{"id":5419,"date":"2017-10-03T14:24:14","date_gmt":"2017-10-03T13:24:14","guid":{"rendered":"https:\/\/www.n-able.com\/?p=5419"},"modified":"2021-07-12T13:41:11","modified_gmt":"2021-07-12T12:41:11","slug":"using-machine-learning-create-smart-antivirus","status":"publish","type":"post","link":"https:\/\/www.n-able.com\/it\/blog\/using-machine-learning-create-smart-antivirus","title":{"rendered":"Using Machine Learning to Create a Smart Antivirus"},"content":{"rendered":"<p>One of the greatest challenges in the fight against malware is that the enemy keeps changing tactics. Malware developers, like antivirus (AV) vendors, are constantly updating their methods, code used, filenames, command and control (C&amp;C) locations, and more\u2014all in an effort to remain undetected. Traditional AV, and even next generation AV, all rely on historical signature and behavior data generated back in an AV vendor\u2019s lab, which can take hours to develop a signature that can be pushed out to every client.\u00a0 But with new springing up all the time,\u00a0<i>four hours is a lifetime<\/i>.<\/p>\n<p>Thinking back just a few months, the\u00a0<a class=\"ext\" href=\"https:\/\/en.wikipedia.org\/wiki\/WannaCry_ransomware_attack\" target=\"_blank\" rel=\"noopener noreferrer\">WannaCry<\/a>\u00a0ransomware hit over 200K machines worldwide. While that seems rather small given the number of machines in the world, it still makes the point that in today\u2019s climate of malware protection, we no longer have hours to respond.<\/p>\n<h3>Gathering Data Using Machine Learning<\/h3>\n<p><img loading=\"lazy\" decoding=\"async\" id=\"\" class=\"img-responsive\" title=\"\" src=\"https:\/\/www.n-able.com\/wp-content\/uploads\/blog\/2017\/10\/gathering%20data.jpg\" alt=\"gathering data.jpg\" width=\"421\" height=\"208\" align=\"left\" hspace=\"5\" data-entity-type=\"\" data-entity-uuid=\"\" \/>What\u2019s coming next is data gathering at the front lines using machine learning and millions of sample endpoints. The concept of obtaining information from large numbers of sources has been especially viable with the modern internet. We\u2019ve seen crowdsourcing, crowdfunding, and even\u00a0<a class=\"ext\" href=\"https:\/\/en.wikipedia.org\/wiki\/Crowdsolving\" target=\"_blank\" rel=\"noopener noreferrer\">crowdsolving<\/a>. And this concept has moved in recent years , applying to things like autonomous driving\u2014companies like Tesla<sup>\u00ae<\/sup>\u00a0leverage the data from all of its cars. In fact,\u00a0<a class=\"ext\" href=\"https:\/\/qz.com\/694520\/tesla-has-780-million-miles-of-driving-data-and-adds-another-million-every-10-hours\/\" target=\"_blank\" rel=\"noopener noreferrer\">last year, the number of miles of data was at 780 million and growing at a rate of a million miles every 10 hours.<\/a><\/p>\n<p>So, there\u2019s something really valuable about utilizing the data found across millions of sample endpoints in real time. And no greater opportunity exists than that of leveraging the experiences of millions of endpoints to build a\u00a0<a href=\"https:\/\/www.solarwindsmsp.com\/products\/rmm\/use-cases\/centrally-managed-antivirus\">\u201csmart\u201d antivirus<\/a>. As each endpoint interacts with malware, the specific actions, behaviors, methods of injection, code used, and more can all be documented and aggregated to develop a signature to be deployed to every other endpoint in the world.<\/p>\n<h3>The Rise of Smart Antivirus<\/h3>\n<p><img loading=\"lazy\" decoding=\"async\" id=\"\" class=\"img-responsive\" title=\"\" src=\"https:\/\/www.n-able.com\/wp-content\/uploads\/blog\/2017\/10\/smartantivirs.jpg\" alt=\"smartantivirs.jpg\" width=\"419\" height=\"207\" align=\"right\" hspace=\"5\" data-entity-type=\"\" data-entity-uuid=\"\" \/>Microsoft<sup>\u00ae<\/sup>\u00a0just\u00a0<a class=\"ext\" href=\"https:\/\/www.cnet.com\/news\/microsoft-build-smart-antivirus-using-400-million-computers-artificial-intelligence\/\" target=\"_blank\" rel=\"noopener noreferrer\">announced it plans to leverage 400 million endpoints<\/a>\u00a0as part of its development of Smart AV. It also plans on utilizing data from Azure<sup>\u00ae<\/sup>\u00a0and its Endpoint Protection product as part of this effort.<\/p>\n<p>Using\u00a0artificial intelligence\u00a0and machine learning, the process of identifying malware behaviors to detect the same malware on another endpoints can be completely automated. And, unlike humans, the process of machine learning implies a constant state of improvement in the identifying, definition, and detection of malware at a much faster and more accurate pace.<\/p>\n<p><em>Nick Cavalancia has over 20 years of enterprise IT experience and is an accomplished executive, consultant, trainer, speaker, and columnist. He has authored, co-authored and contributed to over a dozen books on Windows<b><sup>\u00ae<\/sup><\/b>, Active Directory<b><sup>\u00ae<\/sup><\/b>, Exchange<sup>\u2122<\/sup>\u00a0and other Microsoft technologies. Nick has also held executive positions at ScriptLogic<b><sup>\u00ae<\/sup><\/b>, SpectorSoft<b><sup>\u00ae<\/sup><\/b>\u00a0and Netwrix<b><sup>\u00ae<\/sup><\/b>\u00a0and now focuses on the evangelism of technology solutions.<\/em><\/p>\n<p><em>Follow Nick on Twitter<b><sup>\u00ae<\/sup><\/b>\u00a0at\u00a0<a class=\"ext\" href=\"https:\/\/twitter.com\/nickcavalancia\" target=\"_blank\" rel=\"noopener noreferrer\">@nickcavalancia<\/a><\/em><\/p>\n<p><a href=\"https:\/\/www.solarwindsmsp.com\/products\/risk-intelligence\" target=\"_blank\" rel=\"noopener\"><b>Click here<\/b><\/a><b>\u00a0to find out how SolarWinds<sup>\u00ae<\/sup><\/b><b>\u00a0<\/b><b>Risk Intelligence can help you protect your business.<\/b><\/p>\n<p>\u00a9 2017 SolarWinds MSP UK Ltd. All rights reserved.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>One of the greatest challenges in the fight against malware is that the enemy keeps changing tactics. Malware developers, like antivirus (AV) vendors, are constantly updating their methods, code used,&#8230;<\/p>\n","protected":false},"author":24,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"class_list":["post-5419","post","type-post","status-publish","format-standard","hentry","topic-security"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.2 (Yoast SEO v27.2) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Using Machine Learning to Create a Smart Antivirus - N-able<\/title>\n<meta name=\"description\" content=\"Read how you can use machine learning to identify and detect malware at a faster pace.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.n-able.com\/it\/blog\/using-machine-learning-create-smart-antivirus\" \/>\n<meta property=\"og:locale\" content=\"it_IT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Using Machine Learning to Create a Smart Antivirus - 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