Optimising Insider Threat Prediction: Exploring BiLSTM Networks and Sequential Features
Abstract Insider threats pose a critical risk to organisations, impacting their data, processes, resources, and overall security. Such significant risks arise from individuals with authorised access and familiarity with internal systems, emphasising the potential for insider threats to compromise th...
Main Authors: | , , , , , |
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Format: | Article |
Language: | English |
Published: |
SpringerOpen
2024-11-01
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Series: | Data Science and Engineering |
Subjects: | |
Online Access: | https://doi.org/10.1007/s41019-024-00260-z |