggressive growth of old cells which thereby aid the formation of a pernicious mass in the tissue of a breast. Gradually, this may result in the formation of a tumor of malignant nature. Deep learning, considered a sub-field of Machine Learning, enables experts to analyze, model, and study complicated or rather complex scientific data over a comprehensive list of medical applications. This study aims to create a user-friendly, adept system to perform the classification of breast tumors of malignant or benign nature. The proposed system is divided into two halves or stages. The initial stage is the pre-processing and analysis of the acquired dataset which also involves training of the neural network. The next and final stage is the classification of breast tumors by utilizing the created model and loading it onto an API through which users can upload tissue images and check what type of breast cancer the tissue contains. This would eliminate the time spent on studying every particular data using traditional clinical methods. This project would help support the radiologists in training, research, and diagnostic aspects and overall support the entire process of cancer diagnosis and treatment.","PeriodicalId":225239,"journal":{"name":"2022 5th International Conference on Advances in Science and Technology (ICAST)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134483087","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 : 2022-12-02DOI: 10.1109/ICAST55766.2022.10039523
N. Ingle, Siddhant Deepak Sable, D. P. Ghadge, Sarika Y. Mane
The world is facing a water crisis. The lack of water resources is a major challenge for the health and wellbeing of the world's population. This project aims to develop an artificial intelligence based water management system to optimize water resources. The system will provide a platform for water users and water managers to access information, solve problems and make informed decisions for water resource management. Resources of water management involve the supply, allocation and use of water, and the impact of these factors on ecosystems and human well-being. The overall goal of this project is to provide a platform for data to be stored, analyzed, and presented in a user-friendly way. The system will also be able to predict future water resource usage and monitor the status of water resources. The data will be provided by sensors, which will provide the data for the artificial intelligence system. This project will involve the creation of an artificial intelligent system that will be able to predict the future quality of water resources using a combination of data from sensors in the field and artificial intelligence. The artificial intelligent system will be utilized on a water quality management system such as a water treatment plant. The project will also involve the use of artificial intelligence based sensors, which will be capable of detecting water quality and producing a visual representation in the form of graphs on a tablet or PC screen. The data from the sensors will be inputted into the artificial intelligence system.
{"title":"Artificial Intelligence based water Management System","authors":"N. Ingle, Siddhant Deepak Sable, D. P. Ghadge, Sarika Y. Mane","doi":"10.1109/ICAST55766.2022.10039523","DOIUrl":"https://doi.org/10.1109/ICAST55766.2022.10039523","url":null,"abstract":"The world is facing a water crisis. The lack of water resources is a major challenge for the health and wellbeing of the world's population. This project aims to develop an artificial intelligence based water management system to optimize water resources. The system will provide a platform for water users and water managers to access information, solve problems and make informed decisions for water resource management. Resources of water management involve the supply, allocation and use of water, and the impact of these factors on ecosystems and human well-being. The overall goal of this project is to provide a platform for data to be stored, analyzed, and presented in a user-friendly way. The system will also be able to predict future water resource usage and monitor the status of water resources. The data will be provided by sensors, which will provide the data for the artificial intelligence system. This project will involve the creation of an artificial intelligent system that will be able to predict the future quality of water resources using a combination of data from sensors in the field and artificial intelligence. The artificial intelligent system will be utilized on a water quality management system such as a water treatment plant. The project will also involve the use of artificial intelligence based sensors, which will be capable of detecting water quality and producing a visual representation in the form of graphs on a tablet or PC screen. The data from the sensors will be inputted into the artificial intelligence system.","PeriodicalId":225239,"journal":{"name":"2022 5th International Conference on Advances in Science and Technology (ICAST)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125096919","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}
Multiplier is one of the crucial blocks in many DSP applications. There are various multiplier architectures which are in use, some among these are Booth, Modified booth, Array and Vedic multipliers. In the proposed paper we present a Vedic multiplier built around the ‘Urdhva Tiryakbhyam sutra’ algorithm. Advantages of these multipliers are low power consumption and high performance. A notable feature of the anticipated method is that multiplier architecture makes use of CLA as a building block for faster addition. CLA stands for Carry Lookahead Adder. These modified multiplication techniques can be extended for larger sizes. Verilog HDL was used for the design and implementation. Xilinx ISE 14.7 was used for simulation and RTL synthesis.
