Industrial robots are complex and customizable machines that can be programmed with proprietary domain-specific languages. These languages provide not only movement instructions, but also access to low-level system resources such as the network or the file system. Although useful, these features can lead to taint-style vulnerabilities and can be misused to implement malware---on par with general-purpose programming languages.

Website defacement is the practice of altering the web pages of a website after its compromise. The altered pages, calleddeface pages, can negatively affect the reputation and business of the victim site. Previous research has focused primarily on detection, rather than exploring the defacement phenomenon in depth. While investigating several defacements, we observed that the artifacts left by the defacers allow an expert analyst to investigate the actors' modus operandi and social structure, and expand from the single deface page to a group of related defacements (i.e., acampaign ). However, manually performing such analysis on millions of incidents is tedious, and poses scalability challenges. From these observations, we propose an automated approach that efficiently builds intelligence information out of raw deface pages. Our approach streamlines the analysts job by automatically recognizing defacement campaigns, and assigning meaningful textual labels to them. Applied to a comprehensive dataset of 13 million defacement records, from Jan. 1998 to Sept. 2016, our approach allowed us to conduct the first large-scale measurement on web defacement campaigns. In addition, our approach is meant to be adopted operationally by analysts to identify live campaigns on the field.

The significant growth of banking fraud, fueled by the underground economy of malware, has raised the need for effective detection systems. Therefore, in the last few years, banks have upgraded their security to protect transactions from fraud. State-of-the-art solutions detect fraud as deviations from customers’ spending habits. To the best of our knowledge, almost all existing approaches do not provide an in-depth model’s granularity and security analysis against elusive attacks. In this article, we examine Banksealer, a decision support system for banking fraud analysis that evaluates the influence on detection performance of the granularity at which spending habits are modeled and its security against evasive attacks. First, we compare user-centric modeling, which builds a model for each user, with system-centric modeling, which builds a model for the entire system, from the point of view of detection performance. Then, we assess the robustness of Banksealer against malicious attackers that are aware of the structure of the models in use. To this end, we design and implement a proof-of-concept attack tool that performs mimicry attacks, emulating a sophisticated attacker that cloaks frauds to avoid detection. We experimentally confirm the feasibility of such attacks, their cost, and the effort required by an attacker in order to perform them. In addition, we discuss possible countermeasures. We provide a comprehensive evaluation on a large real-world dataset obtained from one of the largest Italian banks.

It could be argued that without search engines, the web would have never grown to the size that it has today. To achieve maximum coverage and provide relevant results, search engines employ large armies of autonomous crawlers that continuously scour the web, following links, indexing content, and collecting features that are then used to calculate the ranking of each page. In this paper, we describe how autonomous crawlers can be abused by attackers to exploit vulnerabilities on thirdparty websites while hiding the true origin of the attacks. Moreover, we show how certain vulnerabilities on websites that are currently deemed unimportant, can be abused in a way that would allow an attacker to arbitrarily boost the rankings of malicious websites in the search results of popular search engines. Motivated by the potentials of these vulnerabilities, we propose a series of preventive and defensive countermeasures that website owners and search engines can adopt to minimize, or altogether eliminate, the effects of crawler-abusing attacks.

Industrial robots, automated manufacturing, and efficient logistics processes are at the heart of the upcoming fourth industrial revolution. While there are seminal studies on the vulnerabilities of cyber-physical systems in the industry, as of today there has been no systematic analysis of the security of industrial robot controllers. We examine the standard architecture of an industrial robot and analyze a concrete deployment from a systems security standpoint. Then, we propose an attacker model and confront it with the minimal set of requirements that industrial robots should honor: precision in sensing the environment, correctness in execution of control logic, and safety for human operators. Following an experimental and practical approach, we then show how our modeled attacker can subvert such requirements through the exploitation of software vulnerabilities, leading to severe consequences that are unique to the robotics domain. We conclude by discussing safety standards and security challenges in industrial robotics.

