21 Jun 2018

feedAndroid Developers Blog

Android Studio 3.2 Beta

Posted by Jamal Eason, Product Manager, Android

Starting today, you can download Android Studio 3.2 Beta. Previewed at Google I/O 2018, the latest release of the official Android IDE is focused on helping onboard you to all the new features launched around Google I/O -- Android JetPack, Android P Developer Preview, and the new Android App Bundle format. There are also several other exciting new features included in Android Studio 3.2 to accelerate your app development, such as Emulator Snapshots and the Energy Profiler.

As the usage of Android Studio has grown in the 3.5 years since version 1.0, we have also become increasingly obsessed with quality. We continue to invest in quality because we know that millions of app developers spend almost everyday in Android Studio and need a reliable set of tools. Stability, build times, and other quality work will be the primary focus for our next release once we finish Android Studio 3.2. We also did not want to wait, so we have made checkins to address memory leaks and performance issues as well as fixed more than 450 bugs. Thank you for the continued feedback and please keep it coming so we can focus on the areas you care about most in the next version of Android Studio. If want to try out the latest features, and assess the improvements in quality, you can download Android Studio on the beta release channel.

What is inside of Android Studio 3.2

Building on the canary release of Android Studio 3.2, the Beta release includes:

Build Android App Bundle

Android Emulator Snapshots

Energy Profiler

Check out the full write-up of all the major features organized by development flow listed below and on the canary blog:

Develop
  • Navigation Editor
  • AndroidX Refactoring
  • Sample Data
  • Material Design Update
  • Android Slices
  • CMakeList editing
  • What's New Assistant
  • New Lint Checks
  • Intellij Platform Update

Build

  • Android App Bundle
  • D8 Desugaring
  • R8 Optimizer
 Test
  • Android Emulator Snapshots
  • Screen Record in Android Emulator
  • Virtual Scene Android Emulator Camera
  • ADB Connection Assistant

Optimize

  • Energy Profiler
  • System Trace
  • Profiler Sessions
  • Automatic CPU Recording
  • JNI Reference Tracking

Sessions at Google I/O '18

With the release of Android Studio 3.2 at Google I/O '18, the Android Studio team also presented a series of sessions about Android Studio. Watch the following videos to see the latest features in action and to get tips & tricks on how to use Android Studio:

Download & Feedback

Download the latest version of Android Studio 3.2 from the beta channel download page. If you are using a previous versions of Android Studio, make sure you update to Android Studio Beta 1 or higher. If you also want to maintain a stable version of Android Studio, you can run the stable release version and beta release versions of Android Studio at the same time. Learn more.

To use the mentioned Android Emulator features make sure you are running at least Android Emulator v27.3+ downloaded via the Android Studio SDK Manager.

Please note, to ensure we maintain product quality, some of the features you saw in the canary channel like Navigation Editor are not enabled by default. To turn on canary release channel features go to File → Settings → Experimental → Editor → Enable Navigation Editor.

If you find a bug or issue, feel free to file an issue. Connect with us -- the Android Studio development team ‐ on our Google+ page or on Twitter.

21 Jun 2018 11:53pm GMT

Android Things client library for Google Cloud IoT Core

Posted by Wayne Piekarski, Developer Advocate for IoT +WaynePiekarski @WaynePiekarski

We're releasing a client library to make it easy to use Google Cloud IoT Core from Android Things devices. With just a few lines of code, you can easily connect to the IoT Core MQTT bridge, authenticate the device, publish device telemetry and state, subscribe to configuration changes, and handle errors and network outages.

What is Cloud IoT Core?

Cloud IoT Core is a fully managed service on Google Cloud Platform that allows you to easily and securely connect, manage, and ingest data from millions of globally dispersed devices. Cloud IoT Core, in combination with other services which make up Google's Cloud IoT platform, provides a complete solution for collecting, processing, analyzing, and visualizing IoT data in real time, to support improved operational efficiency, compliance, or revenue management. Android Things is designed to support everything from collecting telemetry data to powerful computer vision, audio processing, and machine learning applications, all on device, and using Cloud IoT Core, push your data into Google Cloud Platform for further analysis.