乘法器是许多DSP应用中的关键模块之一。有各种各样的乘数架构在使用，其中一些是展台，改装展台，阵列和吠陀乘数。在提议的论文中，我们提出了一个围绕“Urdhva Tiryakbhyam经”算法构建的吠陀乘数。这些乘法器的优点是低功耗和高性能。预期方法的一个显著特征是，乘数体系结构使用CLA作为快速加法的构建块。CLA代表进位前向加法器。这些改进的乘法技术可以扩展到更大的尺寸。采用Verilog HDL进行设计和实现。采用Xilinx ISE 14.7进行模拟和RTL合成。
{"title":"Vedic Multiplier Using Carry look ahead adder","authors":"Jayesh Suryawanshi, Deepak Gawade, Nidhi Tank, Shreya Worlikar, Shridhar Sahu","doi":"10.1109/ICAST55766.2022.10039667","DOIUrl":"https://doi.org/10.1109/ICAST55766.2022.10039667","url":null,"abstract":"Multiplier is one of the crucial blocks in many DSP applications. There are various multiplier architectures which are in use, some among these are Booth, Modified booth, Array and Vedic multipliers. In the proposed paper we present a Vedic multiplier built around the ‘Urdhva Tiryakbhyam sutra’ algorithm. Advantages of these multipliers are low power consumption and high performance. A notable feature of the anticipated method is that multiplier architecture makes use of CLA as a building block for faster addition. CLA stands for Carry Lookahead Adder. These modified multiplication techniques can be extended for larger sizes. Verilog HDL was used for the design and implementation. Xilinx ISE 14.7 was used for simulation and RTL synthesis.","PeriodicalId":225239,"journal":{"name":"2022 5th International Conference on Advances in Science and Technology (ICAST)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125191630","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 : 2022-12-02DOI: 10.1109/ICAST55766.2022.10039633
Prasad Anand Apte, Shaikh Mohammad Bilal Naseem
As there are several technologies introduced regarding to the monitoring and security in the modern world, there is need to bring an advancement and evolution in the pump automation and monitoring system that is to look at and manage the wastage of water because of water tank overflow. It also helps in filling the water tank automatically because of the busy lifestyle of the person or due to irresponsibility. Pump automation and monitoring senses the movement of the water and afterward shows the level of water in the water tank and sends data to the person with a pop-up message. In addition, the turbidity of the water will be checked before it is filled into the tank and stop filling the water if it finds the water has high Turbidity. It will also detect the leakage in the pipeline while filling the water tank and if found then notify the person immediately of the leakage point location.
{"title":"IOT based Research Proposal on Water Pump Automation System for Turbidity, Pipeline Leakage and Fluid Level Monitoring","authors":"Prasad Anand Apte, Shaikh Mohammad Bilal Naseem","doi":"10.1109/ICAST55766.2022.10039633","DOIUrl":"https://doi.org/10.1109/ICAST55766.2022.10039633","url":null,"abstract":"As there are several technologies introduced regarding to the monitoring and security in the modern world, there is need to bring an advancement and evolution in the pump automation and monitoring system that is to look at and manage the wastage of water because of water tank overflow. It also helps in filling the water tank automatically because of the busy lifestyle of the person or due to irresponsibility. Pump automation and monitoring senses the movement of the water and afterward shows the level of water in the water tank and sends data to the person with a pop-up message. In addition, the turbidity of the water will be checked before it is filled into the tank and stop filling the water if it finds the water has high Turbidity. It will also detect the leakage in the pipeline while filling the water tank and if found then notify the person immediately of the leakage point location.","PeriodicalId":225239,"journal":{"name":"2022 5th International Conference on Advances in Science and Technology (ICAST)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125233403","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 main wealth of India is farming and it has a huge share in the economy of the country. Since sugarcane is a recurrent crop, it is cultivated on a large scale in various states of India like Maharashtra, Uttar Pradesh, Tamil Nadu, Karnataka, Bihar, and many other states. Natural disasters such as floods and storms are the primary reasons for a damaging crop in that agricultural field and