New directions in traffic measurement and accounting
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
IPTPS '01 Revised Papers from the First International Workshop on Peer-to-Peer Systems
What's hot and what's not: tracking most frequent items dynamically
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
An improved data stream summary: the count-min sketch and its applications
Journal of Algorithms
Detectives: detecting coalition hit inflation attacks in advertising networks streams
Proceedings of the 16th international conference on World Wide Web
On Hit Inflation Techniques and Detection in Streams of Web Advertising Networks
ICDCS '07 Proceedings of the 27th International Conference on Distributed Computing Systems
HotBots'07 Proceedings of the first conference on First Workshop on Hot Topics in Understanding Botnets
Combating click fraud via premium clicks
SS'07 Proceedings of 16th USENIX Security Symposium on USENIX Security Symposium
SLEUTH: Single-pubLisher attack dEtection Using correlaTion Hunting
Proceedings of the VLDB Endowment
Letizia: an agent that assists web browsing
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
SBotMiner: large scale search bot detection
Proceedings of the third ACM international conference on Web search and data mining
Fighting online click-fraud using bluff ads
ACM SIGCOMM Computer Communication Review
CAPTCHA: using hard AI problems for security
EUROCRYPT'03 Proceedings of the 22nd international conference on Theory and applications of cryptographic techniques
Got traffic?: an evaluation of click traffic providers
Proceedings of the 2011 Joint WICOW/AIRWeb Workshop on Web Quality
What's clicking what? techniques and innovations of today's clickbots
DIMVA'11 Proceedings of the 8th international conference on Detection of intrusions and malware, and vulnerability assessment
Understanding fraudulent activities in online ad exchanges
Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
Click fraud resistant methods for learning click-through rates
WINE'05 Proceedings of the First international conference on Internet and Network Economics
User-Driven Access Control: Rethinking Permission Granting in Modern Operating Systems
SP '12 Proceedings of the 2012 IEEE Symposium on Security and Privacy
Dissecting ghost clicks: ad fraud via misdirected human clicks
Proceedings of the 28th Annual Computer Security Applications Conference
CAMEO: a middleware for mobile advertisement delivery
Proceeding of the 11th annual international conference on Mobile systems, applications, and services
Verifiable auctions for online ad exchanges
Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM
Best paper -- Follow the money: understanding economics of online aggregation and advertising
Proceedings of the 2013 conference on Internet measurement conference
ViceROI: catching click-spam in search ad networks
Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security
Challenges of keyword-based location disclosure
Proceedings of the 12th ACM workshop on Workshop on privacy in the electronic society
Impression fraud in online advertising via pay-per-view networks
SEC'13 Proceedings of the 22nd USENIX conference on Security
Adtributor: revenue debugging in advertising systems
NSDI'14 Proceedings of the 11th USENIX Conference on Networked Systems Design and Implementation
DECAF: detecting and characterizing ad fraud in mobile apps
NSDI'14 Proceedings of the 11th USENIX Conference on Networked Systems Design and Implementation
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Advertising plays a vital role in supporting free websites and smartphone apps. Click-spam, i.e., fraudulent or invalid clicks on online ads where the user has no actual interest in the advertiser's site, results in advertising revenue being misappropriated by click-spammers. While ad networks take active measures to block click-spam today, the effectiveness of these measures is largely unknown. Moreover, advertisers and third parties have no way of independently estimating or defending against click-spam. In this paper, we take the first systematic look at click-spam. We propose the first methodology for advertisers to independently measure click-spam rates on their ads. We also develop an automated methodology for ad networks to proactively detect different simultaneous click-spam attacks. We validate both methodologies using data from major ad networks. We then conduct a large-scale measurement study of click-spam across ten major ad networks and four types of ads. In the process, we identify and perform in-depth analysis on seven ongoing click-spam attacks not blocked by major ad networks at the time of this writing. Our findings highlight the severity of the click-spam problem, especially for mobile ads.