Elasticsearch use cases in cybersecurity

26 Apr 2023 by dzlab

Elastic Stack at its core relies on Elasticsearch, Kibana and a variety of data ingestion tools. Elasticsearch with its capabilities for indexing and retrieving of textual data, and Kibana for analytics and visualization of data stored in Elasticsearch indices. Furthermore, Kibana is very intuitive, making it very easy to perform advanced data analysis and visualize of data in a variety of charts, tables, and maps.

In the context of cybersecurity, and thanks to Elasticsearch performance and extensibility, analysts can apply it to protect their organizations. Some example of those applications are:

• Log analysis: Elasticsearch can be used to store and search through large amounts of log data from different sources, such as network devices, servers, and applications. This can help identify anomalies, detect attacks, and analyze patterns that could indicate potential threats.
• Security incident response: When responding to security incidents, such as breaches or malware outbreaks, Elasticsearch can be used to quickly search through logs and other relevant data to gather evidence and track down the source of the attack.
• Intrusion Detection/Prevention Systems (IDS/IPS): IDS/IPS systems generate a lot of alerts which need to be investigated by security analysts. Elasticsearch can act as a central repository for these alerts, allowing security teams to easily search and filter them based on multiple criteria such as IP addresses, user agents etc., and automate certain actions like blocking IPs etc.
• Security Information and Event Management (SIEM): SIEM solutions aggregate data from various security tools including firewalls ,IPS/IDS etc and uses machine learning algorithms to create correlation rules .Elasticsearch can act as a powerful back end database to store this correlated data and provide real time query capabilities to detect new kinds of advanced persistent threat .It provides ability to perform complex queries on structured ,semi structured and unstructured data at very low latency which makes it extremely scalable compared to traditional relational databases
• Fraud detection: Elasticsearch can be used to build models for fraud detection by analyzing transactional data, browsing behavior, and device metadata.
• Insider Threat Detection: By collecting and indexing data related to employee activity within company networks and infrastructure, Elasticsearch can be leveraged to flag any unusual activities and raise red flags for further investigation

Elastic Security

The Elastic stack has a dedicated solution for cybersecurity purposes that combines analytical capabilities (like threat detection) and protection capabilities (like endpoint prevention and response) into one offering. On a high level, Elastic Security offers following benefits and capabilities:

• A rule-based detection engine to identify attacks and misconfigurations
• Machine learning anomaly jobs to detect signatureless attacks
• Kibana-based interactive visualizations for ad-hoc analysis
• A central place for case management, event triage and investigations

The above diagram depicts the overall architecture of Elastic Security and its different components.

Data is ingested into Elasticsearch from different sources:

• Using Beats to collect audit logs, metrics, network packets, etc.
• Using Logstch to collect and transform any format of logs
• Using Elastic Agent to collect data from hosts and remote machines
• Using third party connectors, for instance to collect data from databases

A Detection engine is used to continuously search for signs of attacks (e.g. suspicious host and network activity). It relies on a set of Detection rules to periodically search the data for suspicious events and generate alerts when such events are discovered. Users can provide their own rules or use the ones packages with Elastic Security. Furthermore, it provides a Machine learning base a anomaly detection components that analyses host and network data for potential attacks and provide a score for further investigation by an analyst.

In the rest of this article we will focus on the Vulnerability management use case of cybersecuirty and discuss how Elastic stack can be leveraged for this specific type of applications.

Vulnerability management

As depicted in the following diagram, Vulnerability management can be defined as the process of identifying, analyzing, and addressing weaknesses and vulnerabilities present in software products, networks, or systems. It involves continuous discovery, tracking, reporting, and mitigation of known vulnerabilities to prevent potential threats from being exploited. Thus making it an important practice for any organization as it helps maintaining a secure environment, meeting regulatory compliance obligations, and reducing risks from cybersecurity threats.

Elasticsearch offers numerous benefits when applied to vulnerability management processes, providing both automation and scalability to address the increasing volume and complexity of incoming vulnerabilities. Here are some specific use cases where Elasticsearch might play a vital role in vulnerability management:

• Vulnerability Correlation: Elasticsearch allows security teams to correlate vulnerabilities across their entire environment by storing and searching through large volumes of scan results from various sources. This helps prioritize remediation efforts and ensure critical assets receive the most attention.
• Patch Management: Elasticsearch can help organizations efficiently manage patches and updates for known vulnerabilities. With accurate tracking of installed software versions, IT administrators can proactively apply necessary patches before vulnerabilities become exploitable.
• Asset Discovery & Tracking: Organizations often struggle with asset discovery and tracking, especially in dynamic environments with rapidly changing infrastructures. Elasticsearch can aid in identifying all connected devices on a network, providing contextual information around each asset, and associating detected vulnerabilities accordingly.
• Compliance Monitoring: To meet industry regulations such as PCI DSS, HIPAA, GDPR, etc., organizations must continuously monitor for compliance gaps and ensure effective risk mitigation measures are in place. Elasticsearch enables faster scanning, reporting, and auditing processes, making it easier to maintain regulatory compliance.
• False Positive Filtering: Security professionals spend significant time manually reviewing vast amounts of scan results to distinguish actual issues from false positives. Leveraging Elasticsearch’s full text search functionality, security teams can automatically reduce noise levels by weeding out unlikely matches.
• Contextual Intelligence Sharing: With open APIs, integration into numerous third-party systems is achievable, enabling collaboration and sharing of data insights among stakeholders. By consuming external feeds such as threat intelligence reports, incident notifications, and CVE advisories.

That’s all folks

We went through a veriety of cybersecurity related use cases for Elasticsearch and then focused on the vulnerability management use case. In a next article, we will implement a vulnerability tracking system based on Elasticsearch. Stay tuned!

I hope you enjoyed this article, feel free to leave a comment or reach out on twitter @bachiirc.