viruses or bacteria are the secondary reasons that infect a plant. It also decreases the quality due to infectious diseases. To maintain the quality of crops, diseases control is highly needed. Generally, diseases in crops are recognized by such farmers who have vast experience in farming also some agricultural scientists are helping them with disease identification. Sometimes due to changes in weather, it is difficult to identify variations in disease. It makes it difficult in identifying diseases in sugarcane. To solve this problem, we have proposed this system in this paper. For detecting diseases, we have used the Mobile Net v2 model as of now, which is majorly used in object detection
印度的主要财富是农业，它在该国经济中占有巨大的份额。由于甘蔗是一种经常性作物，在印度的许多邦，如马哈拉施特拉邦、北方邦、泰米尔纳德邦、卡纳塔克邦、比哈尔邦和许多其他邦，都有大规模种植。洪水和风暴等自然灾害是造成该农田作物受损的主要原因，病毒或细菌是感染植物的次要原因。由于传染病，它也降低了质量。为了保持农作物的品质，防治病害是非常必要的。一般来说，农作物的疾病是由这些有丰富农业经验的农民识别出来的，一些农业科学家也在帮助他们识别疾病。有时由于天气的变化，很难确定疾病的变化。这给甘蔗病害的鉴定带来了困难。为了解决这一问题，本文提出了该系统。对于疾病检测，我们目前使用的是Mobile Net v2模型，主要用于对象检测
{"title":"Sugarcane Disease Detection using Deep Learning","authors":"Vaishali Wadhe, Rashmi Dongre, Yash Kankriya, Anish Kuckian","doi":"10.1109/ICAST55766.2022.10039670","DOIUrl":"https://doi.org/10.1109/ICAST55766.2022.10039670","url":null,"abstract":"The main wealth of India is farming and it has a huge share in the economy of the country. Since sugarcane is a recurrent crop, it is cultivated on a large scale in various states of India like Maharashtra, Uttar Pradesh, Tamil Nadu, Karnataka, Bihar, and many other states. Natural disasters such as floods and storms are the primary reasons for a damaging crop in that agricultural field and viruses or bacteria are the secondary reasons that infect a plant. It also decreases the quality due to infectious diseases. To maintain the quality of crops, diseases control is highly needed. Generally, diseases in crops are recognized by such farmers who have vast experience in farming also some agricultural scientists are helping them with disease identification. Sometimes due to changes in weather, it is difficult to identify variations in disease. It makes it difficult in identifying diseases in sugarcane. To solve this problem, we have proposed this system in this paper. For detecting diseases, we have used the Mobile Net v2 model as of now, which is majorly used in object detection","PeriodicalId":225239,"journal":{"name":"2022 5th International Conference on Advances in Science and Technology (ICAST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131003904","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 : 2022-12-02DOI: 10.1109/ICAST55766.2022.10039600
Dharmik Joshi, A. Gaonkar, J. Bharambe, Abhijit Patil
Breast cancer is one among the various vulnerable types of cancer, after skin cancer and lung cancer. Although deaths from breast cancer have decreased over the years, it is still the major leading causes of women deaths of all races. Many research efforts have been taken to prevent breast cancer by using different breast cancer biomarkers in the last few decades. “Mammographic Breast Density” is amongst various significant markers utilized for the prevention of breast cancer. As there is an increase in the “Mammographic Breast Density”, the mammograms sensitivity also decreases causing wrong prediction of breast cancer. The primal motive behind this article is to study all the research innovations for Mammographic Breast Density classification. This survey article covered all Deep Learning methods proposed for mammographic breast density classification. From 2010-2017 there is an inclination towards the Machine Learning approach, and from 2017 onwards, there is more research inclination towards the Deep Learning approach. Statistics of classification accuracy of Deep Learning is in between 86%-98.87%. Due to the variations, no methods were found so precise and accurate. Hence, current mammographic breast density assessment is subjective, thus raising the need to develop an accurate and accurate mammographic Breast Density classification tool suitable for clinical practice. Implementing a successful CAD system for breast density classification is a social need and can act as a supporting mechanism for the precise classification of mammograms. More research efforts are required in this area to reduce faulty predictions.
