Pub Date : 1900-01-01DOI: 10.5220/0010790400003167
D. S. Mwakapesa, Ye Li, Xiangtai Wang, Binbin Guo, Mao Yimin
: Machine learning is a very important in computer science field which has gained attention in numerous applications. This paper reviewed various machine learning methods including supervised and unsupervised learning and highlighted their applications, advantages and disadvantages in landslide susceptibility mapping. The review has also mentioned the challenges of machine learning algorithms for achieving higher performance accuracy from the supervised and unsupervised learning algorithms during landslide susceptibility. Moreover, highlights on the application of deep learning methods as the current research in landslide susceptibility mapping has also been reported. Finally, this paper argued the necessity of thorough preparation of relevant and enough data being significant important to obtain high performance results from the review methods.
{"title":"Review on the Application of Machine Learning Methods in Landslide Susceptibility Mapping","authors":"D. S. Mwakapesa, Ye Li, Xiangtai Wang, Binbin Guo, Mao Yimin","doi":"10.5220/0010790400003167","DOIUrl":"https://doi.org/10.5220/0010790400003167","url":null,"abstract":": Machine learning is a very important in computer science field which has gained attention in numerous applications. This paper reviewed various machine learning methods including supervised and unsupervised learning and highlighted their applications, advantages and disadvantages in landslide susceptibility mapping. The review has also mentioned the challenges of machine learning algorithms for achieving higher performance accuracy from the supervised and unsupervised learning algorithms during landslide susceptibility. Moreover, highlights on the application of deep learning methods as the current research in landslide susceptibility mapping has also been reported. Finally, this paper argued the necessity of thorough preparation of relevant and enough data being significant important to obtain high performance results from the review methods.","PeriodicalId":346698,"journal":{"name":"Proceedings of the 1st International Conference on Innovation in Computer and Information Science","volume":"165 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114508394","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 : 1900-01-01DOI: 10.5220/0010789900003167
G. Kabanda
The purpose of the research is to evaluate Machine Learning and Big Data Analytics 7 paradigms for use in Cybersecurity. Cybersecurity refers to a combination of technologies, 8 processes and operations that are framed to protect information systems, computers, devices, 9 programs, data and networks from internal or external threats, harm, damage, attacks or 10 unauthorized access. The main characteristic of Machine Learning (ML) is the automatic data 11 analysis of large data sets and production of models for the general relationships found among 12 data. ML algorithms, as part of Artificial Intelligence, can be clustered into supervised, 13 unsupervised, semi-supervised, and reinforcement learning algorithms. collected of opportunity 30 for cyber crimes and other forms cybersecurity risks, especially among interconnected devices now at household 31 level. 32 The research paper is focused on the Performance of Machine Learning and Big Data Analytics paradigms in 33 Cybersecurity and Cloud Computing platforms. The purpose of the research is to evaluate Machine Learning 34 and Big Data Analytics paradigms for use in Cybersecurity. This is relevant due to the rapid advances in 35 machine learning (ML) and deep learning (DL) as we explore the potency of efficient and cost-effective cloud 36 computing platforms and services. Evaluation of the attacks and defenses using ML and Big Data paradigms 37 is the key subject of this research paper. However, ML and DL techniques are resource intensive and require 38 huge volumes of training data with excellent performance, as is often provided by computational resources such 39 as high-performance graphics processing units (GPUs) and tensor processing units. Security issues related to 40 virtualisation, containerization, network monitoring, data protection and attack detection are interrogated whilst 41 strengthening AI/ML/DL security solutions that involve encryption, access control, firewall, authentication and 42 intrusion detection and prevention systems at the appropriate Fog/Cloud level. 43 Cybersecurity consolidates the confidentiality, integrity, and availability of computing resources, networks, 44 software programs, and data into a coherent collection of policies, technologies, processes, and techniques 45
