Online Social Network Security: A Comparative Review Using Machine Learning and Deep Learning
Abstract In the present era, Online Social Networking has become an important phenomenon in human society. However, a large section of users are not aware of the security and privacy concerns involve in it. People have a tendency to publish sensitive and private information for example date of birth...
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Gespeichert in:
| Autor*in: |
Kumar, Chanchal [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Neural processing letters - Springer US, 1994, 53(2021), 1 vom: 05. Jan., Seite 843-861 |
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| Übergeordnetes Werk: |
volume:53 ; year:2021 ; number:1 ; day:05 ; month:01 ; pages:843-861 |
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10.1007/s11063-020-10416-3 |
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| 520 | |a Abstract In the present era, Online Social Networking has become an important phenomenon in human society. However, a large section of users are not aware of the security and privacy concerns involve in it. People have a tendency to publish sensitive and private information for example date of birth, mobile numbers, places checked-in, live locations, emotions, name of their spouse and other family members, etc. that may potentially prove disastrous. By monitoring their social network updates, the cyber attackers, first, collect the user’s public information which is further used to acquire their confidential information like banking details, etc. and to launch security attacks e.g. fake identity attack. Such attacks or information leaks may gravely affect their life. In this technology-laden era, it is imperative for users must be well aware of the potential risks involved in online social networks. This paper comprehensively surveys the evolution of the online social networks, their associated risks and solutions. The various security models and the state of the art algorithms have been discussed along with a comparative meta-analysis using machine learning, deep learning, and statistical testing to recommend a better solution. | ||
| 650 | 4 | |a Online social networks | |
| 650 | 4 | |a Attacks | |
| 650 | 4 | |a Security | |
| 650 | 4 | |a Malware analysis | |
| 650 | 4 | |a Feature extraction | |
| 650 | 4 | |a Big data | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Deep learning | |
| 700 | 1 | |a Bharati, Taran Singh |4 aut | |
| 700 | 1 | |a Prakash, Shiv |4 aut | |
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10.1007/s11063-020-10416-3 doi (DE-627)OLC2124185314 (DE-He213)s11063-020-10416-3-p DE-627 ger DE-627 rakwb eng 000 VZ Kumar, Chanchal verfasserin aut Online Social Network Security: A Comparative Review Using Machine Learning and Deep Learning 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract In the present era, Online Social Networking has become an important phenomenon in human society. However, a large section of users are not aware of the security and privacy concerns involve in it. People have a tendency to publish sensitive and private information for example date of birth, mobile numbers, places checked-in, live locations, emotions, name of their spouse and other family members, etc. that may potentially prove disastrous. By monitoring their social network updates, the cyber attackers, first, collect the user’s public information which is further used to acquire their confidential information like banking details, etc. and to launch security attacks e.g. fake identity attack. Such attacks or information leaks may gravely affect their life. In this technology-laden era, it is imperative for users must be well aware of the potential risks involved in online social networks. This paper comprehensively surveys the evolution of the online social networks, their associated risks and solutions. The various security models and the state of the art algorithms have been discussed along with a comparative meta-analysis using machine learning, deep learning, and statistical testing to recommend a better solution. Online social networks Attacks Security Malware analysis Feature extraction Big data Machine learning Deep learning Bharati, Taran Singh aut Prakash, Shiv aut Enthalten in Neural processing letters Springer US, 1994 53(2021), 1 vom: 05. Jan., Seite 843-861 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:53 year:2021 number:1 day:05 month:01 pages:843-861 https://doi.org/10.1007/s11063-020-10416-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT AR 53 2021 1 05 01 843-861 |
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10.1007/s11063-020-10416-3 doi (DE-627)OLC2124185314 (DE-He213)s11063-020-10416-3-p DE-627 ger DE-627 rakwb eng 000 VZ Kumar, Chanchal verfasserin aut Online Social Network Security: A Comparative Review Using Machine Learning and Deep Learning 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract In the present era, Online Social Networking has become an important phenomenon in human society. However, a large section of users are not aware of the security and privacy concerns involve in it. People have a tendency to publish sensitive and private information for example date of birth, mobile numbers, places checked-in, live locations, emotions, name of their spouse and other family members, etc. that may potentially prove disastrous. By monitoring their social network updates, the cyber attackers, first, collect the user’s public information which is further used to acquire their confidential information like banking details, etc. and to launch security attacks e.g. fake identity attack. Such attacks or information leaks may gravely affect their life. In this technology-laden era, it is imperative for users must be well aware of the potential risks involved in online social networks. This paper comprehensively surveys the evolution of the online social networks, their associated risks and solutions. The various security models and the state of the art algorithms have been discussed along with a comparative meta-analysis using machine learning, deep learning, and statistical testing to recommend a better solution. Online social networks Attacks Security Malware analysis Feature extraction Big data Machine learning Deep learning Bharati, Taran Singh aut Prakash, Shiv aut Enthalten in Neural processing letters Springer US, 1994 53(2021), 1 vom: 05. Jan., Seite 843-861 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:53 year:2021 number:1 day:05 month:01 pages:843-861 https://doi.org/10.1007/s11063-020-10416-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT AR 53 2021 1 05 01 843-861 |