Cloud IoT Core client library

The Cloud IoT Core client library was designed to enable Android Things developers to get started with just a few lines of code. The client library handles the networking, threading, and message handling, implementing best practices for authentication, security, error handling, and offline operation.

Cloud IoT Core maintains a device registry that keeps track of approved devices, and each device uses a public key to authenticate with the server. Android Things provides many features to support secure IoT applications, including a hardware-backed Android Keystore that ensures cryptographic key material is protected. The client library supports both RSA and ECC keys, and implements the generation of JSON Web Tokens (JWTs) for authentication with Cloud IoT Core.

Once the connection is established, devices can publish their telemetry data to one or more buckets in the telemetry topic, as well as report their internal state to a separate device state topic. The device state is intended to store information such as software versions or the number of working sensors. The telemetry messages are for all other data from the device, such as actual sensor measurements. Devices can also subscribe to configuration changes published from Cloud IoT Core.

Because IoT devices operate in the real world with poor wireless conditions, the client library provides extensive support for handling errors, and for caching and retransmitting events later. For developers requiring custom offline behavior, the library's queue is configurable and even replaceable. This provides detailed control over which events to save and the order in which they are sent when back online.

Device provisioning and authentication with Android Things

The Cloud IoT Core client library is part of our overall vision for device provisioning and authentication with Android Things. To learn more about this, watch the video of our presentation from Google I/O 2018:

Sample code

Getting started with the Cloud IoT Core client library is simple. You can simply add the following to the build.gradle file in your Android Things project:

implementation 'com.google.android.things:cloud-iot-core:1.0.0'

The library is also available as open source on GitHub if you prefer to build it yourself. We also have a sample that shows how to implement a sensor hub on Android Things, collecting sensor data from connected sensors and publishing them to a Google Cloud IoT Pub/Sub topic.

It is easy to start using the client library in your own code. The following Kotlin example demonstrates how to create a new configuration and client based on your project.

var configuration = IotCoreConfiguration.Builder().
                         .setProjectId("my-gcp-project")
                         .setRegistry("my-device-registry", "us-central1")
                         .setDeviceId("my-device-id")
                         .setKeyPair(keyPairObject)
                         .build()

var iotCoreClient = IotCoreClient.Builder()
              .setIotCoreConfiguration(configuration)
              .setOnConfigurationListener(onConfigurationListener)
              .setConnectionCallback(connectionCallback)
              .build()

iotCoreClient.connect()

Next, you can publish telemetry information or device state, using the following Kotlin examples.

private fun publishTelemetry(temperature: Float, humidity: Float) {
    // payload is an arbitrary, application-specific array of bytes
    val examplePayload = """{
        |"temperature" : $temperature,
        |"humidity": $humidity
        |}""".trimMargin().toByteArray()
    val event = TelemetryEvent(examplePayload, topicSubpath, TelemetryEvent.QOS_AT_LEAST_ONCE)
    iotCoreClient.publishTelemetry(event)
}

private fun publishDeviceState(telemetryFrequency: Int, enabledSensors: Array<String>) {
    // payload is an arbitrary, application-specific array of bytes
    val examplePayload = """{
        |"telemetryFrequency": $telemetryFrequency,
        |"enabledSensors": ${enabledSensors.contentToString()}
        |}""".trimMargin().toByteArray()
    iotCoreClient.publishDeviceState(examplePayload)
}

Additional resources

You can learn more about building for Android Things at the developer site. For more information about getting started with Cloud IoT Core, visit the information page and documentation. Finally, join Google's IoT Developers Community on Google+ to let us know what you're building with Android Things and Cloud IoT Core!

