There have been several cases of GPT model-based detection for various attacks from system logs.
However, there has been no dedicated framework for detecting APTs as they use a low and slow approach to compromise the systems.
Security researchers have recently unveiled a cutting-edge framework known as LogShield. This innovative tool leverages the self-attention capabilities of transformers to identify attack patterns associated with Advanced Persistent Threats (APTs).
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By analyzing network logs, LogShield can detect subtle indicators of APTs that may have otherwise gone unnoticed, providing a powerful defense against these sophisticated attacks.
According to the researchers, the efficiency of this framework has been reported to be 95% and 98%.
LogShield
The main purpose of using language models for detecting malicious events is because they have been designed to process large sequences of words or log data, which is useful when processing records of events on a cyber attack.
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Additionally, the self-attention mechanism of GPT models can assign different weights to different events based on their relativity to the APTs and can be adjusted concerning the event’s importance.
Machine learning techniques have been used to detect attack patterns instead of rule-based or signature-based attack detection methods, which have relatively low performance when detecting Zero-Day APTs.
Moreover, several deep learning-based methods have been explored to detect APT attacks.
Limitations of LogShield
Though LogShield has superior performance, there is a limitation to this framework. As it has high performance, it also comes with an increased memory consumption and longer computational time. As part of the research, LogShield and LSTM models have been used.
However, after many experiments, efficiency was achieved with a 98% F1-score in APT detection.
A report about LogShield has been published, providing detailed information about the training models using their statistical data and other information.
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