LAN: Learning Adaptive Neighbors for Real-Time Insider Threat Detection

核心概念

LAN introduces a novel framework for real-time insider threat detection, outperforming state-of-the-art methods by at least 9.92% in AUC on CERT r4.2 and r5.2.

要約

Enterprises face threats from insider employees, leading to serious consequences. Previous studies focus on detecting abnormal users or time periods, but LAN offers real-time ITD at the activity level. LAN learns temporal dependencies and relationships between activities with graph structure learning. It proposes a hybrid prediction loss to address data imbalance in ITD. Extensive experiments show LAN's superiority over baselines in AUC for both real-time and post-hoc ITD.

統計

LAN outperforms baselines by at least 9.92% and 6.35% in AUC for real-time ITD on CERT r4.2 and r5.2, respectively.
LAN reduces false positives by 14.11% and 8.67% on CERT r4.2 and r5.2, respectively.

引用

"No more false positives with LAN's innovative approach."
"LAN sets a new standard for real-time insider threat detection."

抽出されたキーインサイト

LAN

by Xiangrui Cai... 場所 arxiv.org 03-15-2024

https://arxiv.org/pdf/2403.09209.pdf

深掘り質問

How can LAN's approach be adapted to different cybersecurity scenarios

LAN's approach can be adapted to different cybersecurity scenarios by leveraging its fine-grained and efficient framework for real-time threat detection. For instance, in network security, LAN's activity-level detection methodology can be applied to detect anomalous behavior in network traffic data. By modeling temporal dependencies within sequences and relationships between activities across sequences with graph structure learning, LAN can effectively identify potential threats in real-time. Additionally, LAN's hybrid prediction loss mechanism can help address the issue of data imbalance commonly encountered in cybersecurity datasets.

What are potential drawbacks or limitations of LAN's methodology

One potential drawback of LAN's methodology could be the computational complexity associated with graph structure learning. Constructing and optimizing dynamic graphs for each detected activity may require significant computational resources, especially as the dataset size increases. Another limitation could be the reliance on historical data for anomaly detection, which may not always capture emerging or previously unseen threats effectively. Furthermore, the effectiveness of LAN may depend on the quality and representativeness of the training data available.

How can graph structure learning be applied beyond insider threat detection

Graph structure learning techniques employed by LAN can have applications beyond insider threat detection in various domains such as fraud detection, anomaly detection in IoT networks, social network analysis, recommendation systems, and healthcare analytics. In fraud detection scenarios, graph-based methods can help uncover fraudulent patterns among interconnected entities or transactions. In IoT networks, graph structures can reveal unusual device interactions indicative of cyber attacks or malfunctions. Social network analysis benefits from identifying suspicious behavior or fake accounts through graph representations of user connections. Recommendation systems utilize graph structures to enhance personalized recommendations based on user preferences and item similarities. Healthcare analytics could leverage graphs to detect unusual patient treatment patterns or medical billing discrepancies for fraud prevention purposes.