arXiv subjects

Gianluca Stringhini

Publications and source records attributed to Gianluca Stringhini.

83 records · Page 5Linked to original sources

arXiv2017

Mean Birds: Detecting Aggression and Bullying on Twitter

In recent years, bullying and aggression against users on social media have grown significantly, causing serious consequences to victims of all demographics. In particular, cyberbullying affects more than half of young social media users worldwide, and has also led to teenage suicides, prompted by prolonged and/or coordinated digital harassment. Nonetheless, tools and technologies for understanding and mitigating it are scarce and mostly ineffective. In this paper, we present a principled and scalable approach to detect bullying and aggressive behavior on Twitter. We propose a robust methodology for extracting text, user, and network-based attributes, studying the properties of cyberbullies and aggressors, and what features distinguish them from regular users. We find that bully users post less, participate in fewer online communities, and are less popular than normal users, while aggressors are quite popular and tend to include more negativity in their posts. We evaluate our methodology using a corpus of 1.6M tweets posted over 3 months, and show that machine learning classification algorithms can accurately detect users exhibiting bullying and aggressive behavior, achieving over 90% AUC.

cs.CY↗

arXiv2017

Hate is not Binary: Studying Abusive Behavior of #GamerGate on Twitter

Over the past few years, online bullying and aggression have become increasingly prominent, and manifested in many different forms on social media. However, there is little work analyzing the characteristics of abusive users and what distinguishes them from typical social media users. In this paper, we start addressing this gap by analyzing tweets containing a great large amount of abusiveness. We focus on a Twitter dataset revolving around the Gamergate controversy, which led to many incidents of cyberbullying and cyberaggression on various gaming and social media platforms. We study the properties of the users tweeting about Gamergate, the content they post, and the differences in their behavior compared to typical Twitter users. We find that while their tweets are often seemingly about aggressive and hateful subjects, "Gamergaters" do not exhibit common expressions of online anger, and in fact primarily differ from typical users in that their tweets are less joyful. They are also more engaged than typical Twitter users, which is an indication as to how and why this controversy is still ongoing. Surprisingly, we find that Gamergaters are less likely to be suspended by Twitter, thus we analyze their properties to identify differences from typical users and what may have led to their suspension. We perform an unsupervised machine learning analysis to detect clusters of users who, though currently active, could be considered for suspension since they exhibit similar behaviors with suspended users. Finally, we confirm the usefulness of our analyzed features by emulating the Twitter suspension mechanism with a supervised learning method, achieving very good precision and recall.

cs.SI↗

arXiv2017

Kissing Cuisines: Exploring Worldwide Culinary Habits on the Web

Food and nutrition occupy an increasingly prevalent space on the web, and dishes and recipes shared online provide an invaluable mirror into culinary cultures and attitudes around the world. More specifically, ingredients, flavors, and nutrition information become strong signals of the taste preferences of individuals and civilizations. However, there is little understanding of these palate varieties. In this paper, we present a large-scale study of recipes published on the web and their content, aiming to understand cuisines and culinary habits around the world. Using a database of more than 157K recipes from over 200 different cuisines, we analyze ingredients, flavors, and nutritional values which distinguish dishes from different regions, and use this knowledge to assess the predictability of recipes from different cuisines. We then use country health statistics to understand the relation between these factors and health indicators of different nations, such as obesity, diabetes, migration, and health expenditure. Our results confirm the strong effects of geographical and cultural similarities on recipes, health indicators, and culinary preferences across the globe.

cs.CY↗

arXiv2017

Email Babel: Does Language Affect Criminal Activity in Compromised Webmail Accounts?

