Keepnet Labs Elasticsearch IntroductionOrhan Sari
Keepnet Labs Elasticsearch Introduction
Elasticsearch is an allocated search and analytics engine based on the Lucene library. Logstash and Beats help to get, aggregate, and improve your data and store it in Elasticsearch. Kibana allows you to interactively search, see, and distribute insights into your data and control and watch the stack: Elasticsearch is where the indexing, search, and analysis magic happen.
1- How does Elasticsearch work?
Elasticsearch provides real-time search and analytics for all sorts of data. Whether you have structured or unstructured text, numerical data, or geospatial data, Elasticsearch can efficiently store and index it in a way that helps fast searches. You can go far beyond simple data retrieval and aggregate information to discover trends and patterns in your data. And as your data and query volume grows, the distributed nature of Elasticsearch enables your deployment to grow seamlessly right along with it.
While not all problems are search problem, Elasticsearch offers speed and flexibility to handle data in a wide variety of use cases:
- Add a search box to an app or website
- Store and analyze logs, metrics, and security event data
- Use machine learning to automatically model the behaviour of your data in real-time
- Automate business workflows using Elasticsearch as a storage engine
- Manage, integrate, and analyze spatial information using Elasticsearch as a geographic information system (GIS)
- Store and process genetic data using Elasticsearch as a bioinformatics research tool
We’re continually amazed by the novel ways people use to search. But whether your use case is similar to one of these, or you’re using Elasticsearch to tackle a new problem, the way you work with your data, documents, and indices in Elasticsearch is the same. 1
2- Keepnet Labs Threat Intelligence and Elasticsearch – Keepnet Labs Elasticsearch
Collecting threat intelligence to prevent future crimes has been an important method in the information security industry. Elasticsearch gives you an opportunity to manage these intelligence data for threat intelligence purposes. It is possible to utilise Logstash, Elasticsearch, and Kibana in working with threat intelligence.
One of the benefits is to make use of translate filters in logstash and alert on data that corresponds to data in blacklists. You can also ingest the data into Elasticsearch and then output it in CSV and make use of the CSV file in a translate filter. This is where the translate filter checks the CSV if it includes a value and then, performs an action if the CSV includes the value. Reasonably, you just want to fill Elasticsearch with possible email addresses that could be sending you phishing emails, blacklisted IPs etc. This would allow you to search for an IP and help make an informed decision whether connections to or from the IP could potentially be malicious. Elasticsearch has been important for Keepnet Labs for in this aspect. 2
3- Why Does Keepnet Labs Use Elasticsearch?
The speed and scalability of Elasticsearch have given Keepnet the ability to index threat data that it is used for a number of use cases:
- Threat Data Search
- Threat Data Logging and log analytics
- Threat Data Monitoring Monitoring
- Threat Data Analysis
- Security Analytics
Due to the nature of big data sources, it is difficult to collect, clean, analyse and manage the distribution of security data in a unified manner. Keepnet Elasticsearch leverages the Elastic Stack to power Keepnet Threat Intelligence.
4- Using Elasticsearch and the Elastic Stack for Advanced Threat Hunting
Cybersecurity threats have become aggressively sophisticated and the data and speed required to detect targeted attacks have increased dramatically – the signature- and rule-based approaches simply don’t cut it anymore.
The need for a cybersecurity solution that maintains and utilizes data effectively, whilst providing a simple yet powerful interface for security analysts to stop malicious users in their tracks is paramount for any security operation. Keepnet Labs Elasticsearch
This webinar demonstrates the Elastic Stack’s ability to carry out threat hunting activities needed to keep pace with the threats of today and tomorrow, and cover:
- The state of today’s threat hunting landscape
- The importance of fast, scalable, and relevant threat intelligence and data enrichment
- How to integrate various types of threat feeds into Elasticsearch
- How to use Kibana visualizations for interactive threat hunting
- The role of machine learning for automated anomaly detection 3