Nowadays Information stealers are reaching high levels of sophistication. The number of families and variants observed increased exponentially in the last years. Furthermore, these trojans are sold on underground markets along with automatic frameworks that include web-based administration panels, builders and customization procedures. From a technical point of view such malware is equipped with a functionality, called WebInject, that exploits API hooking techniques to intercept all sensitive data in a browser context and modify web pages on infected hosts. In this paper we propose Prometheus, an automatic system that is able to analyze trojans that base their attack technique on DOM modifications. Prometheus is able to identify the injection operations performed by malware, and generate signatures based on the injection behavior. Furthermore, it is able to extract the WebInject targets by using memory forensic techniques. We evaluated Prometheus against real-world, online websites and a dataset of distinct variants of financial trojans. In our experiments we show that our approach correctly recognizes known variants of WebInject-based malware and successfully extracts the WebInject targets.

Abstract Nowadays users often possess a variety of electronic devices for communication and entertainment. In particular, smartphones are playing an increasingly central role in users’ lives: Users carry them everywhere they go and often use them to control other devices. This trend provides incentives for the industry to tackle new challenges, such as cross-device authentication, and to develop new monetization schemes. A new technology based on ultrasounds has recently emerged to meet these demands. Ultrasound technology has a number of desirable features: it is easy to deploy, flexible, and inaudible by humans. This technology is already utilized in a number of different real-world applications, such as device pairing, proximity detection, and cross-device tracking. This paper examines the different facets of ultrasound-based technology. Initially, we discuss how it is already used in the real world, and subsequently examine this emerging technology from the privacy and security perspectives. In particular, we first observe that the lack of OS features results in violations of the principle of least privilege: an app that wants to use this technology currently needs to require full access to the device microphone. We then analyse real-world Android apps and find that tracking techniques based on ultrasounds suffer from a number of vulnerabilities and are susceptible to various attacks. For example, we show that ultrasound cross-device tracking deployments can be abused to perform stealthy deanonymization attacks (e.g., to unmask users who browse the Internet through anonymity networks such as Tor), to inject fake or spoofed audio beacons, and to leak a user’s private information. Based on our findings, we introduce several defense mechanisms. We first propose and implement immediately deployable defenses that empower practitioners, researchers, and everyday users to protect their privacy. In particular, we introduce a browser extension and an Android permission that enable the user to selectively suppress frequencies falling within the ultrasonic spectrum. We then argue for the standardization of ultrasound beacons, and we envision a flexible OS-level API that addresses both the effortless deployment of ultrasound-enabled applications, and the prevention of existing privacy and security problems.

Android introduced the dynamic code loading (DCL) mechanism to allow for code reuse, to achieve extensibility, to enable updating functionalities, or to boost application start-up performance. In spite of its wide adoption by developers, previous research has shown that the secure implementation of DCL-based functionality is challenging, often leading to remote code injection vulnerabilities. Unfortunately, previous attempts to address this problem by both the academic and Android developers communities are affected by either practicality or completeness issues, and, in some cases, are affected by severe vulnerabilities.

The capabilities of modern devices, coupled with the almost ubiquitous availability of Internet connectivity, have resulted in photos being shared online at an unprecedented scale. This is further amplified by the popularity of social networks and the immediacy they offer in content sharing. Existing access control mechanisms are too coarse-grained to handle cases of conflicting interests between the users associated with a photo; stories of embarrassing or inappropriate photos being widely accessible have become quite common. In this paper, we propose to rethink access control when applied to photos, in a way that allows us to effectively prevent unwanted individuals from recognizing users in a photo. The core concept behind our approach is to change the granularity of access control from the level of the photo to that of a user's personally identifiable information (PII). In this work, we consider the face as the PII. When another user attempts to access a photo, the system determines which faces the user does not have the permission to view, and presents the photo with the restricted faces blurred out. Our system takes advantage of the existing face recognition functionality of social networks, and can interoperate with the current photo-level access control mechanisms. We implement a proof-of-concept application for Facebook, and demonstrate that the performance overhead of our approach is minimal. We also conduct a user study to evaluate the privacy offered by our approach, and find that it effectively prevents users from identifying their contacts in 87.35% of the restricted photos. Finally, our study reveals the misconceptions about the privacy offered by existing mechanisms, and demonstrates that users are positive towards the adoption of an intuitive, straightforward access control mechanism that allows them to manage the visibility of their face in published photos.