{"title":"Deep Learning Approach for Mammographic Breast Density Classification and Cancer Risk Prediction","authors":"Dharmik Joshi, A. Gaonkar, J. Bharambe, Abhijit Patil","doi":"10.1109/ICAST55766.2022.10039600","DOIUrl":"https://doi.org/10.1109/ICAST55766.2022.10039600","url":null,"abstract":"Breast cancer is one among the various vulnerable types of cancer, after skin cancer and lung cancer. Although deaths from breast cancer have decreased over the years, it is still the major leading causes of women deaths of all races. Many research efforts have been taken to prevent breast cancer by using different breast cancer biomarkers in the last few decades. “Mammographic Breast Density” is amongst various significant markers utilized for the prevention of breast cancer. As there is an increase in the “Mammographic Breast Density”, the mammograms sensitivity also decreases causing wrong prediction of breast cancer. The primal motive behind this article is to study all the research innovations for Mammographic Breast Density classification. This survey article covered all Deep Learning methods proposed for mammographic breast density classification. From 2010-2017 there is an inclination towards the Machine Learning approach, and from 2017 onwards, there is more research inclination towards the Deep Learning approach. Statistics of classification accuracy of Deep Learning is in between 86%-98.87%. Due to the variations, no methods were found so precise and accurate. Hence, current mammographic breast density assessment is subjective, thus raising the need to develop an accurate and accurate mammographic Breast Density classification tool suitable for clinical practice. Implementing a successful CAD system for breast density classification is a social need and can act as a supporting mechanism for the precise classification of mammograms. More research efforts are required in this area to reduce faulty predictions.","PeriodicalId":225239,"journal":{"name":"2022 5th International Conference on Advances in Science and Technology (ICAST)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129451875","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 : 2022-12-02DOI: 10.1109/ICAST55766.2022.10039605
Priyanka R Kamble, U. Kulkarni
The field of online recruitment systems is becoming more popular in Artificial Intelligence because it is beneficial for both candidates and interviewers as it saves time and energy. In the manual process of recruitment, fitting the job specifications according to the resume and selecting the perfect candidate as per their behavior is a difficult task. With uses in psychiatric evaluations, human operator, and personality computing, automatic analysis of video interviews and automatic extraction of resumes for recognizing personality traits has consequently emerged as an important research subject. Convolutional neural network (CNN) models were introduced in some earlier studies as a result of developments in Deep Learning (DL)-based computer vision and pattern recognition. These models are capable of accurately predicting human non-verbal cues when used in conjunction with a web camera. In this paper, the candidate and interviewer both can achieve their goals by the one system. As per job specification included in the resume, candidates can get clarification of the job title and test their own personality by giving a psychometric assessment included in the system. The end-to-end AI interviewing system is developed with the aid of asynchronous video interview (AVI) processing, and automatic personality identification (APR) is carried out using features gleaned from the AVIs by the Tensorflow AI engine. The result shows that the interviewer can successfully recognize the Big five personality traits of a candidate at an accuracy above 95%. In the automatic personality recognition the semi supervised DL approach gives better performance.
{"title":"An Innovative Approach of Personality Recognition for E-Recruitment","authors":"Priyanka R Kamble, U. Kulkarni","doi":"10.1109/ICAST55766.2022.10039605","DOIUrl":"https://doi.org/10.1109/ICAST55766.2022.10039605","url":null,"abstract":"The field of online recruitment systems is becoming more popular in Artificial Intelligence because it is beneficial for both candidates and interviewers as it saves time and energy. In the manual process of recruitment, fitting the job specifications according to the resume and selecting the perfect candidate as per their behavior is a difficult task. With uses in psychiatric evaluations, human operator, and personality computing, automatic analysis of video interviews and automatic extraction of resumes for recognizing personality traits has consequently emerged as an important research subject. Convolutional neural network (CNN) models were introduced in some earlier studies as a result of developments in Deep Learning (DL)-based computer vision and pattern recognition. These models are capable of accurately predicting human non-verbal cues when used in conjunction with a web camera. In this paper, the candidate and interviewer both can achieve their goals by the one system. As per job specification included in the resume, candidates can get clarification of the job title and test their own personality by giving a psychometric assessment included in the system. The end-to-end AI interviewing system is developed with the aid of asynchronous video interview (AVI) processing, and automatic personality identification (APR) is carried out using features gleaned from the AVIs by the Tensorflow AI engine. The result shows that the interviewer can successfully recognize the Big five personality traits of a candidate at an accuracy above 95%. In the automatic personality recognition the semi supervised DL approach gives better performance.","PeriodicalId":225239,"journal":{"name":"2022 5th International Conference on Advances in Science and Technology (ICAST)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123109596","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 : 2022-12-02DOI: 10.1109/ICAST55766.2022.10039654
Sanjay M. Vidhani, A. Vidhate
5G networks control new technical concepts like ultra-low latency, ultra-high bandwidth, ultra-reliability, ultra-massive device access and manage the continually rising demands of diverse applications. The architecture and hierarchical framework of 5G networks need to understand and introduced. Security has been a primary focus for many telecommunications businesses since risks might have serious consequences for their front-line applications. A new class of security issues will be raised by network softwarization and new technologies including software-defined networking, network function virtualization, mobile-access edge computing and network slicing. This paper emphasizes the security concerns that 5G will raise and demands urgent security fixes. We also discuss the future of safe 5G systems and security fixes for these problems. To increase network security, DDOS attacks are detected and mitigated using a variety of SDN approaches.
