A look under the hood of a decentralised VPN Application.

( original text byDonatas Kučinskas )

MysteriumVPN is the client application of Mysterium Network, a project focused on providing security and privacy to web 3 applications.

In this article, we will discuss the architecture of MysteriumVPN and how it integrates with Mysterium Node to ensure an encrypted end to end flow of data through Mysterium Network.

Cross-platform architecture

Usually, you need separate builds for each platform. Now that cross-platform technology has improved, this is no longer the case.

For desktop:

Electron is a framework which allows us to build cross-platform applications using common web technologies such as HTMLCSS and Javascript. We are using Electron which allows us to develop one application for two platforms for desktop — Windows and Mac OSLinux coming soon. Download our alpha.

Under the hood of an Electron application, sits a Chromium browser; A website, rendered by an embedded browser.

For mobile:

We are kicking off our mobile development for MysteriumVPN, with Android versions set to release shortly.

For this, we are using React Native for cross-platform applications.

Most of MysteriumVPN is written in Javascript, which is run in a separate process. Javascript generates the virtual structure of the user interface. This Javascript process communicates to native mobile processes which are responsible for rendering the actual user interface as you see it.

The architecture of MysteriumVPN Desktop Client Application

How MysteriumVPN works on desktop:

Since we are using Electron, we have two processes, MAIN and RENDERER.

MAIN is the first process which is started when the application starts. It is a NodeJS process which is responsible for managing the following functions:

  • Application state and internal operations
  • Tray
  • Kicking off the RENDERER process

The second process is RENDERER and it is responsible for displaying the graphical user interface for the application.

Communication between processes:

Both the MAIN and RENDERER processes need to communicate with each other to stay in sync. For this reason, we are using a standard approach of Inter-Process Communication (IPC).

Javascript is not type-safe, which isn’t very reliable. We use Flow static type checker which adds type-safety for Javascript. This especially applies to syncing data between processes — it becomes less reliable when using out-of-the-box IPC. To improve that, with custom implementation on top to have type-safety.

MessageTransport describes a single typed message which is sent between processes. It creates alignment between both processes by introducing sender and receiver objects, ensuring that both sides expect the same arguments of this message.

Here is an implementation:

class MessageTransport<T> {
 _channel: string
 _messageBus: MessageBus
constructor (channel: string, messageBus: MessageBus) {
 this._channel = channel
 this._messageBus = messageBus
 }
buildSender (): MessageSender<T> {
 return new MessageSender(this._channel, this._messageBus)
 }
buildReceiver (): MessageReceiver<T> {
 return new MessageReceiver(this._channel, this._messageBus)
 }
}
class MessageSender<T> {
 _channel: string
 _messageBus: MessageBus
constructor (channel: string, messageBus: MessageBus) {
 this._channel = channel
 this._messageBus = messageBus
 }
send (data: T) {
 this._messageBus.send(this._channel, data)
 }
}
class MessageReceiver<T> {
 _channel: string
 _messageBus: MessageBus
constructor (channel: string, messageBus: MessageBus) {
 this._channel = channel
 this._messageBus = messageBus
 }
on (callback: T => void) {
 this._messageBus.on(this._channel, callback)
 }
removeCallback (callback: T => void) {
 this._messageBus.removeCallback(this._channel, callback)
 }
}

Here is an example of communication between both these MAIN and RENDERER processes:

Example: communicating country proposal updates between processes:

MAIN process is managing country proposals internally and it sends all updates:

this._countryList.onUpdate(countries => {
  this._communication.countryUpdate.send(countries)
})

RENDERER process listens for country updates,

this.rendererCommunication.countryUpdate.on(this.onCountriesUpdate)
...
onCountriesUpdate (countries) {
  this.countriesAreLoading = false
  this.countryList = countries
}

Having such an abstraction layer ensures that communication is type-safe, reliable and features around it are simple to test.

How do we integrate Mysterium Node with MysteriumVPN Application?

Once we’ve rendered the application layer, we still need to connect MysteriumVPN to Mysterium NodeMysterium Nodeis a software that connects you to Mysterium Network where you are able to exchange value for bandwidth.

MysteriumVPN is a client application of Mysterium Network. The successful running of our dVPN on the network will attract other use cases from existing or future businesses that require end-to-end encryption of data, thereby expanding Mysterium Network’s ecosystem.

