Fine-Grained Insider Risk Detection Among Support Agents Using Graph-Based Machine Learning

Core Concepts

This research paper introduces a novel graph-based machine learning approach to detect insider threats from support agents by identifying departures from expected workflows within their activity logs.

Abstract

• Bibliographic Information: Huber, B., Neo, C., Sampson, K., Kantchelian, A., Ksobiech, B., & Pavlidis, Y. (2024). Fine Grained Insider Risk Detection. arXiv:2411.02645v1 [cs.CR].

• Research Objective: This paper aims to develop a system for detecting insider risks posed by support agents, focusing on identifying deviations from expected workflows within their activity logs.

• Methodology: The researchers propose a fine-grained approach that analyzes support agents' activity logs by constructing a bipartite graph of Actions and Entities. They extract subgraphs rooted on security-significant actions and prioritize them for auditor review using nearest neighbor techniques, synthetic mutation ranking, and graph neural networks. Contrastive learning and domain-specific data augmentations address the challenge of scarce labeled data.

• Key Findings: The evaluation demonstrates that all proposed techniques, including Nearest Neighbor (NN) and Synthetic Mutation Rank (SMR) with both handcrafted and Graph Neural Network (GNN) embeddings, significantly outperform random audits in identifying potentially malicious activities. NN ranking shows slightly better performance than SMR, while GNN embeddings prove advantageous in capturing workflow-agent affinity.

• Main Conclusions: The research presents a viable solution for modeling workflows and prioritizing them for security audits, requiring minimal labeled data and varying levels of domain expertise. The system shows promise for continuous use and potential generalization beyond support agent workflows.

• Significance: This work contributes significantly to the field of insider threat detection by introducing a novel graph-based approach that moves beyond traditional coarse-grained methods. It offers a practical solution for organizations to proactively mitigate insider risks posed by support agents.

• Limitations and Future Research: Future research directions include generalizing the approach beyond support agent workflows, automating aspects requiring domain engineering, and exploring the use of Large Language Models (LLMs) for log parsing and parameter selection.

Stats

Insider risk incidents cost companies an average of $16.2 million per incident.
The study analyzed one week of data, encompassing over 95,000 rooted subgraphs and actions of around 3,100 active support agents.
The NN and GNN embedding methods both resulted in 38 subgraphs flagged for auditing.

Quotes

"In contrast, we present a fine-grained approach that detects insider risk by finding departures from expected workflows."
"Specifying workflows in detail as hard-coded rules is difficult and toilsome because a company’s services and internal tooling change over time, inevitably changing how support agents should do their jobs, as well as the logs describing how they did do their jobs."
"To avoid enumerating workflows, we use machine learning and learn them from the data."

Key Insights Distilled From

Fine Grained Insider Risk Detection

by Birkett Hube... at arxiv.org 11-06-2024

https://arxiv.org/pdf/2411.02645.pdf

Deeper Inquiries

How can this graph-based approach be adapted to detect insider threats in other departments beyond support, such as finance or software development, considering the unique workflows and data access patterns in those areas?

This graph-based approach offers a versatile framework adaptable to various departments with some modifications:
1. Identifying Department-Specific Actions and Entities:

Finance: Actions could include "Transaction.Approval", "Account.Access", "Report.Generation" while entities might be "Employee.ID", "Account.Number", "Transaction.Amount".
Software Development: Actions like "Code.Commit", "Repository.Access", "Issue.Resolution" would be relevant, with entities like "Developer.ID", "Code.Repository", "Issue.Tracker".
2. Defining Sensitive Actions:

Finance: Accessing large sums of money, approving suspicious transactions, or altering financial records without proper authorization.
Software Development: Accessing and exfiltrating proprietary source code, injecting malicious code into repositories, or granting unauthorized access to critical systems.
3. Tailoring Graph Construction:

Relationship Types: New relationship types might be needed. For example, in finance, a "Beneficiary" relationship between a transaction and an account.
Traversal Parameters: The depth (T) and breadth (M) of subgraph traversal might need adjustments based on the complexity and temporal characteristics of workflows in each department.
4. Domain-Specific Data Augmentation and Features:

