Forensic Anomaly Detection System Development

This case study focuses on a Forensic Anomaly Detection System developed to safeguard critical systems from cybersecurity threats. The system utilizes forensic data from Velociraptor and machine learning models to identify and flag anomalous patterns in logs and network activity, enhancing incident response and overall security.

Enhance Incident Response: Enable quick identification of potential security breaches by automatically flagging unusual patterns in logs and network activity.

Leverage Forensic Data: Utilize Velociraptor's capabilities to perform comprehensive data collection from endpoints.

Automate Anomaly Detection: Implement unsupervised learning models to efficiently detect anomalous patterns.

With the increasing complexity of cybersecurity threats, organizations face the challenge of rapidly detecting and responding to anomalies to protect their critical systems. The problem was to create a system that could analyze logs and fingerprints created on different machines (Windows, Mac, and Linux) to segment logs by user and identify activities that violate an organization's policies or terms of use.

The solution involved developing a system that extracts forensic data from Velociraptor to analyze logs, network activity, and file changes. The system applies feature engineering and uses unsupervised machine learning models, such as Isolation Forest and Autoencoders, to detect anomalies. The models were deployed on AWS Lambda for real-time anomaly detection. The predictions were then flagged and presented to cybersecurity analysts on comprehensive dashboards for proactive investigation and response.

The approach involved a step-by-step workflow for data collection, processing, and analysis:

Velociraptor Installation: Velociraptor was installed across various endpoints (Mac, Windows, and Linux) to collect forensic artifacts.

Data Collection & Storage: A Python program, triggered by a scheduler, periodically fetched forensic artifacts from the Velociraptor server. This data was then stored in a MongoDB database for further processing.

Model Application: Trained machine learning models were downloaded from Amazon S3, and a Lambda function was used to apply feature engineering and run the anomaly detection models on the data.

Reporting: The anomalies detected by the models were flagged, saved as predictions in an S3 bucket, and presented to analysts via dashboards.

Tools: Velociraptor (Forensic Data Extraction)

Programming Language: Python

Libraries: Pandas, Scikit-learn, TensorFlow

Algorithms: Isolation Forest, Autoencoders

Cloud Services: AWS (S3 for storage, Lambda for processing)

Database: MongoDB

The Forensic Anomaly Detection System delivered significant and quantifiable improvements in cybersecurity posture:

95% Reduction in Incident Detection Time: The system reduced the time required to detect potential security breaches by 95% compared to manual analysis.

70% Reduction in False Positives: The machine learning models achieved a 70% reduction in false positives, allowing analysts to focus on genuine threats.

Automated 24/7 Monitoring: The system provides continuous monitoring of endpoints, ensuring round-the-clock protection against threats.

Detection of New Threats: The unsupervised learning models successfully identified previously unknown anomalous patterns, preventing potential security breaches.

500% Increase in Data Analysis Efficiency: The automated system processed and analyzed forensic data 5 times faster than human analysts, dramatically increasing efficiency.

The Forensic Anomaly Detection System successfully addressed the challenge of complex cybersecurity threats by providing an automated, intelligent, and efficient solution. By leveraging forensic data and machine learning, the system allows organizations to proactively monitor employee activity, detect network anomalies, and strengthen their security posture.