Marco Balduzzi

Abusing social networks for automated user profiling

Recently, social networks such as Facebook have experienced a huge surge in popularity. The amount of personal information stored on these sites calls for appropriate security precautions to protect this data. In this paper, we describe how we are able to take advantage of a common weakness, namely the fact that an attacker can query popular social networks for registered e-mail addresses on a large scale. Starting with a list of about 10.4 million email addresses, we were able to automatically identify more than 1.2 million user profiles associated with these addresses. By automatically crawling and correlating these profiles, we collect detailed personal information about each user, which we use for automated profiling (i.e., to enrich the information available from each user). Having access to such information would allow an attacker to launch sophisticated, targeted attacks, or to improve the efficiency of spam campaigns. We have contacted the most popular providers, who acknowledged the threat and are currently implementing our proposed countermeasures. Facebook and XING, in particular, have recently fixed the problem.

A security analysis of amazon's elastic compute cloud service

Cloud services such as Amazons Elastic Compute Cloud and IBMs SmartCloud are quickly changing the way organizations are dealing with IT infrastructures and are providing online services. Today, if an organization needs computing power, it can simply buy it online by instantiating a virtual server image on the cloud. Servers can be quickly launched and shut down via application programming interfaces (API), offering the user a greater flexibility compared to traditional server rooms. In this talk, I will explore the general security risks associated with using virtual server images from the public catalogs of cloud service providers. In particular, we investigate in detail the security problems of public images that are available on the Amazon EC2 service. I will describe the design and implementation of an automated system that we used to instantiate and analyze the security of public AMIs (Amazon Machine Images) on the Amazon EC2 platform, and provide detailed descriptions of the security tests that we performed on each image. Our findings demonstrate that both the users and the providers of public AMIs may be vulnerable to security risks such as unauthorized access, malware infections, and loss of sensitive information. The Amazon Web Services Security Team has acknowledged our findings, and has already taken steps to properly address all the security risks we present in this talk.

Reverse social engineering attacks in online social networks

Social networks are some of the largest and fastest growing online services today. Facebook, for example, has been ranked as the second most visited site on the Internet, and has been reporting growth rates as high as 3% per week. One of the key features of social networks is the support they provide for finding new friends. For example, social network sites may try to automatically identify which users know each other in order to propose friendship recommendations. Clearly, most social network sites are critical with respect to users security and privacy due to the large amount of information available on them, as well as their very large user base. Previous research has shown that users of online social networks tend to exhibit a higher degree of trust in friend requests and messages sent by other users. Even though the problem of unsolicited messages in social networks (i.e., spam) has already been studied in detail, to date, reverse social engineering attacks in social networks have not received any attention. In a reverse social engineering attack, the attacker does not initiate contact with the victim. Rather, the victim is tricked into contacting the attacker herself. As a result, a high degree of trust is established between the victim and the attacker as the victim is the entity that established the relationship. In this paper, we present the first user study on reverse social engineering attacks in social networks. That is, we discuss and show how attackers, in practice, can abuse some of the friend-finding features that online social networks provide with the aim of launching reverse social engineering attacks. Our results demonstrate that reverse social engineering attacks are feasible and effective in practice.

A solution for the automated detection of clickjacking attacks

Clickjacking is a web-based attack that has recently received a wide media coverage. In a clickjacking attack, a malicious page is constructed such that it tricks victims into clicking on an element of a different page that is only barely (or not at all) visible. By stealing the victims clicks, an attacker could force the user to perform an unintended action that is advantageous for the attacker (e.g., initiate an online money transaction). Although clickjacking has been the subject of many discussions and alarming reports, it is currently unclear to what extent clickjacking is being used by attackers in the wild, and how significant the attack is for the security of Internet users. In this paper, we propose a novel solution for the automated and efficient detection of clickjacking attacks. We describe the system that we designed, implemented and deployed to analyze over a million unique web pages. The experiments show that our approach is feasible in practice. Also, the empirical study that we conducted on a large number of popular websites suggests that clickjacking has not yet been largely adopted by attackers on the Internet.

Take a deep breath: a stealthy, resilient and cost-effective botnet using skype

Skype is one of the most used P2P applications on the Internet: VoIP calls, instant messaging, SMS and other features are provided at a low cost to millions of users. Although Skype is a closed source application, an API allows developers to build custom plugins which interact over the Skype network, taking advantage of its reliability and capability to easily bypass firewalls and NAT devices. Since the protocol is completely undocumented, Skype traffic is particularly hard to analyze and to reverse engineer. We propose a novel botnet model that exploits an overlay network such as Skype to build a parasitic overlay, making it extremely difficult to track the botmaster and disrupt the botnet without damaging legitimate Skype users. While Skype is particularly valid for this purpose due to its abundance of features and its widespread installed base, ourmodel is generically applicable to distributed applications that employ overlay networks to send direct messages between nodes (e.g., peer-to-peer software with messaging capabilities). We are convinced that similar bot-netmodels are likely to appear into the wild in the near future and that the threats they pose should not be underestimated. Our contribution strives to provide the tools to correctly evaluate and understand the possible evolution and deployment of this phenomenon.