We set out to understand the effects of differing language on the ability of cybercriminals to navigate webmail accounts and locate sensitive information in them. To this end, we configured thirty Gmail honeypot accounts with English, Romanian, and Greek language settings. We populated the accounts with email messages in those languages by subscribing them to selected online newsletters. We hid email messages about fake bank accounts in fifteen of the accounts to mimic real-world webmail users that sometimes store sensitive information in their accounts. We then leaked credentials to the honey accounts via paste sites on the Surface Web and the Dark Web, and collected data for fifteen days. Our statistical analyses on the data show that cybercriminals are more likely to discover sensitive information (bank account information) in the Greek accounts than the remaining accounts, contrary to the expectation that Greek ought to constitute a barrier to the understanding of non-Greek visitors to the Greek accounts. We also extracted the important words among the emails that cybercriminals accessed (as an approximation of the keywords that they searched for within the honey accounts), and found that financial terms featured among the top words. In summary, we show that language plays a significant role in the ability of cybercriminals to access sensitive information hidden in compromised webmail accounts.

cs.CY↗

arXiv2017

Measuring #GamerGate: A Tale of Hate, Sexism, and Bullying

Over the past few years, online aggression and abusive behaviors have occurred in many different forms and on a variety of platforms. In extreme cases, these incidents have evolved into hate, discrimination, and bullying, and even materialized into real-world threats and attacks against individuals or groups. In this paper, we study the Gamergate controversy. Started in August 2014 in the online gaming world, it quickly spread across various social networking platforms, ultimately leading to many incidents of cyberbullying and cyberaggression. We focus on Twitter, presenting a measurement study of a dataset of 340k unique users and 1.6M tweets to study the properties of these users, the content they post, and how they differ from random Twitter users. We find that users involved in this "Twitter war" tend to have more friends and followers, are generally more engaged and post tweets with negative sentiment, less joy, and more hate than random users. We also perform preliminary measurements on how the Twitter suspension mechanism deals with such abusive behaviors. While we focus on Gamergate, our methodology to collect and analyze tweets related to aggressive and bullying activities is of independent interest.

cs.SI↗

arXiv2017

What's in a Name? Understanding Profile Name Reuse on Twitter

Users on Twitter are commonly identified by their profile names. These names are used when directly addressing users on Twitter, are part of their profile page URLs, and can become a trademark for popular accounts, with people referring to celebrities by their real name and their profile name, interchangeably. Twitter, however, has chosen to not permanently link profile names to their corresponding user accounts. In fact, Twitter allows users to change their profile name, and afterwards makes the old profile names available for other users to take. In this paper, we provide a large-scale study of the phenomenon of profile name reuse on Twitter. We show that this phenomenon is not uncommon, investigate the dynamics of profile name reuse, and characterize the accounts that are involved in it. We find that many of these accounts adopt abandoned profile names for questionable purposes, such as spreading malicious content, and using the profile name's popularity for search engine optimization. Finally, we show that this problem is not unique to Twitter (as other popular online social networks also release profile names) and argue that the risks involved with profile-name reuse outnumber the advantages provided by this feature.

cs.SI↗

arXiv2017

All Your Cards Are Belong To Us: Understanding Online Carding Forums

Underground online forums are platforms that enable trades of illicit services and stolen goods. Carding forums, in particular, are known for being focused on trading financial information. However, little evidence exists about the sellers that are present on carding forums, the precise types of products they advertise, and the prices buyers pay. Existing literature mainly focuses on the organisation and structure of the forums. Furthermore, studies on carding forums are usually based on literature review, expert interviews, or data from forums that have already been shut down. This paper provides first-of-its-kind empirical evidence on active forums where stolen financial data is traded. We monitored 5 out of 25 discovered forums, collected posts from the forums over a three-month period, and analysed them quantitatively and qualitatively. We focused our analyses on products, prices, seller prolificacy, seller specialisation, and seller reputation.

cs.CR↗

arXiv2016

Honey Sheets: What Happens to Leaked Google Spreadsheets?

