Digital lending applications, commonly referred to as loan apps, have become a primary channel for microcredit in emerging markets. However, many of these apps demand excessive permissions and misuse sensitive user data for coercive debt-recovery practices, including harassment, blackmail, and public shaming that affect both borrowers and their contacts.

Vulnerability management relies on databases that record, classify, and disseminate information on software weaknesses. The National Vulnerability Database (NVD), the Chinese National Vulnerability Database (CNVD), and the Japanese Vulnerability Notes Database (JVNDB) are three prominent resources, each reflecting distinct regional and institutional contexts. NVD, maintained by the U.S. National Institute of Standards and Technology (NIST), provides a globally standardized framework. CNVD, managed by the China Information Technology Security Evaluation Center (CNIT-SEC), emphasizes Chinese software and incidents, often publishing disclosures rapidly. JVNDB, jointly operated by JPCERT/CC and the Information-technology Promotion Agency (IPA) of Japan since 2007, aggregates vulnerabilities reported by Japanese vendors, the JVN portal, and NVD, with content offered in both Japanese and English to serve domestic industries.

Cloud computing has become integral to modern technology infrastructure, supporting a wide range of services from e-commerce to AI applications. Chameleon is a large-scale, configurable testbed designed to enable edge-to-cloud research through full bare-metal provisioning, virtualization, and diverse hardware resources, which is built on a leading open source cloud platform OpenStack. However, monitoring Chameleon’s heterogeneous infrastructure is challenging, particularly across Open-Stack services and hardware components. Traditional threshold-based alerting methods struggle to keep up with the scale and complexity of such environments. In this work, we present an anomaly detection framework for OpenStack services in the Chameleon Cloud. We curate and publish the first dataset of resource usage metrics collected from OpenStack control plane services. We evaluate four state-of-the-art unsupervised multivariate time series models, namely TranAD, Prodigy, USAD, and OmniAnomaly, on this dataset and share key insights from deploying them. Our findings indicate that for our use case, while all models achieve high F1 scores, training with three days of healthy data effectively balances training cost and detection accuracy.

Ensuring the online safety of youth has motivated research towards the development of machine learning (ML) methods capable of accurately detecting social media risks after-the-fact. However, for these detection models to be effective, they must proactively identify high-risk scenarios (e.g., sexual solicitations, cyberbullying) to mitigate harm. This `real-time' responsiveness is a recognized challenge within the risk detection literature. Therefore, this paper presents a novel two-level framework that first uses reinforcement learning to identify conversation stop points to prioritize messages for evaluation. Then, we optimize state-of-the-art deep learning models to accurately categorize risk priority (low, high). We apply this framework to a time-based simulation using a rich dataset of 23K private conversations with over 7 million messages donated by 194 youth (ages 13-21). We conducted an experiment comparing our new approach to a traditional conversation-level baseline. We found that the timeliness of conversations significantly improved from over 2 hours to approximately 16 minutes with only a slight reduction in accuracy (0.88 to 0.84). This study advances real-time detection approaches for social media data and provides a benchmark for future training reinforcement learning that prioritizes the timeliness of classifying high-risk conversations.

Accurate real-time risk identification is vital to protecting social media users from online harm, which has driven research towards advancements in machine learning (ML). While strides have been made regarding the computational facets of algorithms for “real-time” risk detection, such research has not yet evaluated these advancements through a human-centered lens. To this end, we conducted a systematic literature review of 53 peer-reviewed articles on real-time risk detection on social media. Real-time detection was mainly operationalized as “early” detection after-the-fact based on pre-defined chunks of data and evaluated based on standard performance metrics, such as timeliness. We identified several human-centered opportunities for advancing current algorithms, such as integrating human insight in feature selection, algorithms’ improvement considering human behavior, and utilizing human evaluations. This work serves as a critical call-to-action for the HCI and ML communities to work together to protect social media users before, during, and after exposure to risks.

Distributed tracing has become a fundamental tool for diagnosing performance issues in the cloud by recording causally ordered, end-to-end workflows of request executions. However, tracing workloads in production can introduce significant overheads due to the extensive instrumentation needed for identifying performance variations. This paper addresses the trade-off between the cost of tracing and the utility of the “spans” within that trace through Astraea, an online probabilistic distributed tracing system. Astraea is based on our technique that combines online Bayesian learning and multi-armed bandit frameworks. This formulation enables Astraea to effectively steer tracing towards the useful instrumentation needed for accurate performance diagnosis. Astraea localizes performance variations using only 20-35% of available instrumentation, markedly reducing tracing overhead, storage, compute costs, and trace analysis time.

