Pub Date : 2023-05-26DOI: 10.1109/ICSCCC58608.2023.10177024
Susmita Mahato
When two people want to communicate secretly, they can use steganography, which entails hiding the message within a seemingly innocuous medium. This research proposes a novel approach to encrypt hidden data in the famous Snake game, which is available online. The method builds a stego-snake game resembling online snake games with several information-concealing features. This paper primarily focuses on hiding messages in the snakes' food grid, followed by a simulation of a stego-snake game with an embedded message.
{"title":"Snake-Stega: A snake game-based steganography scheme","authors":"Susmita Mahato","doi":"10.1109/ICSCCC58608.2023.10177024","DOIUrl":"https://doi.org/10.1109/ICSCCC58608.2023.10177024","url":null,"abstract":"When two people want to communicate secretly, they can use steganography, which entails hiding the message within a seemingly innocuous medium. This research proposes a novel approach to encrypt hidden data in the famous Snake game, which is available online. The method builds a stego-snake game resembling online snake games with several information-concealing features. This paper primarily focuses on hiding messages in the snakes' food grid, followed by a simulation of a stego-snake game with an embedded message.","PeriodicalId":359466,"journal":{"name":"2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115466369","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 : 2023-05-26DOI: 10.1109/ICSCCC58608.2023.10176974
S. Vaishnavi, Nandikaa Shanmugam, Galla Kiran, A. Priyadharshini
Adopting the same fertilizer gives minimum yield to the farmers as soil properties have changed drastically due to the change in environmental condition. In literature, different algorithmic analysis has been carried out to predict the fertilizer considering various factors, however, there is a gap in identifying every possible factor relevant to fertilizer recommendation. Hence, in our proposed work, we have utilized various soil and environmental factors like Nitrogen, Phosphorus and Potassium values, humidity, rainfall, weather condition and performed a dependency analysis of these factors to give a more accurate fertilizer prediction so as to enhance the crop yield. Algorithms such as Random Forest, Decision Tree, Support Vector Machine (SVM), Naïve Bayes (NB) and Logistic Regression (LR) have been explored to study the suitability of these algorithms in fertilizer prediction. The presented algorithms are compared based on the performance metrics such as accuracy, F1 score, Recall and precision. It is found that, among other algorithms, SVM performed better with maximum accuracy of 97% when all the factors are taken into account.
{"title":"Dependency analysis of various factors and ML models related to Fertilizer Recommendation","authors":"S. Vaishnavi, Nandikaa Shanmugam, Galla Kiran, A. Priyadharshini","doi":"10.1109/ICSCCC58608.2023.10176974","DOIUrl":"https://doi.org/10.1109/ICSCCC58608.2023.10176974","url":null,"abstract":"Adopting the same fertilizer gives minimum yield to the farmers as soil properties have changed drastically due to the change in environmental condition. In literature, different algorithmic analysis has been carried out to predict the fertilizer considering various factors, however, there is a gap in identifying every possible factor relevant to fertilizer recommendation. Hence, in our proposed work, we have utilized various soil and environmental factors like Nitrogen, Phosphorus and Potassium values, humidity, rainfall, weather condition and performed a dependency analysis of these factors to give a more accurate fertilizer prediction so as to enhance the crop yield. Algorithms such as Random Forest, Decision Tree, Support Vector Machine (SVM), Naïve Bayes (NB) and Logistic Regression (LR) have been explored to study the suitability of these algorithms in fertilizer prediction. The presented algorithms are compared based on the performance metrics such as accuracy, F1 score, Recall and precision. It is found that, among other algorithms, SVM performed better with maximum accuracy of 97% when all the factors are taken into account.","PeriodicalId":359466,"journal":{"name":"2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121045722","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}
Any computer vision application must first improve a picture before continuing to process it color details losses during the enhancement process is a prevalent issue with most current techniques when applied to photographs that are essentially unnoticeable the qualitatively undetectable image should be improved while maintaining its freshness and coloring. Histogram equalization, a traditional approach of contrast enhancement, resulting in more than enhancement of something like the picture, particularly one with poorer resolution. The objective of this research is to develop an innovative fuzzy inference system capable of enhancing the contrast of low-resolution photos while simultaneously addressing any existing limitations, existing techniques and segmenting the tumor in MRI images. The outcomes from the two methods are contrasted. Throughout this research, the technique results in a very tiny change in intensity value while maintaining the image's information about color and brightness. The method enhances striking contrast while preserving naturalness without introducing any artefacts. Active contour processing on these photos produces extremely accurate segmentation results. Mainly this is used to detect the tumor in MRI images with some basic morphological operations.