Cloud-based documents are inherently valuable, due to the volume and nature of sensitive personal and business content stored in them. Despite the importance of such documents to Internet users, there are still large gaps in the understanding of what cybercriminals do when they illicitly get access to them by for example compromising the account credentials they are associated with. In this paper, we present a system able to monitor user activity on Google spreadsheets. We populated 5 Google spreadsheets with fake bank account details and fake funds transfer links. Each spreadsheet was configured to report details of accesses and clicks on links back to us. To study how people interact with these spreadsheets in case they are leaked, we posted unique links pointing to the spreadsheets on a popular paste site. We then monitored activity in the accounts for 72 days, and observed 165 accesses in total. We were able to observe interesting modifications to these spreadsheets performed by illicit accesses. For instance, we observed deletion of some fake bank account information, in addition to insults and warnings that some visitors entered in some of the spreadsheets. Our preliminary results show that our system can be used to shed light on cybercriminal behavior with regards to leaked online documents.

cs.CR↗

arXiv2015

Master of Puppets: Analyzing And Attacking A Botnet For Fun And Profit

A botnet is a network of compromised machines (bots), under the control of an attacker. Many of these machines are infected without their owners' knowledge, and botnets are the driving force behind several misuses and criminal activities on the Internet (for example spam emails). Depending on its topology, a botnet can have zero or more command and control (C&C) servers, which are centralized machines controlled by the cybercriminal that issue commands and receive reports back from the co-opted bots. In this paper, we present a comprehensive analysis of the command and control infrastructure of one of the world's largest proprietary spamming botnets between 2007 and 2012: Cutwail/Pushdo. We identify the key functionalities needed by a spamming botnet to operate effectively. We then develop a number of attacks against the command and control logic of Cutwail that target those functionalities, and make the spamming operations of the botnet less effective. This analysis was made possible by having access to the source code of the C&C software, as well as setting up our own Cutwail C&C server, and by implementing a clone of the Cutwail bot. With the help of this tool, we were able to enumerate the number of bots currently registered with the C&C server, impersonate an existing bot to report false information to the C&C server, and manipulate spamming statistics of an arbitrary bot stored in the C&C database. Furthermore, we were able to make the control server inaccessible by conducting a distributed denial of service (DDoS) attack. Our results may be used by law enforcement and practitioners to develop better techniques to mitigate and cripple other botnets, since many of findings are generic and are due to the workflow of C&C communication in general.

cs.CR↗

arXiv2015

Towards Detecting Compromised Accounts on Social Networks

Compromising social network accounts has become a profitable course of action for cybercriminals. By hijacking control of a popular media or business account, attackers can distribute their malicious messages or disseminate fake information to a large user base. The impacts of these incidents range from a tarnished reputation to multi-billion dollar monetary losses on financial markets. In our previous work, we demonstrated how we can detect large-scale compromises (i.e., so-called campaigns) of regular online social network users. In this work, we show how we can use similar techniques to identify compromises of individual high-profile accounts. High-profile accounts frequently have one characteristic that makes this detection reliable -- they show consistent behavior over time. We show that our system, were it deployed, would have been able to detect and prevent three real-world attacks against popular companies and news agencies. Furthermore, our system, in contrast to popular media, would not have fallen for a staged compromise instigated by a US restaurant chain for publicity reasons.

cs.CR↗

arXiv2014

That Ain't You: Detecting Spearphishing Emails Before They Are Sent

One of the ways in which attackers try to steal sensitive information from corporations is by sending spearphishing emails. This type of emails typically appear to be sent by one of the victim's coworkers, but have instead been crafted by an attacker. A particularly insidious type of spearphishing emails are the ones that do not only claim to come from a trusted party, but were actually sent from that party's legitimate email account that was compromised in the first place. In this paper, we propose a radical change of focus in the techniques used for detecting such malicious emails: instead of looking for particular features that are indicative of attack emails, we look for possible indicators of impersonation of the legitimate owners. We present IdentityMailer, a system that validates the authorship of emails by learning the typical email-sending behavior of users over time, and comparing any subsequent email sent from their accounts against this model. Our experiments on real world e-mail datasets demonstrate that our system can effectively block advanced email attacks sent from genuine email accounts, which traditional protection systems are unable to detect. Moreover, we show that it is resilient to an attacker willing to evade the system. To the best of our knowledge, IdentityMailer is the first system able to identify spearphishing emails that are sent from within an organization, by a skilled attacker having access to a compromised email account.

cs.CR↗