MEASURING AND FINGERPRINTING CLICK-SPAM IN AD NETWORKS
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1 MEASURING AND FINGERPRINTING CLICK-SPAM IN AD NETWORKS Vacha Dave *, Saikat Guha and Yin Zhang * * The University of Texas at Austin Microsoft Research India
2 Internet Advertising Today 2 Online advertising is a 31 billion dollar industry * Publishers can monetize traffic Blogs, News sites, Syndicated search engines Revenue for content development Pay-per-click advertising Advertisers pay per-click to ad networks Publishers make a 70% cut on each click on their site *Based on Interactive Advertising Bureau Report, a consortium of Online Ad Networks
3 Click-spam in Ad Networks 3 Click-spam Fraudulent or invalid clicks Users delivered to the advertiser site are uninterested Advertisers lose money Possible Motives Malicious advertisers (or other parties) Deplete competitor s ad budgets Isolated cases Publishers/Syndicated search engines Make money on every click that happens on their site
4 4 Click-spam in Ad Networks
5 Mobile Devices and Ads 5 Mobile game Squish the ant to win the game Ads placed close to where user is expected to click Ant Ad
6 Click-spam Detection 6 No ground truth Almost impossible to know if particular click is genuine Need to guess the intent of user Different levels of click-spam in different segments Aggregate numbers are meaningless Ad networks aren t transparent Security by obscurity Real problem lot of work needed Researchers lack real attack data
7 Contributions First method to independently estimate click-spam As an advertiser For specific keywords Test across ten ad networks Search, contextual, social and mobile ad networks Show that click-spam is a problem For Mobile and Social ad networks Discover five classes of sophisticated attacks Why simple heuristics don t work Release data for researchers
8 Estimating click-spam Approach 8 Hard to classify any single click Estimate fraction of click-spam Designed Bayesian estimation framework Uses only advertiser-measurable quantities Cancel out unmeasurable quantities By relating different mixes of good and bad traffic
9 Estimating Click-spam Main Idea How many? Equate ratios of buyers to non-spammers Both non-spammers and spammers click ads A fraction of non-spammers buy Black box Both non-spammers and spammers click ads Lose spammers and some non-spammers Some non-spammers buy
10 Dissecting Black box Hurdles Hurdle Spammers and non-spammers Extra click required to view click on an ad site Different hurdles have different hardness 5 sec wait, Click to continue Send only a fraction of traffic through hurdles 10 Some spammers and Non-spammers see the content To minimize impact on user experience Perfect hurdle would block all spam In reality, some spammers get through (False Negatives)
11 Dissecting Black box - Bluff Ads[1] Bluff Ads Junk ad text with normal keywords, same targeting Normal users unlikely to click Bluff Normal Hurdle Spammers and curious users click on an ad 11 Some spammers and users may see the content [1] Fighting online click fraud using bluff ads [CCR 2010]
12 Dissecting Black box - Bluff Ads[1] Maximum False Negative rate known for each hurdle Can be subtracted out Hurdle Spammers and curious users click on an ad 12 Some spammers and users may see the content [1] Fighting online click fraud using bluff ads [CCR 2010]
13 Testing Ad Networks 13 Sign up as advertisers for ten ad networks 240 Ads Keywords: Celebrity, Yoga, Lawnmower Hurdles: Click to continue, 5 sec wait 50,000 Clicks Search, Contextual, Mobile and Social Google, Bing, AdMob, InMobi, Facebook and others 30,000 bluff ad clicks Cost: $1500
14 Uh-oh. How do we validate? 14 No ground truth! Compare against search ads on Google and Bing
15 Results Validation using search ads 15 Fraction valid (norm.) Valid Traffic Fraction (Normalized) Ad Network s Estimate celebrity yoga lawnmower Our Estimate A B C Ad Networks Clicks charged are close to the estimated valid clicks
16 Results Validation using search ads 16 Fraction valid (norm.) Valid Traffic Fraction (Normalized) Ad Network s Estimate celebrity yoga lawnmower Our Estimate A B C Ad Networks Clicks charged are close to the estimated valid clicks
17 Results Estimating Mobile Spam 17 Fraction valid (norm.) Valid Traffic Fraction (Normalized) 1 Ad Network s Estimate Our Estimate A B C D Most mobile ad networks fail to fight click-spam
18 Results Estimating Contextual Spam 18 Fraction valid (norm.) Valid Traffic Fraction (Normalized) Ad Network s Estimate Our Estimate celebrity yoga lawnmower A B C All networks seem to be underestimating the amount of spam
19 Where is click-spam coming from? 19 Analyze bluff ad clicks Publishers: Strong motive Instead of clicks/users Manual Investigation Challenge: Scale publishers, 30,000 Clicks Identical sites! Cluster on cosine similarity Feature vector WHOIS, IP Address/Subnet, HTTP parameters
20 20 Publisher Website: Cosine Similarity:
21 Publisher Website: Cosine Similarity: 21
22 Publishers creating sybil nodes to beat ad network thresholds Publisher Website: Cosine Similarity: 22
23 Case Study 1 - Malware driven click fraud Malware infected PC (BOTID=50018&SEARCH-ENGINE-NAME&q=books) Base64 Jane searches for books Malware infected PC Publisher List Botmaster generates list of publishers 23 Jane clicks on a search result Publisher URL All background traffic Jane sees nothing AD URL Auto-Redirect (Fraud)
24 Case Study 1 - Malware driven Click fraud 24 Responsible Malware: TDL4 Validation: Run malware in VM Can intercept and redirect all browser requests Browser specific filtering doesn t work Only 1 click per IP address per day Threshold based filtering doesn t work Mimics real user behavior Timing analysis doesn t work
25 ClickSpam and Arbitrage 25 Polished forum sites Bluff ad clicks on ad network X No malware reports Not popular Where do they get traffic? Copied No ads on the site!!
26 Click-spam and Arbitrage 26 Advertiser on network Y Site pays $ to Y Creates ads Publisher on network X Page now has only ads No questions or answers Confusing users into clicks Site earns $$$$ from X Ads
27 Click-spam and Arbitrage 27 Site pays $ to Y Tricking real users into clicking Bot detection techniques don t apply Site earns $$$$ from X Ads
28 Case Study3 - Click Fraud using Parked Domains Go to icicibank.com Jane mistypes icicbank.com in her browser and presses enter Parked Domain Auto-Redirect 28 AD URL Auto-Redirect (Fraud) Jane ends up on icicibank.com icicibank.com pays for a click
29 Case Study3 - Click Fraud using Parked Domains 29 41of 400 parked domains hosted on a single IP Misspellings of common websites: icicbank.com, nsdi.com Auto-redirect depends on Jane s geo-location IP hosts 500,000 such domains User mistypes a URL Advertiser must pay! User behavior indistinguishable from normal traffic Naively using conversions don t work
30 Case Study 4 Mobile click-spam 30 Indian Mobile ad network Supplies WAP Ads to a group of WAP porn sites Ad links indistinguishable from porn video links Gaming apps Place ads close to where users are expected to click Ant-Smasher, Milk-the-Cow, and 50 others
31 Malware 31 Sybil Nodes
32 Malware Arbitrage 32 Sybil Nodes
33 Malware Arbitrage Parked 33 Sybil Nodes
34 Malware Arbitrage Parked WAP 34 Sybil Nodes
35 Summary Click-spam remains a problem First way of estimating click-spam Independently As an advertiser, for a set of keywords Extensive validation Sophisticated click-spam attacks today Sybil sites Malware mimics user behavior Social engineering attacks and others Dataset is available for download All clicks (minimally sanitized)
36 36 Thanks! Data at:
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