{"title":"Fuzzified Contrast Enhancement and Segmentation For Nearly Invisible Images","authors":"Zaheeruddin Syed, Kanneboina Siddhartha, Thota Rahul, Aragonda Sneha, Ellandala Jhansi, K. Suganthi","doi":"10.1109/ICSCCC58608.2023.10176516","DOIUrl":"https://doi.org/10.1109/ICSCCC58608.2023.10176516","url":null,"abstract":"Any computer vision application must first improve a picture before continuing to process it color details losses during the enhancement process is a prevalent issue with most current techniques when applied to photographs that are essentially unnoticeable the qualitatively undetectable image should be improved while maintaining its freshness and coloring. Histogram equalization, a traditional approach of contrast enhancement, resulting in more than enhancement of something like the picture, particularly one with poorer resolution. The objective of this research is to develop an innovative fuzzy inference system capable of enhancing the contrast of low-resolution photos while simultaneously addressing any existing limitations, existing techniques and segmenting the tumor in MRI images. The outcomes from the two methods are contrasted. Throughout this research, the technique results in a very tiny change in intensity value while maintaining the image's information about color and brightness. The method enhances striking contrast while preserving naturalness without introducing any artefacts. Active contour processing on these photos produces extremely accurate segmentation results. Mainly this is used to detect the tumor in MRI images with some basic morphological operations.","PeriodicalId":359466,"journal":{"name":"2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122389013","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 : 2023-05-26DOI: 10.1109/ICSCCC58608.2023.10176502
V. K. Gupta, Avdhesh Gupta, Vikas Tyagi, Priyank Pandey, Richa Gupta, D. Kumar
Due to COVID outbreak face mask detection in the industry as well as in any gathering playing an important role. Either person worn facemask or not worn. In this CORONA situation, Use of a face mask is one such preventative that is crucial. Facial recognition technologies are now used by many businesses and organizations for their own general purposes. We are all aware of how important it has become to always wear a mask when we travel. However, as we all know, it is impossible to monitor who is wearing a mask and who is not. If some person who worn the mask, then it is not confirmed whether he/she worn it correctly or not. We make the use of AI in our daily life. We achieve this with the help of a deep learning, where we train the model using various convolution neural network approaches and created a hybrid model using bagging-based ensemble learning. Here, detection is performed based on voting-based classification so that we can enhance the accuracy of our model. We have found dataset from MAFA and Kaggle. The hybrid approach of C2N model achieved exceptional accuracy with the use of a dataset of face mask detection that contains both with and without face mask photographs. In our multilevel facemask detection system at the first level our model will predict whether the person worn facemask or not and at its second level it will predict the correctness of facemask, whether it is worn correct or not.
{"title":"Multilevel Face Mask Detection System using Ensemble based Convolution Neural Network","authors":"V. K. Gupta, Avdhesh Gupta, Vikas Tyagi, Priyank Pandey, Richa Gupta, D. Kumar","doi":"10.1109/ICSCCC58608.2023.10176502","DOIUrl":"https://doi.org/10.1109/ICSCCC58608.2023.10176502","url":null,"abstract":"Due to COVID outbreak face mask detection in the industry as well as in any gathering playing an important role. Either person worn facemask or not worn. In this CORONA situation, Use of a face mask is one such preventative that is crucial. Facial recognition technologies are now used by many businesses and organizations for their own general purposes. We are all aware of how important it has become to always wear a mask when we travel. However, as we all know, it is impossible to monitor who is wearing a mask and who is not. If some person who worn the mask, then it is not confirmed whether he/she worn it correctly or not. We make the use of AI in our daily life. We achieve this with the help of a deep learning, where we train the model using various convolution neural network approaches and created a hybrid model using bagging-based ensemble learning. Here, detection is performed based on voting-based classification so that we can enhance the accuracy of our model. We have found dataset from MAFA and Kaggle. The hybrid approach of C2N model achieved exceptional accuracy with the use of a dataset of face mask detection that contains both with and without face mask photographs. In our multilevel facemask detection system at the first level our model will predict whether the person worn facemask or not and at its second level it will predict the correctness of facemask, whether it is worn correct or not.","PeriodicalId":359466,"journal":{"name":"2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129583817","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 : 2023-05-26DOI: 10.1109/ICSCCC58608.2023.10176955
Doney Abraham, Sule YAYILGAN YILDIRIM, Filip Holík, S. Acevedo, Alemayehu Gebremedhin
Transforming electrical grids into digital systems has brought many advantages, but it has also introduced new vulnerabilities that cyber attackers can exploit. Therefore, early detection of these attacks is crucial to minimize the impact on power grid operations. This paper presents the results of our investigation into the simulation and detection of cyber attacks in digital substations. Our study focuses on comparing multiple machine learning algorithms for detecting replay attacks and false data injections. The results of our study show that the best model for replay attack detection is the Logistic Regression with an accuracy of 94%. On the other hand, for false data injection detection, multiple models show high precision, recall, F1-score, and accuracy, with the best model in terms of computation time being Support Vector Machine. Our findings provide valuable insights into using machine learning algorithms to simulate and detect cyber attacks in digital substations.
