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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.02645 |
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| _version_ | 1866915005706797056 |
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| author | Huber, Birkett Neo, Casper Sampson, Keiran Kantchelian, Alex Ksobiech, Brett Pavlidis, Yanis |
| author_facet | Huber, Birkett Neo, Casper Sampson, Keiran Kantchelian, Alex Ksobiech, Brett Pavlidis, Yanis |
| contents | We present a method to detect departures from business-justified workflows among support agents. Our goal is to assist auditors in identifying agent actions that cannot be explained by the activity within their surrounding context, where normal activity patterns are established from historical data. We apply our method to help audit millions of actions of over three thousand support agents.
We collect logs from the tools used by support agents and construct a bipartite graph of Actions and Entities representing all the actions of the agents, as well as background information about entities. From this graph, we sample subgraphs rooted on security-significant actions taken by the agents. Each subgraph captures the relevant context of the root action in terms of other actions, entities and their relationships. We then prioritize the rooted-subgraphs for auditor review using feed-forward and graph neural networks, as well as nearest neighbors techniques. To alleviate the issue of scarce labeling data, we use contrastive learning and domain-specific data augmentations.
Expert auditors label the top ranked subgraphs as ``worth auditing" or ``not worth auditing" based on the company's business policies. This system finds subgraphs that are worth auditing with high enough precision to be used in production. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_02645 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fine Grained Insider Risk Detection Huber, Birkett Neo, Casper Sampson, Keiran Kantchelian, Alex Ksobiech, Brett Pavlidis, Yanis Cryptography and Security Machine Learning We present a method to detect departures from business-justified workflows among support agents. Our goal is to assist auditors in identifying agent actions that cannot be explained by the activity within their surrounding context, where normal activity patterns are established from historical data. We apply our method to help audit millions of actions of over three thousand support agents. We collect logs from the tools used by support agents and construct a bipartite graph of Actions and Entities representing all the actions of the agents, as well as background information about entities. From this graph, we sample subgraphs rooted on security-significant actions taken by the agents. Each subgraph captures the relevant context of the root action in terms of other actions, entities and their relationships. We then prioritize the rooted-subgraphs for auditor review using feed-forward and graph neural networks, as well as nearest neighbors techniques. To alleviate the issue of scarce labeling data, we use contrastive learning and domain-specific data augmentations. Expert auditors label the top ranked subgraphs as ``worth auditing" or ``not worth auditing" based on the company's business policies. This system finds subgraphs that are worth auditing with high enough precision to be used in production. |
| title | Fine Grained Insider Risk Detection |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2411.02645 |