Finance: Mutations could involve manipulating transaction amounts, changing beneficiaries, or altering timestamps to simulate fraudulent activities. Handcrafted features could include transaction frequency, average transaction value, or deviations from typical account behavior.
Software Development: Mutations might involve code injection patterns, unusual commit frequencies, or access to sensitive code sections. Features could include code complexity metrics, commit history analysis, or developer collaboration patterns.
5. Collaboration with Domain Experts:

Close collaboration with finance analysts or software development leads is crucial to understand normal behavior patterns, identify relevant anomalies, and refine the model's accuracy.
By incorporating these department-specific adaptations, the graph-based approach can effectively detect insider threats across diverse organizational functions.

While the paper focuses on detecting deviations from expected workflows, could this approach lead to false positives if legitimate reasons exist for an agent's unusual activity, such as handling a novel or complex support ticket?

Yes, the potential for false positives exists, especially in situations involving:

Novel Scenarios: The model, trained on historical data, might flag unusual but legitimate actions taken to handle new types of support requests or address unforeseen issues.
Complex Cases: Complex tickets often require agents to deviate from standard procedures, potentially triggering false positives if the model doesn't adequately capture the nuances of such situations.
Incomplete Data: If the system doesn't capture all relevant information, such as internal communication logs or external knowledge base consultations, it might misinterpret an agent's actions.
Mitigation Strategies:

Contextual Information: Integrating additional data sources, like ticket content, customer interactions, or internal communication logs, can provide valuable context and reduce false positives.
Anomaly Explanation: Developing mechanisms to explain the model's reasoning behind flagging an action as anomalous can help analysts quickly differentiate between true positives and false alarms.
Feedback Loop: Implementing a feedback loop where analysts can mark false positives allows the model to learn from its mistakes and improve its accuracy over time.
Threshold Adjustment: Fine-tuning the model's sensitivity by adjusting the threshold for flagging anomalies can help balance detection accuracy with an acceptable false positive rate.
Addressing these challenges is crucial for ensuring the practical effectiveness and user acceptance of this insider threat detection system.

If machine learning models can learn and adapt to evolving workflows, could this technology be applied to other security domains, such as fraud detection or anomaly detection in network traffic, to enhance proactive threat identification?

Absolutely, the adaptability of machine learning models to evolving patterns makes them highly applicable to other security domains:
1. Fraud Detection:

Financial Transactions: Similar to detecting unusual support agent actions, the system can learn normal spending patterns, transaction types, and locations associated with user accounts. Deviations from these patterns, such as large purchases, unusual locations, or rapid transaction sequences, can be flagged as potential fraud.
Insurance Claims: Analyzing claim details, medical records, and historical data can help identify suspicious patterns indicative of fraudulent claims, such as inflated damages, fabricated injuries, or collusive activities.
2. Anomaly Detection in Network Traffic:

Intrusion Detection: By learning typical network behavior, including traffic volume, communication protocols, and access patterns, the model can detect anomalies like port scans, brute-force attacks, or data exfiltration attempts.
DDoS Mitigation: Identifying unusual spikes in traffic volume, originating IP addresses, or packet characteristics can help distinguish legitimate traffic from DDoS attacks and trigger appropriate mitigation responses.
Key Advantages for Proactive Threat Identification:

Adaptability: Machine learning models can continuously learn and adapt to evolving attack vectors, new fraud techniques, and changing network behavior, ensuring long-term effectiveness.
Early Detection: By identifying subtle anomalies that might escape rule-based systems, these models enable proactive threat detection, preventing potential damage before it occurs.
Scalability: Machine learning algorithms can handle massive datasets generated in network security and fraud prevention, enabling comprehensive threat monitoring across large organizations.
By leveraging the power of machine learning, security systems can move beyond reactive measures and embrace a proactive approach to threat identification, bolstering overall security posture.