{"title":"Cyber Attack Simulation and Detection in Digital Substation","authors":"Doney Abraham, Sule YAYILGAN YILDIRIM, Filip Holík, S. Acevedo, Alemayehu Gebremedhin","doi":"10.1109/ICSCCC58608.2023.10176955","DOIUrl":"https://doi.org/10.1109/ICSCCC58608.2023.10176955","url":null,"abstract":"Transforming electrical grids into digital systems has brought many advantages, but it has also introduced new vulnerabilities that cyber attackers can exploit. Therefore, early detection of these attacks is crucial to minimize the impact on power grid operations. This paper presents the results of our investigation into the simulation and detection of cyber attacks in digital substations. Our study focuses on comparing multiple machine learning algorithms for detecting replay attacks and false data injections. The results of our study show that the best model for replay attack detection is the Logistic Regression with an accuracy of 94%. On the other hand, for false data injection detection, multiple models show high precision, recall, F1-score, and accuracy, with the best model in terms of computation time being Support Vector Machine. Our findings provide valuable insights into using machine learning algorithms to simulate and detect cyber attacks in digital substations.","PeriodicalId":359466,"journal":{"name":"2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129923008","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 : 2023-05-26DOI: 10.1109/ICSCCC58608.2023.10176985
Gourav Singhal, Amritpal Singh
In the modern world, the use of IoT devices and emerging technologies are contributing to a daily escalation in data generation. Numerous novel approaches are arising to handle such copious amounts of data. The utilization of this data in making decisions related to agriculture, combined with the integration of smart agriculture techniques, can enhance the conventional agricultural system. Smart agriculture relies heavily on the seamless integration and coordination of various devices. Data retrieval, storage, and analysis are some of the crucial tasks in this field. Data security, privacy, real-time decision-making, and semi-structured and unstructured data are some of the challenges and limitations of using traditional approaches when dealing with a high amount of generated data. For handling data and getting a real-time response in smart agriculture Probabilistic Data Structures (PDS) are used as an effective and efficient solution for various applications. Providing a thorough analysis of how PDS applications are utilized in the realm of smart agriculture is the main objective of this paper. This study takes an in-depth look into the important area of smart agriculture, examining its inception, obstacles, areas of research that require further exploration, and possible future paths. This paper aims to provide a comprehensive examination of PDS in smart agriculture, catering to readers and researchers who seek to expand their knowledge in this area. Additionally, this paper aims to identify potential research opportunities within this field.