We require specific information to ensure the successful running of our dVPNservice.

Operation System Service
Since we are running Mysterium Node under the MysteriumVPN application we need to supervise the Mysterium Node to ensure that it works.

Our Data Protection Policy
We make a clear distinction between personal data and usage data. We do not collect information on who you are. We collect data on session and connection inputs and outputs. This is important data for us as it gives us visibility on how our technology fares against the realities of cyber oppression. Check out our privacy policy for more information.

Logging
Since we are integrating Mysterium Node into the MysteriumVPN application, the application itself gets quite complex. That’s why we have to be prepared to log errors from everywhere, — our application, Mysterium Node, and from Electron.

That means that there are three sources of inputs. When we are inspecting something, we need to understand that these errors can happen in three different places. We need to synchronise those and collect all relevant data from these sources.

Data management in the era of web 3 is complex and we hope to do so in an ethical and fair manner. Check out how our no logs policy protects your personal data.

Build on Mysterium Network

We have an npm package that allows for you to connect to Mysterium Nodeeasily. This is the same package that the MysteriumVPN uses to connect to Mysterium Network. This can be used for any application — it’s literally plug and play.

Interested in contributing to Mysterium Network? We are an open source project focused on bringing privacy, security and freedom to web 3. Check out our Github.

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GPU side channel attacks can enable spying on web activity, password stealing

( Original text )

Computer scientists at the University of California, Riverside have revealed for the first time how easily attackers can use a computer’s graphics processing unit, or GPU, to spy on web activity, steal passwords, and break into cloud-based applications.

GPU side channel attacks

Threat scenarios

Marlan and Rosemary Bourns College of Engineering computer science doctoral student Hoda Naghibijouybari and post-doctoral researcher Ajaya Neupane, along with Associate Professor Zhiyun Qian and Professor Nael Abu-Ghazaleh, reverse engineered a Nvidia GPU to demonstrate three attacks on both graphics and computational stacks, as well as across them.

All three attacks require the victim to first acquire a malicious program embedded in a downloaded app. The program is designed to spy on the victim’s computer.

Web browsers use GPUs to render graphics on desktops, laptops, and smart phones. GPUs are also used to accelerate applications on the cloud and data centers. Web graphics can expose user information and activity. Computational workloads enhanced by the GPU include applications with sensitive data or algorithms that might be exposed by the new attacks.

GPUs are usually programmed using application programming interfaces, or APIs, such as OpenGL. OpenGL is accessible by any application on a desktop with user-level privileges, making all attacks practical on a desktop. Since desktop or laptop machines by default come with the graphics libraries and drivers installed, the attack can be implemented easily using graphics APIs.

The first attack tracks user activity on the web. When the victim opens the malicious app, it uses OpenGL to create a spy to infer the behavior of the browser as it uses the GPU. Every website has a unique trace in terms of GPU memory utilization due to the different number of objects and different sizes of objects being rendered. This signal is consistent across loading the same website several times and is unaffected by caching.

The researchers monitored either GPU memory allocations over time or GPU performance counters and fed these features to a machine learning based classifier, achieving website fingerprinting with high accuracy. The spy can reliably obtain all allocation events to see what the user has been doing on the web.

In the second attack, the authors extracted user passwords. Each time the user types a character, the whole password textbox is uploaded to GPU as a texture to be rendered. Monitoring the interval time of consecutive memory allocation events leaked the number of password characters and inter-keystroke timing, well-established techniques for learning passwords.

The third attack targets a computational application in the cloud. The attacker launches a malicious computational workload on the GPU which operates alongside the victim’s application. Depending on neural network parameters, the intensity and pattern of contention on the cache, memory and functional units differ over time, creating measurable leakage. The attacker uses machine learning-based classification on performance counter traces to extract the victim’s secret neural network structure, such as number of neurons in a specific layer of a deep neural network.

The researchers reported their findings to Nvidia, who responded that they intend to publish a patch that offers system administrators the option to disable access to performance counters from user-level processes. They also shared a draft of the paper with the AMD and Intel security teams to enable them to evaluate their GPUs with respect to such vulnerabilities.

In the future the group plans to test the feasibility of GPU side channel attacks on Android phones.