Phishing, malicious hacking techniques to get folks unfortunately clicking malicious links, only a powerful new weapon. Black Hat, the researchers created a Twitter bot read your tweets and send you a message for the benefit of you-along with a shorter URL, resulting in hacktown.
Security company Baltimore ZeroFox do as a bot SNAP_R proof-of-concept for the next generation of phishing techniques, an explanation of the method in a paper released at the conference at the Black Hat security. It uses machine learning to churn through tweets a victim and their followers, then send an alarm message related to their interests. It uses the phrase to identify high value targets based on social commitment, like the followers and retweets, and measures the success of bots by monitoring click-through rates. In summary, the researchers claim it is "the world's first automated end to end online fraud Catholic campaign generator for Twitter."
The SNAP_R ZeroFox created as an assessment tool education and security: like many companies, they are often hired to attack customers using cutting-edge methods of hackers actually want to use. Machine learning is often used defensively, so this method is one of the first to turn it around to target victims at the "spear" of security against fraud.
Since the links in tweets will automatically be shortened, the user is almost impossible to sniff out the shifty destination URL, so spotting poor grammar or inappropriate content is the fastest way to suss out malicious intent. Catering is a smart way to keep from arousing suspicion and final victim get them to click on the link they will be too cautious to otherwise. British intelligence agency GCHQ exploitation of this technique as it uses its own URL shortener innocuous to track activity and agitation in favor of a revolution during the Arab Spring and Iran stand thick. ZeroFox incredible trick a victim into 2/3 clicking the links, higher than the success rate to 15 percent for normal forging method, is evidence of a serious vulnerability in the behavior of users of social networking security.
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