The One-Stop Anomaly Shop (OSAS) project packages machine-learning algorithms into a Docker container for finding anomalies in security log data.
Adobe has released an open source project to detect and classify anomalies in security log data using a tool the company says is simple to run and easily modified.
The One-Stop Anomaly Shop (OSAS) is an open source machine-learning (ML) tool that can add structure to log data by generating labels for different types of data and then use that data as the inputs to classification algorithms. The approach solves several ML problems — such as data sparsity and overfitting — while giving security analysts a macro view of log data that allows easier analysis, states Adobe’s security intelligence team in a technical paper on their approach.
The software project allows security teams to quickly identify what features — or feature combinations — provide the most benefit in terms of analysis in a particular dataset, the Adobe Security Intelligence Team told Dark Reading in an email interview.
“One could consider finding anomalies as somewhat trivial from the computational perspective,” they said. “However, being able to say why something is an anomaly is an entirely different story. OSAS is useful in identifying why [an event is considered] an anomaly.”
The One-Stop Anomaly Shop project provides security analysts and researchers with a way to quickly analyze security logs using labeled data, even when the security log file has a variety of event types, the Adobe group stated in a blog post. The project, available on GitHub, creates a Docker container running the Elasticsearch search engine, Logstash indexer, and Kibana Web front-end — a combination known as the ELK stack — while the ML application is written in Python.
The system labels events with a variety of tags, indicating, for example, whether the anomaly is unique, whether a particular port, process, or path is rare, and whether the event connects to a public IP or the localhost.
“There is a lot of research and whitepapers on data-science in security, but few tools that implement state-of-the-art ideas that are made available to the community,” Adobe’s team told Dark Reading. “Primarily, open-sourcing OSAS was an opportunity for us to put our work in an end-to-end framework. Secondly, we want to make OSAS as robust and security oriented as possible and we cannot achieve that without support from the security community.”
Adobe is not the only software company to provide security teams with ML tools. Earlier this month, Microsoft published details of a project that uses the company’s massive data set of attack traffic, along with the MITRE ATT&CK framework, to build an ML model that not only assigns particular attack tactics with certain groups, but predicts the attacker’s potential next steps.
Adobe’s tool is best-suited for working with security log data, but it can work on any source of flat log data that follow the same patterns, such as authentication logs, Web server logs, and access logs, Adobe stated.
After tagging the various elements of the log file, a second pipeline also assigns risk-based scores to collections of tags.
“The primary goal is to assign high scores to suspicious activity and low scores to normal operations,” the security intelligence team states in its technical whitepaper.
The ML tool has multiple strategies for detecting anomalies in log files and assigning them risk-based scores. An unsupervised-learning approach may find malicious activities that would go undiscovered with supervised-learning models but will also likely generate more false alerts.
“In theory, potentially malicious events are a subset of the anomalies set,” the security intelligence team said via email. “Targeting the detection of these potentially malicious events by OSAS can be achieved by creating a tailored data-grooming pipeline via the configuration file.”
In tests, the security intelligence team used benign data of normal operations to train the ML algorithm and then input an artificially constructed dataset to benchmark the ability to detect anomalies. The supervised approach had a nearly 95% detection rate, while two unsupervised models performed less well, with a 63% and 50% score.
The Adobe Security Intelligence Team aims to garner feedback with the project and perhaps build a collection of pretrained analysis pipelines — a “model-zoo” — that can be distributed with future versions. The team comprises security engineers Vivek Malik and Kumar Vikramjeet, data scientist Tiberiu Boros, and technical lead Andrei Cotaie.
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