In an effort to hinder attackers from compromising user accounts, Facebook launched a form of two-factor authentication called social authentication (SA), where users are required to identify photos of their friends to complete a log-in attempt. Recent research, however, demonstrated that attackers can bypass the mechanism by employing face recognition software. Here we demonstrate an alternative attack. that employs image comparison techniques to identify the SA photos within an offline collection of the users' photos.

Modern trojans are equipped with a functionality, called WebInject, that can be used to silently modify a web page on the infected end host. Given its flexibility, WebInject-based malware is becoming a popular information-stealing mechanism. In addition, the structured and well-organized malware-as-a-service model makes revenue out of customization kits, which in turns leads to high volumes of binary variants. Analysis approaches based on memory carving to extract the decrypted webinject.txt and config.bin files at runtime make the strong assumption that the malware will never change the way such files are handled internally, and therefore are not future proof by design. In addition, developers of sensitive web applications (e.g., online banking) have no tools that they can possibly use to even mitigate the effect of WebInjects.

URL shortening services facilitate the need of exchanging long URLs using limited space, by creating compact URL aliases that redirect users to the original URLs when followed. Some of these services show advertisements (ads) to link-clicking users and pay a commission of their advertising earnings to link-shortening users.

Solid-state drives (SSDs) are inherently different from traditional drives, as they incorporate data-optimization mechanisms to overcome their limitations (such as a limited number of program-erase cycles, or the need of blanking a block before writing). The most common optimizations are wear leveling, trimming, compression, and garbage collection, which operate transparently to the host OS and, in certain cases, even when the disks are disconnected from a computer (but still powered up). In simple words, SSD controllers are designed to hide these internals completely, rendering them inaccessible if not through direct acquisition of the memory cells.

URL shortening services have become extremely popular. However, it is still unclear whether they are an effective and reliable tool that can be leveraged to hide malicious URLs, and to what extent these abuses can impact the end users. With these questions in mind, we first analyzed existing countermeasures adopted by popular shortening services. Surprisingly, we found such countermeasures to be ineffective and trivial to bypass. This first measurement motivated us to proceed further with a large-scale collection of the HTTP interactions that originate when web users access live pages that contain short URLs. To this end, we monitored 622 distinct URL shortening services between March 2010 and April 2012, and collected 24,953,881 distinct short URLs. With this large dataset, we studied the abuse of short URLs. Despite short URLs are a significant, new security risk, in accordance with the reports resulting from the observation of the overall phishing and spamming activity, we found that only a relatively small fraction of users ever encountered malicious short URLs. Interestingly, during the second year of measurement, we noticed an increased percentage of short URLs being abused for drive-by download campaigns and a decreased percentage of short URLs being abused for spam campaigns. In addition to these security-related findings, our unique monitoring infrastructure and large dataset allowed us to complement previous research on short URLs and analyze these web services from the user's perspective.

Malicious software installed on infected computers is a fundamental component of online crime. Malware development thus plays an essential role in the underground economy of cyber-crime. Malware authors regularly update their software to defeat defenses or to support new or improved criminal business models. A large body of research has focused on detecting malware, defending against it and identifying its functionality. In addition to these goals, however, the analysis of malware can provide a glimpse into the software development industry that develops malicious code.

