Social Media Zero-Day Attack Detection Using TensorFlow
In the current information era, knowledge can pose risks in the online realm. It is imperative to proactively recognize potential threats, as unforeseen dangers cannot be eliminated entirely. Often, malware exploits and other emerging hazards are only identified after they have occurred. These types...
Ausführliche Beschreibung
Autor*in: |
Ahmet Ercan Topcu [verfasserIn] Yehia Ibrahim Alzoubi [verfasserIn] Ersin Elbasi [verfasserIn] Emre Camalan [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2023 |
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Übergeordnetes Werk: |
In: Electronics - MDPI AG, 2013, 12(2023), 17, p 3554 |
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Übergeordnetes Werk: |
volume:12 ; year:2023 ; number:17, p 3554 |
Links: |
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DOI / URN: |
10.3390/electronics12173554 |
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Katalog-ID: |
DOAJ093523173 |
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10.3390/electronics12173554 doi (DE-627)DOAJ093523173 (DE-599)DOAJ2eb8518ee0104c29815ef4602568d5e6 DE-627 ger DE-627 rakwb eng TK7800-8360 Ahmet Ercan Topcu verfasserin aut Social Media Zero-Day Attack Detection Using TensorFlow 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the current information era, knowledge can pose risks in the online realm. It is imperative to proactively recognize potential threats, as unforeseen dangers cannot be eliminated entirely. Often, malware exploits and other emerging hazards are only identified after they have occurred. These types of risks are referred to as zero-day attacks since no pre-existing anti-malware measures are available to mitigate them. Consequently, significant damages occur when vulnerabilities in systems are exploited. The effectiveness of security systems, such as IPS and IDS, relies heavily on the prompt and efficient response to emerging threats. Failure to address these issues promptly hinders the effectiveness of security system developers. The purpose of this study is to analyze data from the Twitter platform and deploy machine learning techniques, such as word categorization, to identify vulnerabilities and counteract zero-day attacks swiftly. TensorFlow was utilized to handle the processing and conversion of raw Twitter data, resulting in significant efficiency improvements. Moreover, we integrated the Natural Language Toolkit (NLTK) tool to extract targeted words in various languages. Our results indicate that we have achieved an 80% success rate in detecting zero-day attacks by using our tool. By utilizing publicly available information shared by individuals, relevant security providers can be promptly informed. This approach enables companies to patch vulnerabilities more quickly. zero-day attack Twitter TensorFlow machine learning word classification Electronics Yehia Ibrahim Alzoubi verfasserin aut Ersin Elbasi verfasserin aut Emre Camalan verfasserin aut In Electronics MDPI AG, 2013 12(2023), 17, p 3554 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:12 year:2023 number:17, p 3554 https://doi.org/10.3390/electronics12173554 kostenfrei https://doaj.org/article/2eb8518ee0104c29815ef4602568d5e6 kostenfrei https://www.mdpi.com/2079-9292/12/17/3554 kostenfrei https://doaj.org/toc/2079-9292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2023 17, p 3554 |
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In the current information era, knowledge can pose risks in the online realm. It is imperative to proactively recognize potential threats, as unforeseen dangers cannot be eliminated entirely. Often, malware exploits and other emerging hazards are only identified after they have occurred. These types of risks are referred to as zero-day attacks since no pre-existing anti-malware measures are available to mitigate them. Consequently, significant damages occur when vulnerabilities in systems are exploited. The effectiveness of security systems, such as IPS and IDS, relies heavily on the prompt and efficient response to emerging threats. Failure to address these issues promptly hinders the effectiveness of security system developers. The purpose of this study is to analyze data from the Twitter platform and deploy machine learning techniques, such as word categorization, to identify vulnerabilities and counteract zero-day attacks swiftly. TensorFlow was utilized to handle the processing and conversion of raw Twitter data, resulting in significant efficiency improvements. Moreover, we integrated the Natural Language Toolkit (NLTK) tool to extract targeted words in various languages. Our results indicate that we have achieved an 80% success rate in detecting zero-day attacks by using our tool. By utilizing publicly available information shared by individuals, relevant security providers can be promptly informed. This approach enables companies to patch vulnerabilities more quickly. |
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In the current information era, knowledge can pose risks in the online realm. It is imperative to proactively recognize potential threats, as unforeseen dangers cannot be eliminated entirely. Often, malware exploits and other emerging hazards are only identified after they have occurred. These types of risks are referred to as zero-day attacks since no pre-existing anti-malware measures are available to mitigate them. Consequently, significant damages occur when vulnerabilities in systems are exploited. The effectiveness of security systems, such as IPS and IDS, relies heavily on the prompt and efficient response to emerging threats. Failure to address these issues promptly hinders the effectiveness of security system developers. The purpose of this study is to analyze data from the Twitter platform and deploy machine learning techniques, such as word categorization, to identify vulnerabilities and counteract zero-day attacks swiftly. TensorFlow was utilized to handle the processing and conversion of raw Twitter data, resulting in significant efficiency improvements. Moreover, we integrated the Natural Language Toolkit (NLTK) tool to extract targeted words in various languages. Our results indicate that we have achieved an 80% success rate in detecting zero-day attacks by using our tool. By utilizing publicly available information shared by individuals, relevant security providers can be promptly informed. This approach enables companies to patch vulnerabilities more quickly. |
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In the current information era, knowledge can pose risks in the online realm. It is imperative to proactively recognize potential threats, as unforeseen dangers cannot be eliminated entirely. Often, malware exploits and other emerging hazards are only identified after they have occurred. These types of risks are referred to as zero-day attacks since no pre-existing anti-malware measures are available to mitigate them. Consequently, significant damages occur when vulnerabilities in systems are exploited. The effectiveness of security systems, such as IPS and IDS, relies heavily on the prompt and efficient response to emerging threats. Failure to address these issues promptly hinders the effectiveness of security system developers. The purpose of this study is to analyze data from the Twitter platform and deploy machine learning techniques, such as word categorization, to identify vulnerabilities and counteract zero-day attacks swiftly. TensorFlow was utilized to handle the processing and conversion of raw Twitter data, resulting in significant efficiency improvements. Moreover, we integrated the Natural Language Toolkit (NLTK) tool to extract targeted words in various languages. Our results indicate that we have achieved an 80% success rate in detecting zero-day attacks by using our tool. By utilizing publicly available information shared by individuals, relevant security providers can be promptly informed. This approach enables companies to patch vulnerabilities more quickly. |
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|
score |
7.397187 |