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Erschienen in:
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2021 | OriginalPaper | Buchkapitel

68. Clickedroid: A Methodology Based on Heuristic Approach to Detect Mobile Ad-Click Frauds

verfasst von : Pankaj Kumar Keserwani, Vedant Jha, Mahesh Chandra Govil, Emmanuel S. Pilli

Erschienen in: Proceedings of the International Conference on Paradigms of Computing, Communication and Data Sciences

Verlag: Springer Singapore

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Abstract

A number of apps are available in app stores for different types of online services and are growing with the use of mobile devices in the public. Advertisements on mobile through apps become a popular trend in online businesses. Ad frauds is a major concern for the advertising industry, and the most affected ones are the advertisers. The advertiser pays revenue to the ad network for each click on his ad. If ghost clicks are not captured as fraud or invalid clicks, then the advertiser has to pay to the ad network for ghost clicks also. Hence, return on investment (ROI) of the advertiser becomes very low and not achieving the goal as expected. Detecting fraud clicks on ads is very important to achieve the expected goal of the advertiser. The proposed methodology follows a heuristic approach to detect mobile ad-click frauds. A click fraud detection algorithm has been developed and implemented on the cloud server. The algorithm is triggered for each entry of the ad click to detect the click belongs to fraud or not fraud. The obtained results have been verified by popular machine learning (ML) algorithms namely as support vector machine (SVM), random forest, and k-nearest neighbors (k-NN) with the accuracies of 91%, 84% and 85%, respectively.

