“The whole is more than the sum of its parts”

Unlike conventional behaviour analysis, BEAST records all system behaviour in a graph and thus provides a holistic view. The technology is based on a self-developed, lightweight graph database. In this interview, Arnas Staude explains the details of the development process to us.

Can you explain how BEAST works using an example?

BEAST focuses on how processes behave. There is a host of graphs containing benign as well as harmful behaviour stored in the backend. Now, we can use our rules to ensure that we identify a malicious processes, because with every change in the graph we scan the environment of the node for known patterns. BEAST shows its strengths when malicious behaviour is distributed over multiple processes. BEAST recognises these connections by means of subgraph matching and compares the current behaviour with existing rules.

A malicious process typically involves the following sequence: Process 1 creates a file in a specific folder and launches process 2. This process in turn changes firewall rules and other security settings so that it can operate later without interruption – for example when stealing access credentials or credit card information. At the same time, process 1 downloads another file, launches process 3 and then deletes itself automatically to cover its tracks. Process 3 establishes the persistence of the malware and creates a RunKey with an entry in the registry on process 2. RunKeys use malware authors to create persistence so that, in the event of a restart, the malicious processes launch automatically as well. This is what the rule looks like: we have a directed graph with three process nodes and three file nodes. We also know the sequence of the individual processes. The scanner compares this behaviour with the existing graphs and can clearly classify it as malicious. This is because individual process steps are not in themselves malicious. One example is the downloading of files from the Internet. This happens not only with malware, but also with updates for programs that are installed on the computer.

We wanted to understand the detection better by having and being able to view the data. And since behavioural data in its natural form is actually graphic, it was obvious that we should use a graph database. This allows us to handle the data and the associated data growth very efficiently.

Why did you decide to use a graph database? What are the advantages of the technology compared to the former behaviour analysis?

We wanted to understand the detection better by having and being able to view the data. And since behavioural data in its natural form is actually graphic, it was obvious that we should use a graph database. This allows us to handle the data and the associated data growth very efficiently. It works simply and quickly. Processes can be represented as a tree, for example. A high level process launches multiple low level processes, which in turn each launch multiple lower level processes of their own. In this way, we have stored both the process structure and other information such as file accesses in a single system, so we can then understand the relationships. The processes then form the central nodes in the graph. Further information strands branch

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