{"id":1080,"date":"2019-04-24T15:04:54","date_gmt":"2019-04-24T15:04:54","guid":{"rendered":"https:\/\/persona.ly\/blog\/?p=1080"},"modified":"2022-12-01T14:20:18","modified_gmt":"2022-12-01T14:20:18","slug":"battling-publisher-fraud","status":"publish","type":"post","link":"https:\/\/persona.ly\/blog\/2019\/04\/battling-publisher-fraud\/","title":{"rendered":"Battling Publisher Fraud"},"content":{"rendered":"\n<p><span style=\"font-weight: 400;\"><a href=\"https:\/\/persona.ly\/blog\/2019\/01\/market-research-fraud-distributed-survey-farms-exposed\/\">Our latest fraud case study<\/a> focused on our market research platform and uncovered how a single source systematically exercised fraudulent activity at scale. This follow-up article will focus on how we combat publisher fraud while running programmatic performance-based campaigns via OpenRTB. <\/span><\/p>\n\n\n\n<p><span style=\"font-weight: 400;\">The topic of <a href=\"https:\/\/persona.ly\/blog\/2022\/11\/the-vicious-cycle-of-click-spamming\/\">fraud <\/a>always occupies anyone involved in the mobile advertising world &#8211; as the ways to prevent it improve, new fraud tactics evolve and the cycle, unfortunately, continues. Moving into programmatic buying brought with it the hope that with all the data that\u2019s available in a single ad request and all the possibilities that machine learning algorithms have to offer, fraud prevention will become easier, but as always, fraudulent activity evolves as its prevention tools improve.<\/span><\/p>\n\n\n\n<h2><strong>Impression Level Fraud<\/strong><\/h2>\n\n\n\n<p><span style=\"font-weight: 400;\">Programmatic buying via OpenRTB allows for unparalleled levels of transparency for advertisers &#8211; it enables them to see exactly where the installs came from and track all the metrics at the placement bundle level (i.e. the apps where the ads were displayed). <\/span><\/p>\n\n\n\n<p><span style=\"font-weight: 400;\">When offering a performance-based model from day 1, as Persona.ly does, it\u2019s inherently a low to a non-risk campaign, since most fraud in the programmatic world is the generation of fake impressions by malicious publishers (which get paid for impressions and have no incentive to fake installs&#8230; <em>yet<\/em> \ud83d\ude00 ) which means the advertisers not only get all of the information they need, they\u2019re also guaranteed essentially fraud-free user acquisition campaigns.<\/span><\/p>\n\n\n\n<p><span style=\"font-weight: 400;\">While this is great news for the advertises, <em>performance-based DSPs aren\u2019t as fortunate<\/em>, since they need to fend for themselves and ensure they don\u2019t spend on manufactured impressions.<\/span><\/p>\n\n\n\n<h2><strong>What Publisher Fraud Looks Like<\/strong><\/h2>\n\n\n\n<p><span style=\"font-weight: 400;\">In the past couple of years, anti-fraud providers have started distinguishing between two different types of fraudulent traffic- <\/span><\/p>\n\n\n\n<ul>\n<li><span style=\"font-weight: 400;\"><strong>General Invalid Traffic (GIVT)<\/strong> &#8211; which can be relatively easy to detect by using lists and standardized checks. One example that can be rather easily identified is data-center traffic.<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\"><strong>Sophisticated Invalid Traffic (SIVT)<\/strong> &#8211; more complex methods that require machine learning and sometimes human intervention to detect. These are cases where publishers go out of their way to try and emulate real user behavior.<\/span><\/li>\n<\/ul>\n\n\n\n<p><span style=\"font-weight: 400;\">In reality, GIVT rarely slips under the radar, and detecting SIVT consistently, as it evolves is a different game entirely, mostly because the fraudsters have a say in the rules of said game.<\/span><\/p>\n\n\n\n<h2><strong>How We Deal With the Mundane<\/strong><\/h2>\n\n\n\n<ul>\n<li><span style=\"font-weight: 400;\">As a rule, we don\u2018t bid on incoming requests that don\u2019t include an IDFA\/GAID, ensuring we only bid on in-app traffic, and defacto eliminating a significant portion of GIVT fraud.<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">Using a <strong>combination of device recognition data enrichment<\/strong> (like 51Degrees &#8211; <a href=\"https:\/\/persona.ly\/blog\/2019\/03\/51degrees-device-detection-boosts-persona-lys-performance-by-15\/\">click here<\/a> to read a press release detailing some of the uses we have for their data) to build a device \u2018risk score\u2019 &#8211; allowing us to create a database of safe devices and focus our bidding on them.<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">Using <strong>different IP validation tools<\/strong> like <a href=\"https:\/\/www.maxmind.com\/en\/home\">MaxMind<\/a> to ensure the reported location in the requests checks out and that it\u2019s not coming from data-centers.<\/span><\/li>\n<\/ul>\n\n\n\n<p><span style=\"font-weight: 400;\">That\u2019s not all, but sharing the rest can make the mundane become unusual- and when it comes to battling fraud, we have our fair share of unusual and unexpected behavior.<\/span><\/p>\n\n\n\n<h2><span style=\"font-weight: 400;\"><strong>How We Deal With the Uncanny<\/strong> <\/span><\/h2>\n\n\n\n<p><span style=\"font-weight: 400;\">SIVT has many forms, and we have many different ways of detecting it. We won&#8217;t divulge all of them in a case study for obvious reasons, but we will share some specific cases our data science team picked up a while back.