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Literatur
1.
2.
Alrwais SA, Gerber A, Dunn CW, Spatscheck O, Gupta M, Osterweil E (2012) Dissecting ghost clicks: ad fraud via misdirected human clicks. In: Proceedings of the 28th annual computer security applications conference. ACM, pp 21–30
3.
Arp D, Spreitzenbarth M, Hubner M, Gascon H, Rieck K, Siemens C (2014) Drebin: effective and explainable detection of android malware in your pocket. In: Ndss, vol 14, pp 23–26
4.
Badhe A (2017) Click fraud detection in mobile ads served in programmatic inventory. Neural Netw Mach Learn 1(1):1
5.
Blizard T, Livic N (2012) Click-fraud monetizing malware: a survey and case study. In: 2012 7th international conference on malicious and unwanted software. IEEE, pp 67–72
6.
Chen Y, Kintis P, Antonakakis M, Nadji Y, Dagon D, Farrell M (2017) Measuringlower bounds of the financial abuse to online advertisers: a four year case study of the TDSS/TDL4 Botnet. Comput Secur 67:164–180CrossRef
7.
Crussell J, Stevens R, Chen H (2014) Madfraud: investigating ad fraud in android applications. In: Proceedings of the 12th annual international conference on mobile systems, applications, and services. ACM, pp 123–134
8.
Daswani N, Stoppelman M (2007) The anatomy of Clickbot. A. In: Proceedings of the first conference on first workshop on hot topics in understanding botnets. USENIX Association, p 11
9.
Dave V, Guha S, Zhang Y (2012) Measuring and fingerprinting click-spam in ad networks. In: Proceedings of the ACM SIGCOMM 2012 conference on applications, technologies, architectures, and protocols for computer communication. ACM, pp 175–186
10.
Dave V, Guha S, Zhang Y (2013) Viceroi: catching click-spam in search ad networks. In: Proceedings of the 2013 ACM SIGSAC conference on computer & communications security. ACM, pp 765–776
11.
Dong F, Wang H, Li L, Guo Y, Bissyand’e TF, Liu T, Xu G, Klein J (2018) Frauddroid: automated ad fraud detection for android apps. In: Proceedings of the 2018 26th ACM joint meeting on European software engineering conference and symposium on the foundations of software engineering. ACM, pp 257–268
12.
13.
Haider CMR, Iqbal A, Rahman AH, Rahman MS (2018) An ensemble learning based approach for impression fraud detection in mobile advertising. J Netw Comput Appl 112:126–141CrossRef
14.
He B, Xu H, Jin L, GuoG, Chen Y, Weng G (2018) An investigation into android in-app ad practice: implications for app developers. In: IEEE INFOCOM 2018 conference on computer communications. IEEE, pp 2465–2473
15.
Iqbal MS, Zulkernine M, Jaafar F, Gu Y (2016) Fcfraud: fighting click-fraud from the user side. In: 2016 IEEE 17th international symposium on high assurance systems engineering (HASE). IEEE, pp 157–164
16.
Jacob G, Kirda E, Kruegel C, Vigna G (2012) {PUBCRAWL}: protecting users and businesses from crawlers. In: Presented as part of the 21st {USENIX} security symposium ({USENIX} security 12), pp 507–522
17.
Khanchi S, Zincir-Heywood N, Heywood M (2018) Streaming botnet traffic analysis using bio-inspired active learning. In: NOMS 2018—2018 IEEE/IFIP network operations and management symposium. IEEE, pp 1–6
18.
Lashkari AH, Kadir AFA, Gonzalez H, Mbah KF, Ghorbani AA (2017) Towards a network-based framework for android malware detection and characterization. In: 2017 15th annual conference on privacy, security and trust (PST). IEEE, pp 233–239
19.
Li Z, Zhang K, Xie Y, Yu F, Wang X (2012) Knowing your enemy: understanding and detecting malicious web advertising. In: Proceedings of the 2012 ACM conference on computer and communications security. ACM, pp 674–686
20.
Liu B, Nath S, Govindan R, Liu J (2014) {DECAF}: detecting and characterizing ad fraud in mobile apps. In: 11th {USENIX} symposium on networked systems design and implementation ({NSDI} 14), pp 57–70
21.
Metwally A, Agrawal D, El Abbad A, Zheng Q (2007) On hit inflation techniques and detection in streams of web advertising networks. In: 27th international conference on distributed computing systems (ICDCS’07). IEEE, p 52
22.
Metwally A, Agrawal D, El Abbadi A: Detectives: detecting coalition hit inflation attacks in advertising networks streams. In: Proceedings of the 16th international conference on world wide web. ACM, pp 241–250 (2007)
23.
Metwally A, Emekçi F, Agrawal D, El Abbadi A (2008) Sleuth: single-publisher attack detection using correlation hunting. Proc VLDB Endow 1(2):1217–1228
24.
Miller B, Pearce P, Grier C, Kreibich C, Paxson V (2011) (2011) What’s clicking what? Techniques and innovations of today’s clickbots. In: International conference on detection of intrusions and malware, and vulnerability assessment. Springer, pp 164–183
25.
Mouawi R, Awad M, Chehab A, El Hajj IH, Kayssi A (2018) Towards a machine-learning approach for detecting click fraud in mobile advertizing. In: 2018 international conference on innovations in information technology (IIT). IEEE, pp 88–92
26.
27.
Pearce P, Dave V, Grier C, Levchenko K, Guha S, McCoy D, Paxson V, Savage S, Voelker GM (2014) Characterizing large-scale click fraud in zeroaccess. In: Proceedings of the 2014 ACM SIGSAC conference on computer and communications security. ACM, pp 141–152
28.
Stone-Gross B, Stevens R, Zarras A, Kemmerer R, Kruegel C, Vigna G (2011) Understanding fraudulent activities in online and exchanges. In: Proceedings of the 2011 ACM SIGCOMM conference on internet measurement conference. ACM, pp 279–294
29.
Tuzhilin A (2006) The lanes gifts v. google report. Official Google Blog: findings on invalid clicks, posted, pp 1–47
30.
Xu H, Liu D, Koehl A, Wang H, Stavrou A (2014) Click fraud detection on the advertiser side. In: European symposium on research in computer security. Springer, pp 419–438
31.
Yu F, Xie Y, Ke Q (2010) Sbotminer: large scale search bot detection. In: Proceedings of the third ACM international conference on web search and data mining. ACM, pp 421–430
32.
Zarras A, Kapravelos A, Stringhini G, Holz T, Kruegel C, Vigna G (2014) The dark alleys of Madison avenue: understanding malicious advertisements. In: Proceedings of the 2014 conference on internet measurement conference. ACM, pp 373–380
33.
Zhang L, Guan Y (2008) Detecting click fraud in pay-per-click streams of online advertising networks. In: The 28th international conference on distributed computing systems. IEEE, pp 77–84
Metadaten
Titel
Clickedroid: A Methodology Based on Heuristic Approach to Detect Mobile Ad-Click Frauds
verfasst von
Pankaj Kumar Keserwani
Vedant Jha
Mahesh Chandra Govil
Emmanuel S. Pilli
Copyright-Jahr
2021
Verlag
Springer Singapore
Buch
Proceedings of the International Conference on Paradigms of Computing, Communication and Data Sciences

Print ISBN: 978-981-15-7532-7

Electronic ISBN: 978-981-15-7533-4

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-981-15-7533-4_68

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