However, such technologies are quite resource-intensive, meaning that doing all of the necessary work right on a user’s device can reduce performance and battery life. That’s where machine-learning technologies come in and can save significant amounts of time and resources. To pick through that number of malware samples manually would require an ever-expanding team, and, more important, would take a lot of time (during which users could pick up new malware). We know of more than 360,000 unique versions of Fttkit, and the figure continues to grow. It works by using obfuscation to trick security solutions and then installing other malware, usually banking Trojans. The creators of this Trojan dropper call it an “automated service to protect Android apps,” but it actually helps fellow malware writers evade antivirus detection. In 2012, we were detecting an average of 467,515 samples per month, our team of mobile threat analysts had grown to four, and heuristic analysis and statistical methods supplemented the signature-based engine - but that wasn’t enough.įttkit provides a striking example of how mobile threats have evolved. The signature-based engine still managed, but far more time was spent on analyzing the malicious files.Īs the operating system’s popularity soared, the amount of new Android malware swelled. Very soon, however, the number of threats snowballed, and by 2010 our monthly detections of new Android malware had shot up to 20,000. In 2009, we detected three new samples of Android malware per month on average
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