Unsupervised Threat Hunting Framework: CBoTT

Konsep Inti

An unsupervised framework, Continuous Bag-of-Terms-and-Time (CBoTT), outperforms benchmark approaches in identifying anomalies in SIEM logs for threat hunting.

Abstrak

The article introduces the CBoTT framework for unsupervised threat hunting, focusing on anomaly detection in SIEM logs. It addresses the limitations of existing security tools and the increasing sophistication of cyber threats. The framework uses machine learning and natural language processing to process large volumes of text-based process audits efficiently. By grouping commands into tasks and training models based on continuous bag-of-words approach, it can identify potential security risks at different percentiles. The study evaluates the framework's performance against benchmark models through injection schemes, demonstrating its superior ability to detect anomalies.

Statistik

Our approach consistently outperforms benchmark approaches.
Analyses show that our framework identifies anomalies at higher percentiles compared to benchmark approaches.
The average cost of a data breach in the US is $9.44 million.
AI/ML tools for threat hunting have increased from 15% in 2020 to approximately 45% in 2022.
Anomalies are usually defined by frequency of occurrence.

Kutipan

"Threat hunting aims to identify threats that remain undetected in an organization despite various cybersecurity controls."
"Our framework consistently outperforms benchmark approaches."
"The CBoTT framework proposed in this study can effectively address these challenges by analyzing large volumes of text-based commands and identifying unique and suspicious activities."

Wawasan Utama Disaring Dari

Unsupervised Threat Hunting using Continuous Bag-of-Terms-and-Time (CBoTT)

by Varol Kayhan... pada arxiv.org 03-18-2024

https://arxiv.org/pdf/2403.10327.pdf

Pertanyaan yang Lebih Dalam

How can organizations ensure that analysts do not become desensitized to alerts generated by threat hunting frameworks?

To prevent analysts from becoming desensitized to the alerts generated by threat hunting frameworks, organizations can implement several strategies.

Tuning Alert Thresholds: Organizations should fine-tune alert thresholds to reduce false positives and ensure that only critical alerts are presented to analysts. By setting thresholds based on the organization's risk tolerance and security priorities, analysts will receive fewer but more relevant alerts.

Contextual Information: Providing contextual information along with alerts can help analysts understand the significance of each alert better. This includes details about the affected systems, users involved, potential impact, and any previous incidents related to the alert.

Automated Triage: Implementing automated triage mechanisms using machine learning algorithms can help prioritize alerts based on their severity and likelihood of being a real threat. This reduces manual effort for analysts and ensures they focus on high-priority tasks.

Continuous Training: Regular training sessions for analysts on new threats, attack techniques, and how to interpret different types of alerts can keep them engaged and informed. Continuous education helps in maintaining analyst vigilance towards potential threats.

Feedback Mechanism: Establishing a feedback loop where analysts provide input on the effectiveness of alerts generated by the system allows for continuous improvement in refining detection rules and reducing false positives over time.

What are the implications of false positives generated by unsupervised threat hunting frameworks like CBoTT?

False positives generated by unsupervised threat hunting frameworks like CBoTT can have significant implications for organizations:

Alert Fatigue: A high number of false positives may lead to alert fatigue among cybersecurity analysts as they spend valuable time investigating non-critical issues instead of focusing on genuine threats.

Resource Wastage: Analysts spending time investigating false alarms result in wasted resources that could have been utilized more effectively elsewhere within an organization's security operations center.

Missed Threats: The presence of numerous false positives might cause important security events or actual malicious activities to be overlooked or deprioritized due to overwhelming noise in the system.

4Reputation Damage: If an organization consistently struggles with handling false positive incidents efficiently, it may damage its reputation as clients lose confidence in its ability to protect sensitive data effectively.

How can advancements in natural language processing further enhance the capabilities of threat hunting frameworks?

Advancements in natural language processing (NLP) offer several ways to enhance threat hunting frameworks' capabilities:
1Improved Anomaly Detection: NLP techniques enable better analysis of unstructured text data such as logs containing process audits through semantic understanding, allowing for more accurate anomaly detection without relying solely on predefined rules or patterns.
2Behavioral Analysis: NLP models trained on historical data can identify patterns indicative of malicious behavior across endpoints or networks based on textual commands executed, enabling proactive identification before traditional signature-based methods detect them.
3Contextual Understanding: NLP models equipped with context-awareness capabilities can analyze commands within their operational context (timeframe, user role) providing deeper insights into whether certain actions are anomalous or part of regular operations
4**Reduced False Positives: Advanced NLP algorithms combined with machine learning approaches help refine anomaly detection criteria over time based on feedback loops from analyst investigations leadingto reduced instancesoffalsepositivesandmoreaccuratealertgeneration