fail2ban is known to do a great job at giving attackers a hard time when they try to “test” passwords or enumerate users of a service. fail2ban constantly analyses relevant log files and keeps track of IP addresses trying to log into such services. If a configurable threshold is reached, it uses the Linux firewall (Netfilter / iptables) to block the suspected attackers.
As of the past 2 months, we’ve received two separate reports of two unrelated SQLi vector vulnerabilities in MISP that can lead to any authenticated user being able to execute arbitrary SQL queries in MISP.
Jupyter notebook used during the training session.
MISP has been a widely used open source CTI platform for the past decade, with a long list of tools that allow users to customise the data models and contextualisation of the platform, yet true customisation of the actual workflows and processes had to be done externally using custom scripts.
When you receive threat intelligence from different sources you quickly realise there is a big difference in the quality of the received information. Where some organisations go to great length to ensure their events are accurate, complete and contextualised, other organisations use different standards. Some of these differences are caused by particular use cases but can also be caused by human errors or maturity growing pains. Regardless of what’s causing these differences, as a consumer, it costs time to wade through events and manually curate them.
Communities can share cyber threat intelligence on platforms, such as MISP. In the H2020 project Prometheus TNO has developed a way to securely aggregate cyber threat intelligence and publish the result on MISP.
Let’s say that by no means should an attribute of type passport-number leave your MISP instance. Aside from the analyst following best practices when encoding the data, MISP does not have a built-in mechanism to prevent these leaks to happen, but now you can achieve this by using a third-party tool called misp-guard.
As of version 2.4.162, MISP includes a periodic summary feature allowing users to consult a summary based on a requested time-frame for data the user has access to.
There are a lot of websites that regularly publish reports on new threats, campaigns or actors with useful indicators, references and context information. Unfortunately only a few publish information in an easily accessible and structured format, such as a MISP-feed. As a result, we often find ourself manually scraping these sites, and then copy-pasting this information in new MISP events. These tedious tasks are time-consuming and certainly not the most interesting aspect of CTI-work.
MISP objects are containers around contextually linked attributes. They support analysts in grouping related attributes and describing the relations that exist between the data points in a threat event. Combining these objects and relations is something that can then be used to represent the story of what is being told in the threat event.
MISP is widely known as a powerful tool to gather, correlate and share information. As a response to the growing information-sharing maturity of the community, more features have been introduced over the past few years to meet analyst skills and requirements.
Here the goal is to push to MISP information gathered on Github. The script add_github_user.py will be used as an example.
A previous post covered how to do MISP service monitoring with OpenNSM. Because having different options is good, this post covers how to achieve similar results with Cacti. For those not familiar with Cacti: it is a network graphing solution designed to harness the power of RRDTool’s data storage and graphing functionality.
Many organisations adore how quick and easy MISP can be set up. Once it’s running, people start integrating it into their processes and begin to rely on it, for instance by exporting indicators and using them in security or network focused software. Usually this is the time when MISP becomes a fundamental part in the portfolio of fighting attacks, and the need for reliability grows rapidly.
MISP Galaxies and Clusters are an easy way to add context to data. Compared to the relatively simple concept of tags and taxonomies, they allow you to add more complex data structures. There is already a large list of galaxies and clusters available as a community effort, and directly accessible within MISP, but it’s always possible these do not fully address your needs.
Open data defines the idea of making some data freely available for everyone to use with a possibility of redistribution in any form. The open data format provides metadata information describing the datasets along with resources stored within the portal.
We’re proud to announce the CogSec Collab MISP Community - the first public MISP sharing group dedicated to misinformation and information campaigns.
Having access to a large amount of Threat information through MISP Threat Sharing communities gives you outstanding opportunities to aggregate this information and take the process of trying to understand how all this data fits together telling a broader story to the next level. We are transforming technical data or indicators of compromise (IOCs) into cyber threat intelligence. This is where the analytical challenge begins.
One topic that regularly pops up during trainings and users that are just getting started with MISP in general is the question of running your own MISP versus using a hosted MISP instance. We have seen a lot of great efforts by ISACs, organisations with national or sectorial responsibilities leading the charge and acting as a central sharing hub for communities.
This blog post aims to give a thorough introduction of a new functionality added in MISP 2.4.116, allowing users and organisations to easily expire information depending on their personalised objectives and targets.
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