{"title":"Probabilistic Data Structure in smart agriculture","authors":"Gourav Singhal, Amritpal Singh","doi":"10.1109/ICSCCC58608.2023.10176985","DOIUrl":"https://doi.org/10.1109/ICSCCC58608.2023.10176985","url":null,"abstract":"In the modern world, the use of IoT devices and emerging technologies are contributing to a daily escalation in data generation. Numerous novel approaches are arising to handle such copious amounts of data. The utilization of this data in making decisions related to agriculture, combined with the integration of smart agriculture techniques, can enhance the conventional agricultural system. Smart agriculture relies heavily on the seamless integration and coordination of various devices. Data retrieval, storage, and analysis are some of the crucial tasks in this field. Data security, privacy, real-time decision-making, and semi-structured and unstructured data are some of the challenges and limitations of using traditional approaches when dealing with a high amount of generated data. For handling data and getting a real-time response in smart agriculture Probabilistic Data Structures (PDS) are used as an effective and efficient solution for various applications. Providing a thorough analysis of how PDS applications are utilized in the realm of smart agriculture is the main objective of this paper. This study takes an in-depth look into the important area of smart agriculture, examining its inception, obstacles, areas of research that require further exploration, and possible future paths. This paper aims to provide a comprehensive examination of PDS in smart agriculture, catering to readers and researchers who seek to expand their knowledge in this area. Additionally, this paper aims to identify potential research opportunities within this field.","PeriodicalId":359466,"journal":{"name":"2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129270235","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 : 2023-05-26DOI: 10.1109/ICSCCC58608.2023.10176509
Kusum, Vijay Kumar
The process of detecting whether two people in a given pair of face pictures are biologically related or not is known as facial kinship verification. Deep learning-based techniques, in particular Convolutional Neural Networks (CNNs), have excelled at this challenge in recent years. In this article, we will compare a number of pre-trained CNN models and hybrid models to one another. Also, try to check performance by ensembling these models. The models are trained on the large-scale KinFaceW-I dataset and evaluated on the KinFaceW- II dataset, achieving state-of-the-art performance. In order to evaluate the performance, we have made our new dataset similar to the KinFaceW dataset. Additionally, our technique exhibits resilience to a variety of facial variables, including alterations in age, posture, and expression. Overall, can get a potential answer to the problem of facial kinship verification, which is crucial in numerous disciplines such as forensic investigation, family history research, and social media analysis. At last paper identified a ensembled or single models which work well on KinFace dataset and new dataset introduced by this paper.
{"title":"Convolution Neural Network for Facial Kinship Verification","authors":"Kusum, Vijay Kumar","doi":"10.1109/ICSCCC58608.2023.10176509","DOIUrl":"https://doi.org/10.1109/ICSCCC58608.2023.10176509","url":null,"abstract":"The process of detecting whether two people in a given pair of face pictures are biologically related or not is known as facial kinship verification. Deep learning-based techniques, in particular Convolutional Neural Networks (CNNs), have excelled at this challenge in recent years. In this article, we will compare a number of pre-trained CNN models and hybrid models to one another. Also, try to check performance by ensembling these models. The models are trained on the large-scale KinFaceW-I dataset and evaluated on the KinFaceW- II dataset, achieving state-of-the-art performance. In order to evaluate the performance, we have made our new dataset similar to the KinFaceW dataset. Additionally, our technique exhibits resilience to a variety of facial variables, including alterations in age, posture, and expression. Overall, can get a potential answer to the problem of facial kinship verification, which is crucial in numerous disciplines such as forensic investigation, family history research, and social media analysis. At last paper identified a ensembled or single models which work well on KinFace dataset and new dataset introduced by this paper.","PeriodicalId":359466,"journal":{"name":"2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124108514","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 : 2023-05-26DOI: 10.1109/ICSCCC58608.2023.10176853
S. Agrawal, Pragati Agrawal
Pneumonia is a viral infection affecting many people, especially in underdeveloped and impoverished nations where contaminated, crowded, and unhygienic living conditions are common and inadequate healthcare infrastructures. Recognizing pneumonia immediately is a challenging step that can increase survival odds and allow for early-stage treatment. The successful construction of prediction models makes use of the artificial intelligence discipline of deep learning. There are many approaches to identifying pneumonia, including CT scans, pulse oximetry, and many others, but X-ray tomography is the most popular method. However, reviewing chest X-rays (CXR) is difficult and vulnerable to subjectivity variations. Using x-ray chest images, this study suggests a novel deep learning-based architecture for the quick diagnosis of covid-19 and pneumonia cases. As our basic model, we use the CNN transfer learning models VGG16, ResNet50, and InceptionV3. To adjust the hyperparameters of our model, we use random search optimization approach.