{"title":"Performance of Machine Learning and Big Data Analytics Paradigms in Cyber-security and Cloud Computing Platforms","authors":"G. Kabanda","doi":"10.5220/0010789900003167","DOIUrl":"https://doi.org/10.5220/0010789900003167","url":null,"abstract":"The purpose of the research is to evaluate Machine Learning and Big Data Analytics 7 paradigms for use in Cybersecurity. Cybersecurity refers to a combination of technologies, 8 processes and operations that are framed to protect information systems, computers, devices, 9 programs, data and networks from internal or external threats, harm, damage, attacks or 10 unauthorized access. The main characteristic of Machine Learning (ML) is the automatic data 11 analysis of large data sets and production of models for the general relationships found among 12 data. ML algorithms, as part of Artificial Intelligence, can be clustered into supervised, 13 unsupervised, semi-supervised, and reinforcement learning algorithms. collected of opportunity 30 for cyber crimes and other forms cybersecurity risks, especially among interconnected devices now at household 31 level. 32 The research paper is focused on the Performance of Machine Learning and Big Data Analytics paradigms in 33 Cybersecurity and Cloud Computing platforms. The purpose of the research is to evaluate Machine Learning 34 and Big Data Analytics paradigms for use in Cybersecurity. This is relevant due to the rapid advances in 35 machine learning (ML) and deep learning (DL) as we explore the potency of efficient and cost-effective cloud 36 computing platforms and services. Evaluation of the attacks and defenses using ML and Big Data paradigms 37 is the key subject of this research paper. However, ML and DL techniques are resource intensive and require 38 huge volumes of training data with excellent performance, as is often provided by computational resources such 39 as high-performance graphics processing units (GPUs) and tensor processing units. Security issues related to 40 virtualisation, containerization, network monitoring, data protection and attack detection are interrogated whilst 41 strengthening AI/ML/DL security solutions that involve encryption, access control, firewall, authentication and 42 intrusion detection and prevention systems at the appropriate Fog/Cloud level. 43 Cybersecurity consolidates the confidentiality, integrity, and availability of computing resources, networks, 44 software programs, and data into a coherent collection of policies, technologies, processes, and techniques 45","PeriodicalId":346698,"journal":{"name":"Proceedings of the 1st International Conference on Innovation in Computer and Information Science","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116747676","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 : 1900-01-01DOI: 10.5220/0010789700003167
Shahzad Mujeeb, S. Chowdhary, Abhishek Srivastava, R. Majumdar, M. Kumar
: In recent times, security issues relating to unmanned aerial vehicles (UAVs) and drones have anticipated a staid attention from research communities in various domains in the form of networking, communication, and civilian as well as in defence zone. It has its widespread functionality in the area of agriculture, commerce, and transportation, the use of unmanned aerial vehicles (UAVs)/ drones, is increasing. The ground control systems (GCS) are used to remotely monitor UAVs over the network. Since UAVs are vulnerable to security risk, they become the targets of various attacks such as GPS spoofing, jamming attack, network attacks and many other forms so to tackle with such issues the prime concern will be to identify these attacks followed by to prevent the UAVs or drones from UAV attacks. On contrary network-controlled UAVs however are equally vulnerable to threats like DOS attacks, GPS spoofing etc. In this work a network surveillance approach is projected for UAV attack detection system by means of Snort. Snort uses a set of guidelines and rules set by the user itself to help in identifying the malicious network behaviour and to locate packets that fit them and create user warnings with those rules. It is an open-source tool that records traffic analysis and packets in real time.
{"title":"Unmanned Aerial Vehicle Attack Detection using Snort","authors":"Shahzad Mujeeb, S. Chowdhary, Abhishek Srivastava, R. Majumdar, M. Kumar","doi":"10.5220/0010789700003167","DOIUrl":"https://doi.org/10.5220/0010789700003167","url":null,"abstract":": In recent times, security issues relating to unmanned aerial vehicles (UAVs) and drones have anticipated a staid attention from research communities in various domains in the form of networking, communication, and civilian as well as in defence zone. It has its widespread functionality in the area of agriculture, commerce, and transportation, the use of unmanned aerial vehicles (UAVs)/ drones, is increasing. The ground control systems (GCS) are used to remotely monitor UAVs over the network. Since UAVs are vulnerable to security risk, they become the targets of various attacks such as GPS spoofing, jamming attack, network attacks and many other forms so to tackle with such issues the prime concern will be to identify these attacks followed by to prevent the UAVs or drones from UAV attacks. On contrary network-controlled UAVs however are equally vulnerable to threats like DOS attacks, GPS spoofing etc. In this work a network surveillance approach is projected for UAV attack detection system by means of Snort. Snort uses a set of guidelines and rules set by the user itself to help in identifying the malicious network behaviour and to locate packets that fit them and create user warnings with those rules. It is an open-source tool that records traffic analysis and packets in real time.","PeriodicalId":346698,"journal":{"name":"Proceedings of the 1st International Conference on Innovation in Computer and Information Science","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128979365","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 : 1900-01-01DOI: 10.5220/0010790100003167