{"title":"Security Challenges in 5G Network: A technical features survey and analysis","authors":"Sanjay M. Vidhani, A. Vidhate","doi":"10.1109/ICAST55766.2022.10039654","DOIUrl":"https://doi.org/10.1109/ICAST55766.2022.10039654","url":null,"abstract":"5G networks control new technical concepts like ultra-low latency, ultra-high bandwidth, ultra-reliability, ultra-massive device access and manage the continually rising demands of diverse applications. The architecture and hierarchical framework of 5G networks need to understand and introduced. Security has been a primary focus for many telecommunications businesses since risks might have serious consequences for their front-line applications. A new class of security issues will be raised by network softwarization and new technologies including software-defined networking, network function virtualization, mobile-access edge computing and network slicing. This paper emphasizes the security concerns that 5G will raise and demands urgent security fixes. We also discuss the future of safe 5G systems and security fixes for these problems. To increase network security, DDOS attacks are detected and mitigated using a variety of SDN approaches.","PeriodicalId":225239,"journal":{"name":"2022 5th International Conference on Advances in Science and Technology (ICAST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128954081","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 : 2022-12-02DOI: 10.1109/ICAST55766.2022.10039599
Nisha Gharpure, Aradhana Rai
Databases are at the heart of modern applications and any threats to them can seriously endanger the safety and functionality of applications relying on the services offered by a DBMS. It is therefore pertinent to identify key risks to the secure operation of a database system. This paper identifies the key risks, namely, SQL injection, weak audit trails, access management issues and issues with encryption. A malicious actor can get help from any of these issues. It can compromise integrity, availability and confidentiality of the data present in database systems. The paper also identifies various means and ways to defend against these issues and remedy them. This paper then proceeds to identify from the literature, the potential solutions to these ameliorate the threat from these vulnerabilities. It proposes the usage of encryption to protect the data from being breached and leveraging encrypted databases such as CryptoDB. Better access control norms are suggested to prevent unauthorized access, modification and deletion of the data. The paper also recommends ways to prevent SQL injection attacks through techniques such as prepared statements.
{"title":"Vulnerabilities and Threat Management in Relational Database Management Systems","authors":"Nisha Gharpure, Aradhana Rai","doi":"10.1109/ICAST55766.2022.10039599","DOIUrl":"https://doi.org/10.1109/ICAST55766.2022.10039599","url":null,"abstract":"Databases are at the heart of modern applications and any threats to them can seriously endanger the safety and functionality of applications relying on the services offered by a DBMS. It is therefore pertinent to identify key risks to the secure operation of a database system. This paper identifies the key risks, namely, SQL injection, weak audit trails, access management issues and issues with encryption. A malicious actor can get help from any of these issues. It can compromise integrity, availability and confidentiality of the data present in database systems. The paper also identifies various means and ways to defend against these issues and remedy them. This paper then proceeds to identify from the literature, the potential solutions to these ameliorate the threat from these vulnerabilities. It proposes the usage of encryption to protect the data from being breached and leveraging encrypted databases such as CryptoDB. Better access control norms are suggested to prevent unauthorized access, modification and deletion of the data. The paper also recommends ways to prevent SQL injection attacks through techniques such as prepared statements.","PeriodicalId":225239,"journal":{"name":"2022 5th International Conference on Advances in Science and Technology (ICAST)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115864541","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 : 2022-12-02DOI: 10.1109/ICAST55766.2022.10039625
E. Johri, Leesa Dharod, Rasika Joshi, Shreya Kulkarni, V. Kundle
Visual Question Answering or VQA is a technique used in diverse domains ranging from simple visual questions and answers on short videos to security. Here in this paper, we talk about the video captcha that will be deployed for user authentication. Randomly any short video of length 10 to 20 seconds will be displayed and automated questions and answers will be generated by the system using AI and ML. Automated Programs have maliciously affected gateways such as login, registering etc. Therefore, in today's environment it is necessary to deploy such security programs that can recognize the objects in a video and generate automated MCQs real time that can be of context like the object movements, color, background etc. The features in the video highlighted will be recorded for generating MCQs based on the short videos. These videos can be random in nature. They can be taken from any official websites or even from your own local computer with prior permission from the user. The format of the video must be kept as constant every time and must be cross checked before flashing it to the user. Once our system identifies the captcha and determines the authenticity of a user, the other website in which the user wants to login, can skip the step of captcha verification as it will be done by our system. A session will be maintained for the user, eliminating the hassle of authenticating themselves again and again for no reason. Once the video will be flashed for an IP address and if the answers marked by the user for the current video captcha are correct, we will add the information like the IP address, the video and the questions in our database to avoid repeating the same captcha for the same IP address. In this paper, we proposed the methodology of execution of the aforementioned and will discuss the benefits and limitations of video captcha along with the visual questions and answering.
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