An Approach to Enable Cloud-Computing by the Abstraction of Event-Processing Classes

Following our introduction of the concept ofAbstraction Classes, we present herein their realisation withina cloud environment. This is achieved using a combinationof integrated service-location models, including Knowledge-Based Systems, and distributed metadata using XML. This iscomplemented by service control software invoked at the level ofAbstraction Classes

E-commerce bot traffic: in-network impact, detection, and mitigation

In-network caching expedites data retrieval by storing frequently accessed data items within programmable data planes, thereby reducing data access latency. In this paper we explore a vulnerability of in-network caching to bots’ traffic, showing it can significantly degrade performance. As bots constitute up to 70% of traffic on e-commerce platforms like Amazon, this is a critical problem. To mitigate the effect of bots’ traffic we introduce In-network Caching Shelter (INCS), an in-network machine learning solution implemented on NVIDIA BlueField-2 DPU. Our evaluation shows that INCS can detect malicious bot traffic patterns with accuracy up to 94.72%. Furthermore, INCS takes smart actions to mitigate the effects of bot activity

Clicktok : click fraud detection using traffic analysis

Advertising is a primary means for revenue generation for millions of websites and smartphone apps. Naturally, a fraction abuse ad networks to systematically defraud advertisers of their money. Modern defences have matured to overcome some forms of click fraud but measurement studies have reported that a third of clicks supplied by ad networks could be clickspam. Our work develops novel inference techniques which can isolate click fraud attacks using their fundamental properties.We propose two defences, mimicry and bait-click, which provide clickspam detection with substantially improved results over current approaches. Mimicry leverages the observation that organic clickfraud involves the reuse of legitimate click traffic, and thus isolates clickspam by detecting patterns of click reuse within ad network clickstreams. The bait-click defence leverages the vantage point of an ad network to inject a pattern of bait clicks into a user's device. Any organic clickspam generated involving the bait clicks will be subsequently recognisable by the ad network. Our experiments show that the mimicry defence detects around 81% of fake clicks in stealthy (low rate) attacks, with a false-positive rate of 110 per hundred thousand clicks. Similarly, the bait-click defence enables further improvements in detection, with rates of 95% and a reduction in false-positive rates of between 0 and 30 clicks per million - a substantial improvement over current approaches

Detection of Anomalies in User Behaviour

Internetové vyhledáváče se staly nepostradatelným nástrojem našeho každodenního života. Možnost pohodlně a bez prodlevy vyhledávát informace denně přiláká miliardy lidí. Bohužel někteří uživatelé, lidští či naprogramovaní, se snaží vyhledávače zneužívat ve svůj prospěch, a to například tím, že klikají v nadměrném množství na výsledky vedoucí na jejich doménu, aby zvýšili její popularitu, a tím zlepšili pořadí domény mezi výsledky. Takové chování však může často vést ke zhoršení uživatelského požitku ostatních návštěvníků, a obecně funkcionality vyhledávače. Možností, jak se proti podvodnému a jinému atypickému chování bránit, mají vyhledávače málo, jelikož musí zůstat snadno dostupné. Z důvodu obrovského objemu uživatelů také nepřipadá v úvahu detekovat podvádějící uživatele manuálně, což dále znemožňuje případné natrénování jednoduchého klasifikátoru. Tato bakalářská práce se zabývá způsoby hledání klasifikátoru pomocí metody učení bez učitele, který umožní detekovat toto anomální uživatelské chování. Dohromady ukazuje tři modely využívající různé charakteristiky uživatelských relací. Předbězné výsledky ukazují, že dosažené poznatky by se po dalším rozpracování mohly využít i v praxi.Search engines have become a fundamental tool of everyday live, billions of users are using them to get the information they desire in a comfortable way. Unfortunately, some visitors exhibit various kinds of malicious behavior. For instance, they try to deplete competitions advertising budget through excessive clicking on sponsored results. Such anomalous behavior often leads to a worsened user experience of "normal users". In addition, due to the vast amounts of visitors, such behavior is hard to detect manually, which further means that we can't use standard supervised methods to train a user classifier. This thesis introduces three unsupervised models for atypical user detection, all of them evaluate distinct user session characteristics, for instance, click, query and behavioral patterns. The preliminary results show, that with some further improvements the current findings could be deployed for real world use