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10.1007/s11063-020-10416-3 doi (DE-627)OLC2124185314 (DE-He213)s11063-020-10416-3-p DE-627 ger DE-627 rakwb eng 000 VZ Kumar, Chanchal verfasserin aut Online Social Network Security: A Comparative Review Using Machine Learning and Deep Learning 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract In the present era, Online Social Networking has become an important phenomenon in human society. However, a large section of users are not aware of the security and privacy concerns involve in it. People have a tendency to publish sensitive and private information for example date of birth, mobile numbers, places checked-in, live locations, emotions, name of their spouse and other family members, etc. that may potentially prove disastrous. By monitoring their social network updates, the cyber attackers, first, collect the user’s public information which is further used to acquire their confidential information like banking details, etc. and to launch security attacks e.g. fake identity attack. Such attacks or information leaks may gravely affect their life. In this technology-laden era, it is imperative for users must be well aware of the potential risks involved in online social networks. This paper comprehensively surveys the evolution of the online social networks, their associated risks and solutions. The various security models and the state of the art algorithms have been discussed along with a comparative meta-analysis using machine learning, deep learning, and statistical testing to recommend a better solution. Online social networks Attacks Security Malware analysis Feature extraction Big data Machine learning Deep learning Bharati, Taran Singh aut Prakash, Shiv aut Enthalten in Neural processing letters Springer US, 1994 53(2021), 1 vom: 05. Jan., Seite 843-861 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:53 year:2021 number:1 day:05 month:01 pages:843-861 https://doi.org/10.1007/s11063-020-10416-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT AR 53 2021 1 05 01 843-861 |
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10.1007/s11063-020-10416-3 doi (DE-627)OLC2124185314 (DE-He213)s11063-020-10416-3-p DE-627 ger DE-627 rakwb eng 000 VZ Kumar, Chanchal verfasserin aut Online Social Network Security: A Comparative Review Using Machine Learning and Deep Learning 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 Abstract In the present era, Online Social Networking has become an important phenomenon in human society. However, a large section of users are not aware of the security and privacy concerns involve in it. People have a tendency to publish sensitive and private information for example date of birth, mobile numbers, places checked-in, live locations, emotions, name of their spouse and other family members, etc. that may potentially prove disastrous. By monitoring their social network updates, the cyber attackers, first, collect the user’s public information which is further used to acquire their confidential information like banking details, etc. and to launch security attacks e.g. fake identity attack. Such attacks or information leaks may gravely affect their life. In this technology-laden era, it is imperative for users must be well aware of the potential risks involved in online social networks. This paper comprehensively surveys the evolution of the online social networks, their associated risks and solutions. The various security models and the state of the art algorithms have been discussed along with a comparative meta-analysis using machine learning, deep learning, and statistical testing to recommend a better solution. Online social networks Attacks Security Malware analysis Feature extraction Big data Machine learning Deep learning Bharati, Taran Singh aut Prakash, Shiv aut Enthalten in Neural processing letters Springer US, 1994 53(2021), 1 vom: 05. Jan., Seite 843-861 (DE-627)198692617 (DE-600)1316823-X (DE-576)052842762 1370-4621 nnns volume:53 year:2021 number:1 day:05 month:01 pages:843-861 https://doi.org/10.1007/s11063-020-10416-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-MAT AR 53 2021 1 05 01 843-861 |
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Abstract In the present era, Online Social Networking has become an important phenomenon in human society. However, a large section of users are not aware of the security and privacy concerns involve in it. People have a tendency to publish sensitive and private information for example date of birth, mobile numbers, places checked-in, live locations, emotions, name of their spouse and other family members, etc. that may potentially prove disastrous. By monitoring their social network updates, the cyber attackers, first, collect the user’s public information which is further used to acquire their confidential information like banking details, etc. and to launch security attacks e.g. fake identity attack. Such attacks or information leaks may gravely affect their life. In this technology-laden era, it is imperative for users must be well aware of the potential risks involved in online social networks. This paper comprehensively surveys the evolution of the online social networks, their associated risks and solutions. The various security models and the state of the art algorithms have been discussed along with a comparative meta-analysis using machine learning, deep learning, and statistical testing to recommend a better solution. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
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Abstract In the present era, Online Social Networking has become an important phenomenon in human society. However, a large section of users are not aware of the security and privacy concerns involve in it. People have a tendency to publish sensitive and private information for example date of birth, mobile numbers, places checked-in, live locations, emotions, name of their spouse and other family members, etc. that may potentially prove disastrous. By monitoring their social network updates, the cyber attackers, first, collect the user’s public information which is further used to acquire their confidential information like banking details, etc. and to launch security attacks e.g. fake identity attack. Such attacks or information leaks may gravely affect their life. In this technology-laden era, it is imperative for users must be well aware of the potential risks involved in online social networks. This paper comprehensively surveys the evolution of the online social networks, their associated risks and solutions. The various security models and the state of the art algorithms have been discussed along with a comparative meta-analysis using machine learning, deep learning, and statistical testing to recommend a better solution. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract In the present era, Online Social Networking has become an important phenomenon in human society. However, a large section of users are not aware of the security and privacy concerns involve in it. People have a tendency to publish sensitive and private information for example date of birth, mobile numbers, places checked-in, live locations, emotions, name of their spouse and other family members, etc. that may potentially prove disastrous. By monitoring their social network updates, the cyber attackers, first, collect the user’s public information which is further used to acquire their confidential information like banking details, etc. and to launch security attacks e.g. fake identity attack. Such attacks or information leaks may gravely affect their life. In this technology-laden era, it is imperative for users must be well aware of the potential risks involved in online social networks. This paper comprehensively surveys the evolution of the online social networks, their associated risks and solutions. The various security models and the state of the art algorithms have been discussed along with a comparative meta-analysis using machine learning, deep learning, and statistical testing to recommend a better solution. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
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