Two-factor authentication is widely used by high-value services to prevent adversaries from compromising accounts using stolen credentials. Facebook has recently released a two-factor authentication mechanism, referred to as Social Authentication, which requires users to identify some of their friends in randomly selected photos. A recent study has provided a formal analysis of social authentication weaknesses against attackers inside the victim's social circles. In this paper, we extend the threat model and study the attack surface of social authentication in practice, and show how any attacker can obtain the information needed to solve the challenges presented by Facebook. We implement a proof-of-concept system that utilizes widely available face recognition software and cloud services, and evaluate it using real public data collected from Facebook. Under the assumptions of Facebook's threat model, our results show that an attacker can obtain access to (sensitive) information for at least 42% of a user's friends that Facebook uses to generate social authentication challenges. By relying solely on publicly accessible information, a casual attacker can solve 22% of the social authentication tests in an automated fashion, and gain a significant advantage for an additional 56% of the tests, as opposed to just guessing. Additionally, we simulate the scenario of a determined attacker placing himself inside the victim's social circle by employing dummy accounts. In this case, the accuracy of our attack greatly increases and reaches 100% when 120 faces per friend are accessible by the attacker, even though it is very accurate with as little as 10 faces.

Our research concentrates on anomaly detection techniques, which have both industrial applications such as network monitoring and protection, as well as research applications such as software behavioral analysis or malware classification. During our doctoral research, we worked on anomaly detection from three different perspective, as a complex computer infrastructure has several weak spots that must be protected. We first focused on the operating system, central to any computer, to avoid malicious code to subvert its normal activity. Secondly, we concentrated on web applications, which are the main interface to modern computing: Because of their immense popularity, they have indeed become the most targeted entry point of intrusions. Last, we developed novel techniques with the aim of identifying related events (e.g., alerts reported by intrusion detection systems) to build new and more compact knowledge to detect malicious activity on large-scale systems. During our research we enhanced existing anomaly detection tools and also contributed with new ones. Such tools have been tested over different datasets, both synthetic data and real network traffic, and lead to interesting results that were accepted for publication at main security venues.

The pervasiveness of mobile devices increases the risk of exposing sensitive information on the go. In this paper, we arise this concern by presenting an automatic attack against modern touchscreen keyboards. We demonstrate the attack against the Apple iPhone - 2010's most popular touchscreen device - although it can be adapted to other devices (e.g., Android) that employ similar key-magnifying keyboards. Our attack processes the stream of frames from a video camera (e.g., surveillance or portable camera) and recognizes keystrokes online, in a fraction of the time needed to perform the same task by direct observation or offline analysis of a recorded video, which can be unfeasible for large amount of data. Our attack detects, tracks, and rectifies the target touchscreen, thus following the device or camera's movements and eliminating possible perspective distortions and rotations In real-world settings, our attack can automatically recognize up to 97.07 percent of the keystrokes (91.03 on average), with 1.15 percent of errors (3.16 on average) at a speed ranging from 37 to 51 keystrokes per minute.

Manual analysis of security-related events is still a necessity to investigate non-trivial cyber attacks. This task is particularly hard when the events involve slow, stealthy and large-scale activities typical of the modern cybercriminals' strategy. In this regard, visualization tools can effectively help analysts in their investigations. In this paper, we present BURN, an interactive visualization tool for displaying autonomous systems exhibiting rogue activity that helps at finding misbehaving networks through visual and interactive exploration. Up to seven values are displayed in a single visual element, while avoiding cumbersome and confusing maps. To this end, animations and alpha channels are leveraged to create simple views that highlight relevant activity patterns. In addition, BURN incorporates a simple algorithm to identify migrations of nefarious services across autonomous systems, which can support, for instance, root-cause analysis and law enforcement investigations.

We describe an unsupervised host-based intrusion detection system based on system call arguments and sequences. We define a set of anomaly detection models for the individual parameters of the call. We then describe a clustering process that helps to better fit models to system call arguments and creates interrelations among different arguments of a system call. Finally, we add a behavioral Markov model in order to capture time correlations and abnormal behaviors. The whole system needs no prior knowledge input; it has a good signal-to-noise ratio, and it is also able to correctly contextualize alarms, giving the user more information to understand whether a true or false positive happened, and to detect global variations over the entire execution flow, as opposed to punctual ones over individual instances.