DNA-inspired online behavioral modeling and its application to spambot detection

We propose a strikingly novel, simple, and effective approach to model online user behavior: we extract and analyze digital DNA sequences from user online actions and we use Twitter as a benchmark to test our proposal. We obtain an incisive and compact DNA-inspired characterization of user actions. Then, we apply standard DNA analysis techniques to discriminate between genuine and spambot accounts on Twitter. An experimental campaign supports our proposal, showing its effectiveness and viability. To the best of our knowledge, we are the first ones to identify and adapt DNA-inspired techniques to online user behavioral modeling. While Twitter spambot detection is a specific use case on a specific social media, our proposed methodology is platform and technology agnostic, hence paving the way for diverse behavioral characterization tasks

Detecting Social Spamming on Facebook Platform

Tänapäeval toimub väga suur osa kommunikatsioonist elektroonilistes suhtlusvõrgustikes. Ühest küljest lihtsustab see omavahelist suhtlemist ja uudiste levimist, teisest küljest loob see ideaalse pinnase sotsiaalse rämpsposti levikuks. Rohkem kui kahe miljardi kasutajaga Facebooki platvorm on hetkel rämpsposti levitajate üks põhilisi sihtmärke. Platvormi kasutajad puutuvad igapäevaselt kokku ohtude ja ebameeldivustega nagu pahavara levitavad lingid, vulgaarsused, vihakõned, kättemaksuks levitatav porno ja muu. Kuigi uurijad on esitanud erinevaid tehnikaid sotsiaalmeedias rämpspostituste vähendamiseks, on neid rakendatud eelkõige Twitteri platvormil ja vaid vähesed on seda teinud Facebookis. Pidevalt arenevate rämpspostitusmeetoditega võitlemiseks tuleb välja töötada järjest uusi rämpsposti avastamise viise. Käesolev magistritöö keskendub Facebook platvormile, kuhu on lõputöö raames paigutatud kümme „meepurki” (ingl honeypot), mille abil määratakse kindlaks väljakutsed rämpsposti tuvastamisel, et pakkuda tõhusamaid lahendusi. Kasutades kõiki sisendeid, kaasa arvatud varem mujal sotsiaalmeedias testitud meetodid ja informatsioon „meepurkidest”, luuakse andmekaeve ja masinõppe meetoditele tuginedes klassifikaator, mis suudab eristada rämpspostitaja profiili tavakasutaja profiilist. Nimetatu saavutamiseks vaadeldakse esmalt peamisi väljakutseid ja piiranguid rämpsposti tuvastamisel ning esitletakse varasemalt tehtud uuringuid koos tulemustega. Seejärel kirjeldatakse rakenduslikku protsessi, alustades „meepurgi” ehitusest, andmete kogumisest ja ettevalmistamisest kuni klassifikaatori ehitamiseni. Lõpuks esitatakse „meepurkidelt” saadud vaatlusandmed koos klassifikaatori tulemustega ning võrreldakse neid uurimistöödega teiste sotsiaalmeedia platvormide kohta. Selle lõputöö peamine panus on klassifikaator, mis suudab eristada Facebooki kasutaja profiilid spämmerite omast. Selle lõputöö originaalsus seisneb eesmärgis avastada erinevat sotsiaalset spämmi, mitte ainult pahavara levitajaid vaid ka neid, kes levitavad roppust, massiliselt sõnumeid, heakskiitmata sisu jne.OSNs (Online Social Networks) are dominating the human interaction nowadays, easing the communication and spreading of news on one hand and providing a global fertile soil to grow all different kinds of social spamming, on the other. Facebook platform, with its 2 billions current active users, is currently on the top of the spammers' targets. Its users are facing different kind of social threats everyday, including malicious links, profanity, hate speech, revenge porn and others. Although many researchers have presented their different techniques to defeat spam on social media, specially on Twitter platform, very few have targeted Facebook's.To fight the continuously evolving spam techniques, we have to constantly develop and enhance the spam detection methods. This research digs deeper in the Facebook platform, through 10 implemented honeypots, to state the challenges that slow the spam detection process, and ways to overcome it. Using all the given inputs, including the previous techniques tested on other social medias along with observations driven from the honeypots, the final product is a classifier that distinguish the spammer profiles from legitimate ones through data mining and machine learning techniques. To achieve this, the research first overviews the main challenges and limitations that obstruct the spam detection process, and presents the related researches with their results. It then, outlines the implementation steps, from the honeypot construction step, passing through the data collection and preparation and ending by building the classifier itself. Finally, it presents the observations driven from the honeypot and the results from the classifier and validates it against the results from previous researches on different social platforms. The main contribution of this thesis is the end classifier which will be able to distinguish between the legitimate Facebook profiles and the spammer ones. The originality of the research lies in its aim to detect all kind of social spammers, not only the spreading-malware spammers, but also spamming in its general context, e.g. the ones spreading profanity, bulk messages and unapproved contents