قابلیت ربات های جفنگنگار تکامل یافته برای شناسایی فرار از طریق مهندسی ژنتیک
Abstract
1- Introduction
2- Background
3- Experiments
4- Evaluation
5- Discussion
6- Related work
7- Conclusions
References
Abstract
Since decades, genetic algorithms have been used as an effective heuristic to solve optimization problems. However, in order to be applied, genetic algorithms may require a string-based genetic encoding of information, which severely limited their applicability when dealing with online accounts. Remarkably, a behavioral modeling technique inspired by biological DNA has been recently proposed – and successfully applied – for monitoring and detecting spambots in Online Social Networks. In this so-called digital DNA representation, the behavioral lifetime of an account is encoded as a sequence of characters, namely a digital DNA sequence. In a previous work, the authors proposed to create synthetic digital DNA sequences that resemble the characteristics of the digital DNA sequences of real accounts. The combination of (i) the capability to model the accounts’ behaviors as digital DNA sequences, (ii) the possibility to create synthetic digital DNA sequences, and (iii) the evolutionary simulations allowed by genetic algorithms, open up the unprecedented opportunity to study – and even anticipate – the evolutionary patterns of modern social spambots. In this paper, we experiment with a novel ad-hoc genetic algorithm that allows to obtain behaviorally evolved spambots. By varying the different parameters of the genetic algorithm, we are able to evaluate the capability of the evolved spambots to escape a state-of-art behavior-based detection technique. Notably, despite such detection technique achieved excellent performances in the recent past, a number of our spambot evolutions manage to escape detection. Our analysis, if carried out at large-scale, would allow to proactively identify possible spambot evolutions capable of evading current detection techniques.
Introduction
As part of today’s ongoing socio-technical convergence, Online Social Networks (OSNs) have a profound impact on our everyday life. We increasingly rely on OSNs content in order to form our opinions, to plan activities, and to establish social relationships. One of the most striking examples of the influence that OSNs have on our societies could be witnessed during all the latest political elections. Indeed, during the 2014 Italian mayoral elections, the 2016 US presidential elections, the 2016 UK Brexit referendum, and the 2017 French presidential elections, social media played a dominant role in the electoral campaigns, often contributing to invert the foreseen electoral outcome1. It is not surprising that OSNs have also been exploited for maliciously influencing the public opinion [1,2]. One common way to achieve this goal is to employ large groups of automated (bot) accounts (henceforth spambots) that repeatedly spam polarized content. Worryingly, this malicious practice is pervasive: it has been witnessed in online discussions on important societal topics (e.g., politics, finance, terrorism, immigration) [3,4], as well as in debates about seemingly less relevant topics, such as products on sale on e-commerce platforms and mobile applications [5]. Among the peculiar characteristics of spambots is that they evolve over time, by changing their behavior – and, in general – by adopting techniques in order to evade existing detection systems [6].