The role of phone numbers in understanding cyber-crime schemes

Internet and telephones are part of everyones modern life. Unfortunately, several criminal activities also rely on these technologies to reach their victims. While the use and importance of the Internet has been largely studied, previous work overlooked the role that phone numbers can play in understanding online threats. In this work we aim at determining if leveraging phone numbers analysis can improve our understanding of the underground markets, illegal computer activities, or cyber-crime in general. This knowledge could then be adopted by several defensive mechanisms, including blacklists or advanced spam heuristics. Our results show that, in scam activities, phone numbers remain often more stable over time than email addresses. Using a combination of graph analysis and geographical Home Location Register (HLR) lookups, we identify recurrent cyber-criminal business models and link together scam communities that spread over different countries.

Soundsquatting: Uncovering the Use of Homophones in Domain Squatting

In this paper we present soundsquatting, a previously unreported type of domain squatting which we uncovered during analysis of cybersquatting domains. In soundsquatting, an attacker takes advantage of homophones, i.e., words that sound alike, and registers homophone-including variants of popular domain names. We explain why soundsquatting is different from existing domain-squatting attacks, and describe a tool for the automatic generation of soundsquatting domains. Using our tool, we discover that attackers are already aware of the principles of soundsquatting and are monetizing them in various unethical and illegal ways. In addition, we register our own soundsquatting domains and study the population of users who reach our monitors, recording a monthly average of more than 1,700 non-bot page requests. Lastly, we show how sound-dependent users are particularly vulnerable to soundsquatting through the abuse of text-to-speech software.

MobiPot: Understanding Mobile Telephony Threats with Honeycards

Over the past decade, the number of mobile phones has increased dramatically, overtaking the world population in October 2014. In developing countries like India and China, mobile subscribers outnumber traditional landline users and account for over 90% of the active population. At the same time, convergence of telephony with the Internet with technologies like VoIP makes it possible to reach a large number of telephone users at a low or no cost via voice calls or SMS (short message service) messages. As a consequence, cybercriminals are abusing the telephony channel to launch attacks, e.g., scams that offer fraudulent services and voice-based phishing or vishing, that have previously relied on the Internet. In this paper, we introduce and deploy the first mobile phone honeypot called MobiPot that allow us to collect fraudulent calls and SMS messages. We implement multiple ways of advertising mobile numbers (honeycards) on MobiPot to investigate how fraudsters collect phone numbers that are targeted by them. During a period of over seven months, MobiPot collected over two thousand voice calls and SMS messages, and we confirmed that over half of them were unsolicited. We found that seeding honeycards enables us to discover attacks on the mobile phone numbers which were not known before.

Automatic Extraction of Indicators of Compromise for Web Applications

Indicators of Compromise (IOCs) are forensic artifacts that are used as signs that a system has been compromised by an attack or that it has been infected with a particular malicious software. In this paper we propose for the first time an automated technique to extract and validate IOCs for web applications, by analyzing the information collected by a high-interaction honeypot. Our approach has several advantages compared with traditional techniques used to detect malicious websites. First of all, not all the compromised web pages are malicious or harmful for the user. Some may be defaced to advertise product or services, and some may be part of affiliate programs to redirect users toward (more or less legitimate) online shopping websites. In any case, it is important to detect these pages to inform their owners and to alert the users on the fact that the content of the page has been compromised and cannot be trusted. Also in the case of more traditional drive-by-download pages, the use of IOCs allows for a prompt detection and correlation of infected pages, even before they may be blocked by more traditional URLs blacklists. Our experiments show that our system is able to automatically generate web indicators of compromise that have been used by attackers for several months (and sometimes years) in the wild without being detected. So far, these apparently harmless scripts were able to stay under the radar of the existing detection methodologies -- despite being hosted for a long time on public web sites.

A Summary of Two Practical Attacks Against Social Networks

Social networking sites have been increasingly gaining popularity, and they have already changed the communication habits of hundred of millions of users. Unfortunately, this new technology can easily be misused to collect private information and violate the users’ privacy. In this chapter, we summarize two practical attacks we have presented in the past: an impersonation attack in which we automatically clone a user profile, and an attack that abuses the information provided by social networks to automatically correlate information extracted from different social networks. Our results show that these attacks are very successful in practice and that they can significantly impact the users’ privacy. Therefore, these attacks represent a first important step to raise awareness among users about the privacy and security risks involved in sharing information in one or more social networks.