<\/span><\/p>\n\n\n\n<p><span style=\"font-weight: 400;\">Fraudsters are undoubtedly creative in their attempts to cover their tracks, but they still make mistakes. Earlier this year, following the decreased performance of one of the exchanges we are integrated with, our data science team started digging deeper into the requests from the said exchange during the 14-17 of February. <\/span><\/p>\n\n\n\n<p><span style=\"font-weight: 400;\">Our team then found that an abnormal amount of new devices started incoming from the exchange. Further investigation into these devices uncovered an alarming amount of suspiciously similar GAID strings.<\/span><\/p>\n\n\n\n<p><span style=\"font-weight: 400;\">As seen in the table below &#8211; we saw requests from dozens of different devices, with almost exactly the same sub-string (the fraudster bothered to only change 4 characters from the 32 character strings):<\/span><\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><a href=\"https:\/\/persona.ly\/blog\/wp-content\/uploads\/2019\/04\/Unique-devices.png\"><img decoding=\"async\" loading=\"lazy\" width=\"498\" height=\"439\" src=\"https:\/\/persona.ly\/blog\/wp-content\/uploads\/2019\/04\/Unique-devices.png\" alt=\"A list of request with the same sub-string\" class=\"wp-image-1086\"\/><\/a><figcaption class=\"wp-element-caption\"> <em>GAIDs are randomly generated by the operating system, and it\u2019s highly unlikely to see ones <\/em>so <em>similar in requests from the same apps.<\/em> <\/figcaption><\/figure><\/div>\n\n\n<p><span style=\"font-weight: 400;\">Two more alarming findings came from the additional analysis:<\/span><\/p>\n\n\n\n<ul>\n<li><span style=\"font-weight: 400;\">All of the devices with the strings containing the repeating substrings completely disappeared from the system after the 17th &#8211; we didn\u2019t see any additional requests from them.<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">When reviewing specific device id\u2019s, we noticed some pretty\u2026 well, impossible user behavior. As seen below, <strong>we saw four requests from the same device, from four different states, in the span of 12 hours<\/strong>:<\/span><\/li>\n<\/ul>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter is-resized\"><a href=\"https:\/\/persona.ly\/blog\/wp-content\/uploads\/2019\/04\/Python2.png\"><img decoding=\"async\" loading=\"lazy\" src=\"https:\/\/persona.ly\/blog\/wp-content\/uploads\/2019\/04\/Python2.png\" alt=\"four requests from the same device, from four different states, in the span of 12 hours: NY, IL, NV, CT\" class=\"wp-image-1092\" width=\"559\" height=\"155\"\/><\/a><figcaption class=\"wp-element-caption\"><em>The list of the four states<\/em><\/figcaption><\/figure><\/div>\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter\"><a href=\"https:\/\/persona.ly\/blog\/wp-content\/uploads\/2019\/04\/MAP.png\"><img decoding=\"async\" loading=\"lazy\" width=\"650\" height=\"462\" src=\"https:\/\/persona.ly\/blog\/wp-content\/uploads\/2019\/04\/MAP.png\" alt=\"A map showing the unrealistic user journey to cover all 4 states in the span of 12 hours \" class=\"wp-image-1088\"\/><\/a><figcaption class=\"wp-element-caption\"> <em>Unless we accidentally found Iron Man\u2019s GAID, these are definitely manufactured requests.<\/em> <\/figcaption><\/figure><\/div>\n\n\n<p><span style=\"font-weight: 400;\">In reality, battling fraud is a significant, interesting, and challenging part of our daily routine. Besides helping our performance, it also helps us learn more about authentic user behavior, which is why we invest plenty of resources into developing machine learning-based detection tools as well as manual deep-dives by our data science and analytics teams.<\/span><\/p>\n\n\n\n<p><em><span style=\"font-weight: 400;\">Our team is exhibiting at MAU in Las Vegas next week &#8211; If you want to learn more about our fraud prevention abilities and our programmatic user acquisition services, drop by booth #313, or <a href=\"https:\/\/persona.ly\/contact\">click here<\/a> to set up a meeting with our team.<\/span><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Our latest fraud case study focused on our market research platform and uncovered how a single source systematically exercised fraudulent activity at scale. This follow-up article will focus on how we combat publisher fraud while running programmatic performance-based campaigns via OpenRTB. The topic of fraud always occupies anyone involved in the mobile advertising world &ndash; &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/persona.ly\/blog\/2019\/04\/battling-publisher-fraud\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Battling Publisher Fraud&#8221;<\/span><\/a><\/p>\n","protected":false},"author":8,"featured_media":1084,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[23,6,24],"tags":[276,278],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v19.13 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Battling Publisher Fraud | Persona.ly - The Weekly Retention<\/title>\n<meta name=\"description\" content=\"Our latest fraud case study focused on our market research platform and uncovered how a single source systematically exercised fraudulent activity at\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/persona.ly\/blog\/2019\/04\/battling-publisher-fraud\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Battling Publisher Fraud | Persona.ly - 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