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The Google Identity Platform is a system that allows you to sign in to applications and other services by using your Google account. Google Sign-In is one such method for providing your identity to the Google Identity Platform. Google Sign-In is available for Android applications and iOS applications, as well as for websites and other devices.

Users of Google Sign-In find that it integrates well with the Android platform, but iOS users (iPhone, iPad, etc.) do not have the same experience. The user experience when logging in to a Google account on an iOS application is not only more tedious than the Android experience, but it also conditions users to engage in behaviors that put their Google accounts at risk!

Application whitelisting is a useful defense against users running unapproved applications. Whether you're dealing with a malicious executable file that slips through email defenses, or you have a user that is attempting to run an application that your organization has not approved for use, application whitelisting can help prevent those activities from succeeding.

Some enterprises may deploy application whitelisting with the idea that it prevents malicious code from executing. But not all malicious code arrives in the form of a single executable application file. Many configurations of application whitelisting do not prevent malicious code from executing, though. In this blog post I explain how this is possible.

Recently, there has been a resurgence of malware that is spread via Microsoft Word macro capabilities. In 1999, CERT actually published an advisory about the Melissa virus, which leveraged macros to spread. We even published an FAQ about the Melissa virus that suggests to disable macros in Microsoft Office products.

Why is everything old new again? Reliability of the exploit is one reason, but the user interface of Microsoft Office is also to blame.

What does it mean to say that an indicator is exhibiting persistent behavior? This is a question that Timur, Angela, and I have been asking each other for the past couple of months. In this blog post, we show you the analytics that we believe identify persistent behavior and how that identification can be used to identify potential threats as well as help with network profiling.

As you may have read in a previous post, the CERT/CC has been actively researching vulnerabilities in the connected vehicles. When we began our research, it became clear that in the realm of cyber-physical systems, safety is king. For regulators, manufacturers, and the consumer, we all want (and expect!) the same thing: a safe vehicle to drive. But what does safety mean in the context of security? This is the precisely the question that the National Highway Transit Safety Administration (NHTSA) asked the public in its federal register notice.

One of my responsibilities on the Situational Awareness Analysis team is to create analytics for various purposes. For the past few weeks, I've been working on some anomaly detection analytics for hunting in the network flow traffic of common network services. I decided to start with a very simple approach using mean and standard deviation for a historical period to create a profile that I could compare against current volumes. To do this, I planned on binning network traffic by some length of time to find time periods with anomalous volumes. The question I then had to answer was, "How should I define the historical period?" In this post, I explain the process I used to answer that question.