Due to the increasing complexity of cloud architectures, automatically tracking and inspecting container packages in Platform-as-a-Service (PaaS) clusters are challenging tasks. This introspection capability, however, is critical to identify vulnerable packages and compile an accurate Software Bill of Materials (SBOM). Motivated by introspection frameworks focusing on virtual machine (VM) settings and ML methods for software discovery, we design PraxiPaaS as a framework to inspect PaaS container images with a highly scalable ML inference pipeline by scanning file changes during package installations. Our ML pipeline includes a structured collection of word2vec encoders and a corresponding structured ML model to achieve short incremental training time for incorporating additional packages while maintaining a high F1-score in generating the SBOM. Our evaluation shows that our structured ML pipeline provides an exponential drop in incremental training time from 2.8 hours to $8.6 \mathbf{s}$ with 32 CPU cores, while maintaining an F1-score of 0.82, compared to the traditional monolithic model design. We deploy a prototype of PraxiPaaS in the New England Research Cloud (NERC) OpenShift cluster and evaluate the inference time comparing structured versus monolithic model design.

Online web communities often face bans for violating platform policies, encouraging their migration to alternative platforms. This migration, however, can result in increased toxicity and unforeseen consequences on the new platform. In recent years, researchers have collected data from many alternative platforms, indicating coordinated efforts leading to offline events, conspiracy movements, hate speech propagation, and harassment. Thus, it becomes crucial to characterize and understand these alternative platforms. To advance research in this direction, we collect and release a large-scale dataset from Scored -- an alternative Reddit platform that sheltered banned fringe communities, for example, c/TheDonald (a prominent right-wing community) and c/GreatAwakening (a conspiratorial community). Over four years, we collected approximately 57M posts from Scored, with at least 58 communities identified as migrating from Reddit and over 950 communities created since the platform's inception. Furthermore, we provide sentence embeddings of all posts in our dataset, generated through a state-of-the-art model, to further advance the field in characterizing the discussions within these communities. We aim to provide these resources to facilitate their investigations without the need for extensive data collection and processing efforts.

Alas, coordinated hate attacks, or raids, are becoming increasingly common online. In a nutshell, these are perpetrated by a group of aggressors who organize and coordinate operations on a platform (e.g., 4chan) to target victims on another community (e.g., YouTube). In this paper, we focus on attributing raids to their source community, paving the way for moderation approaches that take the context (and potentially the motivation) of an attack into consideration. We present TUBERAIDER, an attribution system achieving over 75% accuracy in detecting and attributing coordinated hate attacks on YouTube videos. We instantiate it using links to YouTube videos shared on 4chan's /pol/ board, r/The_Donald, and 16 Incels-related subreddits. We use a peak detector to identify a rise in the comment activity of a YouTube video, which signals that an attack may be occurring. We then train a machine learning classifier based on the community language (i.e., TF-IDF scores of relevant keywords) to perform the attribution. We test TUBERAIDER in the wild and present a few case studies of actual aggression attacks identified by it to showcase its effectiveness.

In this paper, we address the prevalent issue of account takeover (ATO) fraud, which significantly impacts businesses through stolen user information. Websites have adopted risk-based authentication, incorporating browser fingerprinting techniques to counteract this threat. However, attackers have adapted by using anti-detect browsers, referred to as fraud browsers, to spoof user information effectively. While traditional fingerprinting methods are capable of identifying fraud browsers, they encounter scalability and performance challenges in risk-based systems. To address these issues, we developed Browser Polygraph, an ML-based tool that applies coarse-grained privacy-preserving fingerprints to assess browser authenticity and assigns risk factors to suspicious sessions. Coarse-grained fingerprints, by design, cannot be used for user tracking but only for fraud detection purposes. Deployed at a major financial company, Browser Polygraph has flagged suspicious sessions, enabling more targeted identification of potential fraud, thus enhancing the company's ability to tackle ATO attempts.

Social media platforms offer unprecedented opportunities for connectivity and exchange of ideas; however, they also serve as fertile grounds for the dissemination of disinformation. Over the years, there has been a rise in state-sponsored campaigns aiming to spread disinformation and sway public opinion on sensitive topics through designated accounts, known as troll accounts. Past works on detecting accounts belonging to state-backed operations focus on a single campaign. While campaign-specific detection techniques are easier to build, there is no work done on developing systems that are campaign-agnostic and offer generalized detection of troll accounts unaffected by the biases of the specific campaign they belong to.