21 Jun 2018 9:13pm GMT

Better Biometrics in Android P

Posted by Vishwath Mohan, Security Engineer

To keep users safe, most apps and devices have an authentication mechanism, or a way to prove that you're you. These mechanisms fall into three categories: knowledge factors, possession factors, and biometric factors. Knowledge factors ask for something you know (like a PIN or a password), possession factors ask for something you have (like a token generator or security key), and biometric factors ask for something you are (like your fingerprint, iris, or face).

Biometric authentication mechanisms are becoming increasingly popular, and it's easy to see why. They're faster than typing a password, easier than carrying around a separate security key, and they prevent one of the most common pitfalls of knowledge-factor based authentication-the risk of shoulder surfing.

As more devices incorporate biometric authentication to safeguard people's private information, we're improving biometrics-based authentication in Android P by:

A better security model for biometrics

Currently, biometric unlocks quantify their performance today with two metrics borrowed from machine learning (ML): False Accept Rate (FAR), and False Reject Rate (FRR).

In the case of biometrics, FAR measures how often a biometric model accidentally classifies an incorrect input as belonging to the target user-that is, how often another user is falsely recognized as the legitimate device owner. Similarly, FRR measures how often a biometric model accidentally classifies the user's biometric as incorrect-that is, how often a legitimate device owner has to retry their authentication. The first is a security concern, while the second is problematic for usability.

Both metrics do a great job of measuring the accuracy and precision of a given ML (or biometric) model when applied to random input samples. However, because neither metric accounts for an active attacker as part of the threat model, they do not provide very useful information about its resilience against attacks.

In Android 8.1, we introduced two new metrics that more explicitly account for an attacker in the threat model: Spoof Accept Rate (SAR) and Imposter Accept Rate (IAR). As their names suggest, these metrics measure how easily an attacker can bypass a biometric authentication scheme. Spoofing refers to the use of a known-good recording (e.g. replaying a voice recording or using a face or fingerprint picture), while impostor acceptance means a successful mimicking of another user's biometric (e.g. trying to sound or look like a target user).

Strong vs. Weak Biometrics

We use the SAR/IAR metrics to categorize biometric authentication mechanisms as either strong or weak. Biometric authentication mechanisms with an SAR/IAR of 7% or lower are strong, and anything above 7% is weak. Why 7% specifically? Most fingerprint implementations have a SAR/IAR metric of about 7%, making this an appropriate standard to start with for other modalities as well. As biometric sensors and classification methods improve, this threshold can potentially be decreased in the future.

This binary classification is a slight oversimplification of the range of security that different implementations provide. However, it gives us a scalable mechanism (via the tiered authentication model) to appropriately scope the capabilities and the constraints of different biometric implementations across the ecosystem, based on the overall risk they pose.

While both strong and weak biometrics will be allowed to unlock a device, weak biometrics:

These measures are intended to allow weaker biometrics, while reducing the risk of unauthorized access.

BiometricPrompt API

Starting in Android P, developers can use the BiometricPrompt API to integrate biometric authentication into their apps in a device and biometric agnostic way. BiometricPrompt only exposes strong modalities, so developers can be assured of a consistent level of security across all devices their application runs on. A support library is also provided for devices running Android O and earlier, allowing applications to utilize the advantages of this API across more devices .

Here's a high-level architecture of BiometricPrompt.

The API is intended to be easy to use, allowing the platform to select an appropriate biometric to authenticate with instead of forcing app developers to implement this logic themselves. Here's an example of how a developer might use it in their app:

Conclusion

Biometrics have the potential to both simplify and strengthen how we authenticate our digital identity, but only if they are designed securely, measured accurately, and implemented in a privacy-preserving manner.

We want Android to get it right across all three. So we're combining secure design principles, a more attacker-aware measurement methodology, and a common, easy to use biometrics API that allows developers to integrate authentication in a simple, consistent, and safe manner.

Acknowledgements: This post was developed in joint collaboration with Jim Miller

21 Jun 2018 4:53pm GMT