{"title":"Detection of Pneumonia Cases from X-ray Chest Images using Deep Learning Based on Transfer Learning CNN and Hyperparameter Optimization","authors":"S. Agrawal, Pragati Agrawal","doi":"10.1109/ICSCCC58608.2023.10176853","DOIUrl":"https://doi.org/10.1109/ICSCCC58608.2023.10176853","url":null,"abstract":"Pneumonia is a viral infection affecting many people, especially in underdeveloped and impoverished nations where contaminated, crowded, and unhygienic living conditions are common and inadequate healthcare infrastructures. Recognizing pneumonia immediately is a challenging step that can increase survival odds and allow for early-stage treatment. The successful construction of prediction models makes use of the artificial intelligence discipline of deep learning. There are many approaches to identifying pneumonia, including CT scans, pulse oximetry, and many others, but X-ray tomography is the most popular method. However, reviewing chest X-rays (CXR) is difficult and vulnerable to subjectivity variations. Using x-ray chest images, this study suggests a novel deep learning-based architecture for the quick diagnosis of covid-19 and pneumonia cases. As our basic model, we use the CNN transfer learning models VGG16, ResNet50, and InceptionV3. To adjust the hyperparameters of our model, we use random search optimization approach.","PeriodicalId":359466,"journal":{"name":"2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128078227","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 : 2023-05-26DOI: 10.1109/ICSCCC58608.2023.10176980
P. Patidar, D. Tomar, R. Pateriya, Y. K. Sharma
Agriculture is the most important source of livelihood. Crop segmentation has become an important role in precision agriculture which helps farmers to make decisions about crop damage and its production. However, it's a challenging task to achieve precision in the agriculture field. Drone Surveillance helps to achieve that crop yield assessment, crop damage, crop health, and other parameters. This paper focuses on image segmentation of crops, classified into categories like sparse and dense crops with the multitemporal data image taken by Drone. This model proposed and studied shows the loss percentage in crop identification by image segmentation process, it helps farmers to get good compensation for crops to survey through Drone (UAV) techniques. A detailed analysis with outcome of thisis explained further.
{"title":"Precision Agriculture: Crop Image Segmentation and Loss Evaluation through Drone Surveillance","authors":"P. Patidar, D. Tomar, R. Pateriya, Y. K. Sharma","doi":"10.1109/ICSCCC58608.2023.10176980","DOIUrl":"https://doi.org/10.1109/ICSCCC58608.2023.10176980","url":null,"abstract":"Agriculture is the most important source of livelihood. Crop segmentation has become an important role in precision agriculture which helps farmers to make decisions about crop damage and its production. However, it's a challenging task to achieve precision in the agriculture field. Drone Surveillance helps to achieve that crop yield assessment, crop damage, crop health, and other parameters. This paper focuses on image segmentation of crops, classified into categories like sparse and dense crops with the multitemporal data image taken by Drone. This model proposed and studied shows the loss percentage in crop identification by image segmentation process, it helps farmers to get good compensation for crops to survey through Drone (UAV) techniques. A detailed analysis with outcome of thisis explained further.","PeriodicalId":359466,"journal":{"name":"2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125638287","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 : 2023-05-26DOI: 10.1109/ICSCCC58608.2023.10176740
Priya Kohli, Sachin Sharma, Priya Matta
With its extraordinarily high speed, high computational cost, and significant requirement, 6G is an emerging technology. It has security and privacy issues as a result of being wireless and dynamic. Real-time data about legally registered cars and their drivers can be compromised by an unauthorized intrusion into a node over a fully connected network. Such actions could slow down the network or jeopardize its reliability. The unauthorized invasion of a node across a network is described in relation to many popular algorithms and strategies. Regressive along with the discussion of upcoming work, comparative examination of previously offered methodologies is conducted.
{"title":"Intrusion Detection Techniques For Security and Privacy of 6G Applications","authors":"Priya Kohli, Sachin Sharma, Priya Matta","doi":"10.1109/ICSCCC58608.2023.10176740","DOIUrl":"https://doi.org/10.1109/ICSCCC58608.2023.10176740","url":null,"abstract":"With its extraordinarily high speed, high computational cost, and significant requirement, 6G is an emerging technology. It has security and privacy issues as a result of being wireless and dynamic. Real-time data about legally registered cars and their drivers can be compromised by an unauthorized intrusion into a node over a fully connected network. Such actions could slow down the network or jeopardize its reliability. The unauthorized invasion of a node across a network is described in relation to many popular algorithms and strategies. Regressive along with the discussion of upcoming work, comparative examination of previously offered methodologies is conducted.","PeriodicalId":359466,"journal":{"name":"2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122262067","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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