Reporting phishing attacks can significantly shorten the time required to take down their operations and deter further victimization by the same phishing websites. However, little research has been conducted to understand the phishing reporting ecosystem and its effectiveness. In this paper, we comprehensively evaluate the phishing reporting ecosystem to identify the critical challenges people face and their concerns when reporting smishing, vishing, and phishing email attacks. First, we analyze the existing security advice and channels for reporting phishing attacks in both the public and private sectors. Then, we conduct a scenario-based experiment involving 89 participants to investigate what factors affect a participant’s decision to report a phishing attack and what challenges they face in preparing the report. Third, we report phishing attacks ourselves and monitor the status of the reported phishing websites to empirically measure how reports are acted upon and how that affects the reported phishing websites. Finally, we propose approaches under five major concern categories to mitigate the challenges that we discover in the phishing reporting ecosystem.

Large Language Models (LLMs) have been suggested for use in automated vulnerability repair, but benchmarks showing they can consistently identify security-related bugs are lacking. We thus develop SecLLMHolmes, a fully automated evaluation framework that performs the most detailed investigation to date on whether LLMs can reliably identify and reason about security-related bugs. We construct a set of 228 code scenarios and analyze eight of the most capable LLMs across eight different investigative dimensions using our framework. Our evaluation shows LLMs provide non-deterministic responses, incorrect and unfaithful reasoning, and perform poorly in real-world scenarios. Most importantly, our findings reveal significant non-robustness in even the most advanced models like ‘PaLM2’ and ‘GPT-4’: by merely changing function or variable names, or by the addition of library functions in the source code, these models can yield incorrect answers in 26% and 17% of cases, respectively. These findings demonstrate that further LLM advances are needed before LLMs can be used as general purpose security assistants.

We conducted a study with 173 adolescents (ages 13-21), who self-reported their offline and online risk experiences and uploaded their Instagram data to our study website to flag private conversations as unsafe. Risk profiles were first created based on the survey data and then compared with the risk-flagged social media data. Five risk profiles emerged: Low Risks (51% of the participants), Medium Risks (29%), Increased Sexting (8%), Increased Self-Harm (8%), and High Risk Perpetration (4%). Overall, the profiles correlated well with the social media data with the highest level of risk occurring in the three smallest profiles. Youth who experienced increased sexting and self-harm frequently reported engaging in unsafe sexual conversations. Meanwhile, high risk perpetration was characterized by increased violence, threats, and sales/promotion of illegal activities. A key insight from our study was that offline risk behavior sometimes manifested differently in online contexts (i.e., offline self-harm as risky online sexual interactions). Our findings highlight the need for targeted risk prevention strategies for youth online safety.

Social media platforms have been establishing content moderation guidelines and employing various moderation policies to counter hate speech and misinformation. The goal of this paper is to study these community guidelines and moderation practices, as well as the relevant research publications, to identify the research gaps, differences in moderation techniques, and challenges that should be tackled by the social media platforms and the research community. To this end, we study and analyze fourteen most popular social media content moderation guidelines and practices, and consolidate them. We then introduce three taxonomies drawn from this analysis as well as covering over two hundred interdisciplinary research papers about moderation strategies. We identify the differences between the content moderation employed in mainstream and fringe social media platforms. Finally, we have in-depth applied discussions on both research and practical challenges and solutions.

Distributed tracing has become an essential tool to navigate performance of complex, distributed cloud-native applications, providing a comprehensive view of a request from end-to-end. However, the sheer amount of data generated by distributed tracing can be overwhelming, making it difficult to store, process, and extract meaningful insights. This paper presents the vision of ’nuffTrace that embodies a novel tree-based probabilistic data structure that summarizes trace data in a compact form without storing all of the data, enabling developers to analyze cloud application performance with high accuracy and efficiency.

Network telemetry systems are essential for monitoring network traffic and informing management decisions. However, increasing privacy concerns make user data access and analysis challenging for operators. We introduce PrvTel, a privacy-preserving telemetry system that uses an AutoEncoder model to encode user traffic data and preserve telemetry query ability with differential privacy guarantees. PrvTel features a lightweight model to be stored, facilitates quick training, and executes queries with minimal delay.

Previous research has documented the existence of both online echo chambers and hostile intergroup interactions. In this paper, we explore the relationship between these two phenomena by studying the activity of 5.97M Reddit users and 421M comments posted over 13 years. We examine whether users who are more engaged in echo chambers are more hostile when they comment on other communities. We then create a typology of relationships between political communities based on whether their users are toxic to each other, whether echo chamber-like engagement with these communities has a polarizing effect, and on the communities' political leanings. We observe both the echo chamber and hostile intergroup interaction phenomena, but neither holds universally across communities. Contrary to popular belief, we find that polarizing and toxic speech is more dominant between communities on the same, rather than opposing, sides of the political spectrum, especially on the left; however, this mostly points to the collective targeting of political outgroups.