Ritik Sharma, Gulzar Miglani, Nitin Sharma, Shivam Pundir, S. Chowdhary, Abhishek Srivastava, M. Kumar
{"title":"Energy Efficient Cool Roof System to Reduce Carbon Footprint","authors":"Ritik Sharma, Gulzar Miglani, Nitin Sharma, Shivam Pundir, S. Chowdhary, Abhishek Srivastava, M. Kumar","doi":"10.5220/0010790100003167","DOIUrl":"https://doi.org/10.5220/0010790100003167","url":null,"abstract":"","PeriodicalId":346698,"journal":{"name":"Proceedings of the 1st International Conference on Innovation in Computer and Information Science","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128377941","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 : 1900-01-01DOI: 10.5220/0010789500003167
Himani Kohli, Manoj Kumar, Anuj Rani
{"title":"Post COVID 19 Pandemic Impacts on Socio-economic Development","authors":"Himani Kohli, Manoj Kumar, Anuj Rani","doi":"10.5220/0010789500003167","DOIUrl":"https://doi.org/10.5220/0010789500003167","url":null,"abstract":"","PeriodicalId":346698,"journal":{"name":"Proceedings of the 1st International Conference on Innovation in Computer and Information Science","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126347231","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 : 1900-01-01DOI: 10.5220/0010789800003167
Arvind Garg, Rahul Saini, S. Rawat, S. Chowdhary, M. Kumar
: Abstract: During the coronavirus (COVID-19) pandemic, many people work from home and hold meetings online from the past decade video conferencing become more popular. It is very efficient, distance learning and very effective now a days. We have lots of channels for video conferencing like Zoom, Ms teams, hangouts and Jitsi meet. This can be operated over the mobile phones our personal laptops. But we do not have any application by which we can use the video conferencing over the Android Tv directly. There are many steps by which we can broadcast our mobile or laptop screen over the Android Tv but there is no such application by which we can directly use the application directly on the Android Tv screen. In this Research paper, by using the API integration of the Jitsi meet or connectivity, Android Tv application can be created. From the last year, IT sector becomes more powerful because of the Work from Home Policies due to Pandemic Situation. All the work is going online, which is called distance video conferencing. For the effective learning we need a big screen, which would be very effective for distance learning. With the help of The Android Tv video conferencing application USER can learn the things quickly and without any trouble over the big screen. It will be very helpful for the students at Schools, Universities and for the employees of corporate world.
{"title":"Android TV App for Video Conferencing","authors":"Arvind Garg, Rahul Saini, S. Rawat, S. Chowdhary, M. Kumar","doi":"10.5220/0010789800003167","DOIUrl":"https://doi.org/10.5220/0010789800003167","url":null,"abstract":": Abstract: During the coronavirus (COVID-19) pandemic, many people work from home and hold meetings online from the past decade video conferencing become more popular. It is very efficient, distance learning and very effective now a days. We have lots of channels for video conferencing like Zoom, Ms teams, hangouts and Jitsi meet. This can be operated over the mobile phones our personal laptops. But we do not have any application by which we can use the video conferencing over the Android Tv directly. There are many steps by which we can broadcast our mobile or laptop screen over the Android Tv but there is no such application by which we can directly use the application directly on the Android Tv screen. In this Research paper, by using the API integration of the Jitsi meet or connectivity, Android Tv application can be created. From the last year, IT sector becomes more powerful because of the Work from Home Policies due to Pandemic Situation. All the work is going online, which is called distance video conferencing. For the effective learning we need a big screen, which would be very effective for distance learning. With the help of The Android Tv video conferencing application USER can learn the things quickly and without any trouble over the big screen. It will be very helpful for the students at Schools, Universities and for the employees of corporate world.","PeriodicalId":346698,"journal":{"name":"Proceedings of the 1st International Conference on Innovation in Computer and Information Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125780329","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 : 1900-01-01DOI: 10.5220/0010789600003167
Anubhav Bose, Chetna Choudhary, S. Chowdhary, M. Kumar
{"title":"Survey on Mobile Banking and e-Wallet Usage and its Security Concerns","authors":"Anubhav Bose, Chetna Choudhary, S. Chowdhary, M. Kumar","doi":"10.5220/0010789600003167","DOIUrl":"https://doi.org/10.5220/0010789600003167","url":null,"abstract":"","PeriodicalId":346698,"journal":{"name":"Proceedings of the 1st International Conference on Innovation in Computer and Information Science","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121435210","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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