{"id":231716,"date":"2017-08-01T07:33:29","date_gmt":"2017-08-01T11:33:29","guid":{"rendered":"http:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/uncategorized\/ai-quickly-cooks-malware-that-av-software-cant-spot-the-register-the-register.php"},"modified":"2022-10-27T08:16:11","modified_gmt":"2022-10-27T12:16:11","slug":"ai-quickly-cooks-malware-that-av-software-cant-spot-the-register-the-register","status":"publish","type":"post","link":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/artificial-intelligence\/ai-quickly-cooks-malware-that-av-software-cant-spot-the-register-the-register.php","title":{"rendered":"AI quickly cooks malware that AV software can&#8217;t spot  The Register &#8211; The Register"},"content":{"rendered":"<p><p>    DEF CON Machine-learning tools    can create custom malware that defeats antivirus software.  <\/p>\n<p>    In a keynote demonstration at the DEF    CON hacking convention Hyrum Anderson, technical director    of data science at security shop Endgame, showed off research    that his company had done in adapting Elon Musks OpenAI    framework to the task of creating malware that security engines    cant spot.  <\/p>\n<p>    The system basically learns how to tweak malicious binaries so    that they can slip past antivirus tools and continue to work    once unpacked and executed. Changing small sequences of bytes    can fool AV engines, even ones that are also powered by    artificial intelligence, he said. Anderson cited research by    Google and others to show how changing just a few pixels in an    image can cause classification software to mistake a bus for an    ostrich.  <\/p>\n<p>    All machine learning models have blind spots, he said.    Depending on how much knowledge a hacker has they can be    convenient to exploit.  <\/p>\n<p>    So the team built a fairly simple mechanism to develop    weaponised code by making very small changes to malware and    firing these variants at an antivirus file scanner. By    monitoring the response from the engine they were able to make    lots of tiny tweaks that proved very effective at crafting    software nasties that could evade security sensors.  <\/p>\n<p>    The malware-tweaking machine-learning software was trained over    15 hours and 100,000 iterations, and then lobbed some samples    at an antivirus classifier. The attacking code was able to get    16 per cent of its customized samples past the security    systems defenses, we're told.  <\/p>\n<p>    This software-generation software will be online at the firms    Github page and Anderson    encouraged people to give it a try. No doubt security firms    will also be taking a long look at how this affects their    products in the future.   <\/p>\n<p>    Sponsored:     The Joy and Pain of Buying IT - Have Your Say  <\/p>\n<p><!-- Auto Generated --><\/p>\n<p>Excerpt from:<\/p>\n<p><a target=\"_blank\" rel=\"nofollow noopener\" href=\"https:\/\/www.theregister.co.uk\/2017\/07\/31\/ai_defeats_antivirus_software\/\" title=\"AI quickly cooks malware that AV software can't spot  The Register - The Register\">AI quickly cooks malware that AV software can't spot  The Register - The Register<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p> DEF CON Machine-learning tools can create custom malware that defeats antivirus software. In a keynote demonstration at the DEF CON hacking convention Hyrum Anderson, technical director of data science at security shop Endgame, showed off research that his company had done in adapting Elon Musks OpenAI framework to the task of creating malware that security engines cant spot.  <a href=\"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/artificial-intelligence\/ai-quickly-cooks-malware-that-av-software-cant-spot-the-register-the-register.php\">Continue reading <span class=\"meta-nav\">&rarr;<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"limit_modified_date":"","last_modified_date":"","_lmt_disableupdate":"","_lmt_disable":"","footnotes":""},"categories":[13],"tags":[],"class_list":["post-231716","post","type-post","status-publish","format-standard","hentry","category-artificial-intelligence"],"modified_by":"Danzig","_links":{"self":[{"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/posts\/231716"}],"collection":[{"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/comments?post=231716"}],"version-history":[{"count":0,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/posts\/231716\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/media?parent=231716"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/categories?post=231716"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.euvolution.com\/futurist-transhuman-news-blog\/wp-json\/wp\/v2\/tags?post=231716"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}