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• W4312477653 abstract "The insider threat involves theft of confidential information, commercial information, business plans, intellectual property or any fraud. The attacker may be an employee, third party vendor, contractor, business associate, partner or former employee who has access to data or information which may violate organisation’s security practices. According to the surveys done in some previous years, it has been identified that the threats are harming organizations and among those attacks 60-70% attacks are performed by insider employees. The insider attacker is much more dangerous as compared to the external attacker as security devices like intrusion detection, firewalls and antivirus systems cannot detect them because they are using trusted access that increases the difficulty in detecting insider threat detection. In this paper, a study has been done on various types of insiders as some of them are malicious but most of the threats occur due to carelessness and unintentionally. Then insiders attack types that used different approaches and several characteristics like user action logs, biometric patterns, behavioural changes and distinct algorithms." @default.
• W4312477653 created "2023-01-05" @default.
• W4312477653 creator A5051965447 @default.
• W4312477653 creator A5070231937 @default.
• W4312477653 date "2022-01-01" @default.
• W4312477653 modified "2023-10-16" @default.
• W4312477653 title "A review for insider threats detection using machine learning" @default.
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• W4312477653 doi "https://doi.org/10.1063/5.0108887" @default.
• W4312477653 hasPublicationYear "2022" @default.
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