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In the landscape of web maliciousness Remote Administration Trojans are not a new trend but their usage is still strong and growing steady.
At its core a RAT is a backdoor facility used to let an attacker enter unnoticed into the victim computer to control it remotely. For example, most banking trojan nowadays are using remote desktop modules to open a VNC/RDP channel. They allow an attacker to exfiltrate the money from within the users browser inside its legit session.
Newer sophisticated malware like Dyre are stepped up the game by completely diverting the user on fake banking portals. So they keep the login session alive in the background. Once the user has disclosed the required credentials, the attacker connect to the user machine via a remote desktop channel. Finally he performs the wire fraud unnoticed.
RAT Screen Control
The usual attack is comprised by two phases.
First, a Dyre-infected user enters the banking website location in the browser. The request is then proxied by the malware to a fake website that is identical to the bank website, just after the real login. In the background Dyre keep the real banking session open.
The second phase begins as soon as the attacker receives an automated Jabber notification with user session data. Also recives a VNC callback to a protected terminal server. He then starts interacting with the user by sending challenge questions, fake pages, and a fake login field to the fake browsing session. The user begins responding to the attacker’s forms, providing the needed information. In the meanwhile, the attacker initiates a screen control session on the user’s PC to exploit the real session and perform the wire fraud.
This is why this kind of attack it is so hard to detect.
For the most part the attack, killchain is happening out of reach from the bank’s anti fraud capabilities. The only exception is the final exfiltration phase when the only thing left is the chance to detect the attacker session. Even then, the attacker is coming from within the legit user session making things harder.
These inner weaknesses of classic agentless fraud detection techniques are the reason behind the increase of popularity and sophistication of this kind of attacks.
Agentless fraud detection can detect infected users, or identify the attacker session by diverting users to web fakes and masquerading the attacker session. However, there is a high chance that the entire detection process can be nullified.
Then how can a bank portal understand what’s going on? What they see is a session initiated from the usual user’s IP address, using the usual browser fingerprint, without any kind of webinjects, ASTs, or other common indicators of compromise.
Advanced RAT detection techniques.
To respond to this new kind of fraud, Minded Security has started to research viable detection techniques. Also has implemented a new solution based on Telemetry Modeling.
Since the session appears to be from the usual user’s IP address and browser fingerprint, how can a bank portal identify any anomalies? There are no webinjects, automated script tools (AST), or other common indicators of compromise.
Desktop Properties Detection
This is the most basic detection, whose point is to detect anomalies in the properties of the browser/desktop used. For example, older RDP protocols might alter the color depth, or hidden VNC session may have unusual desktop resolutions.
Those indicators can be tracked and then correlated to build a detection.
Detection of Velocity Pings or Session Keepalives
While waiting for the user to disclose their PIN/OTP, the attacker must keep the user session alive. This is necessary to use the session later to perform a wire transfer.
Chiedi a ChatGPTThis is what “velocity pings” are for: periodic faster HTTP requests whose goal is to keep the session alive.
The requests cadence, their content can be used to build an indicator of compromise and trigger a detection.
Telemetry Modeling of User Biometrics
The point of this approach is to track the user telemetry to build a model of the user (keyboard usage, mouse movements, input gestures). Once the model is built it is used as yardstick in an anomaly detection context. The output provided give an insight if the current session is being used by the usual users.
Unfortunately, while this information is indeed useful, the weaknesses are manifold.
First, the infrastructure needed is far from lightweight. It needs to store big data for the user models. Also he has to run complex machine learning algorithms nearly real time to perform the anomaly detection. This means a complex and expensive infrastructure.
Secondly, the detection is fooled in the corner case of a single machine shared by different people, think of a corporate environment [3].
Telemetry Modeling of Protocols
This detection is among the most advanced.
It relies on tracking glitches and anomalies in how the user’s telemetry is transmitted via the remote desktop protocol.
For example, if a remote desktop is in place, the telemetry data is compressed and transmitted through the remote desktop protocol itself.
Alternatively, if the user is browsing the bank page through a virtual machine, the input is filtered by the VM layer.
All these added software layers operates to synchronize between the input received and the input reproduced adding glitches that could be tracked as anomalies.
This let to have a very light engine that is able to lively catch latency generated by user interface flowing through filter-driver chains. Typically VM guest environments and remote control tools install additional layered interfaces to replicate cursor positions and this creates latency patterns we can detect.
Once these anomalies are collected they are used to understand in real time if there is a remote connection in place. We provide this detection approach in our anti fraud suite AMT – RATDET Module.
IOC Detection
A malware infection can alter the profile of the user’s machine/browser and these alterations could be tracked . Can be used as indicators of compromise to flag the user as a potential victim of fraud.
It could be possible to check the existence of certain files on the user file system. Some examples are suspicious executables, hidden vnc servers and others that can be used as an evidence of infection.
As an example here is a brief proof of concept that is also used by common exploit kits:
These indicators vary from malware to malware but are very useful to prevent fraud in the early stages of the kill chain.
They can be detected as soon as the user is infected, and before the exfiltration is put in place.
In this rat-race against financial malware, there is not a de facto detection to be used. Malware are constantly evolving and so should our defense techniques.
In our opinion, the recipe to a successful anti fraud monitoring lies into having a flexible and modular approach. It is mixing different detection techniques to build an unified risk model of the users.
[1]: https://en.wikipedia.org/wiki/Remote_administration_software
[2]: http://www.lockheedmartin.com/us/what-we-do/information-technology/cybersecurity/tradecraft/cyber-kill-chain.html
[3]: A provider for this kind of solution is BioCatch.