To curb the problem of false information, social media platforms like Twitter started adding warning labels to content discussing debunked narratives, with the goal of providing more context to their audiences. Unfortunately, these labels are not applied uniformly and leave large amounts of false content unmoderated. This paper presents LAMBRETTA, a system that automatically identifies tweets that are candidates for soft moderation using Learning To Rank (LTR). We run Lambretta on Twitter data to moderate false claims related to the 2020 US Election and find that it flags over 20 times more tweets than Twitter, with only 3.93% false positives and 18.81% false negatives, outperforming alternative state-of-the-art methods based on keyword extraction and semantic search. Overall, LAMBRETTA assists human moderators in identifying and flagging false information on social media.

Instagram, one of the most popular social media platforms among youth, has recently come under scrutiny for potentially being harmful to the safety and well-being of our younger generations. Automated approaches for risk detection may be one way to help mitigate some of these risks if such algorithms are both accurate and contextual to the types of online harms youth face on social media platforms. However, the imminent switch by Instagram to end-to-end encryption for private conversations will limit the type of data that will be available to the platform to detect and mitigate such risks. In this paper, we investigate which indicators are most helpful in automatically detecting risk in Instagram private conversations, with an eye on high-level metadata, which will still be available in the scenario of end-to-end encryption. Toward this end, we collected Instagram data from 172 youth (ages 13-21) and asked them to identify private message conversations that made them feel uncomfortable or unsafe. Our participants risk-flagged 28,725 conversations that contained 4,181,970 direct messages, including textual posts and images. Based on this rich and multimodal dataset, we tested multiple feature sets (metadata, linguistic cues, and image features) and trained classifiers to detect risky conversations. Overall, we found that the metadata features (e.g., conversation length, a proxy for participant engagement) were the best predictors of risky conversations. However, for distinguishing between risk types, the different linguistic and media cues were the best predictors. Based on our findings, we provide design implications for AI risk detection systems in the presence of end-to-end encryption. More broadly, our work contributes to the literature on adolescent online safety by moving toward more robust solutions for risk detection that directly takes into account the lived risk experiences of youth.

We collected Instagram data from 150 adolescents (ages 13-21) that included 15,547 private message conversations of which 326 conversations were flagged as sexually risky by participants. Based on this data, we leveraged a human-centered machine learning approach to create sexual risk detection classifiers for youth social media conversations. Our Convolutional Neural Network (CNN) and Random Forest models outperformed in identifying sexual risks at the conversation-level (AUC=0.88), and CNN outperformed at the message-level (AUC=0.85). We also trained classifiers to detect the severity risk level (i.e., safe, low, medium-high) of a given message with CNN outperforming other models (AUC=0.88). A feature analysis yielded deeper insights into patterns found within sexually safe versus unsafe conversations. We found that contextual features (e.g., age, gender, and relationship type) and Linguistic Inquiry and Word Count (LIWC) contributed the most for accurately detecting sexual conversations that made youth feel uncomfortable or unsafe. Our analysis provides insights into the important factors and contextual features that enhance automated detection of sexual risks within youths' private conversations. As such, we make valuable contributions to the computational risk detection and adolescent online safety literature through our human-centered approach of collecting and ground truth coding private social media conversations of youth for the purpose of risk classification.

While COVID-19 text misinformation has already been investigated by various scholars, fewer research efforts have been devoted to characterizing and understanding COVID-19 misinformation that is carried out through visuals like photographs and memes. In this paper, we present a mixed-method analysis of image-based COVID-19 misinformation in 2020 on Twitter. We deploy a computational pipeline to identify COVID-19 related tweets, download the images contained in them, and group together visually similar images. We then develop a codebook to characterize COVID-19 misinformation and manually label images as misinformation or not. Finally, we perform a quantitative analysis of tweets containing COVID-19 misinformation images. We identify five types of COVID-19 misinformation, from a wrong understanding of the threat severity of COVID-19 to the promotion of fake cures and conspiracy theories. We also find that tweets containing COVID-19 misinformation images do not receive more interactions than baseline tweets with random images posted by the same set of users. As for temporal properties, COVID-19 misinformation images are shared for longer periods of time than non-misinformation ones, as well as have longer burst times. %\ywi added "have'' %\ywFor RQ2, we compare non-misinformation images instead of random images, and so it is not a direct comparison. When looking at the users sharing COVID-19 misinformation images on Twitter from the perspective of their political leanings, we find that pro-Democrat and pro-Republican users share a similar amount of tweets containing misleading or false COVID-19 images. However, the types of images that they share are different: while pro-Democrat users focus on misleading claims about the Trump administration's response to the pandemic, as well as often sharing manipulated images intended as satire, pro-Republican users often promote hydroxychloroquine, an ineffective medicine against COVID-19, as well as conspiracy theories about the origin of the virus. Our analysis sets a basis for better understanding COVID-19 misinformation images on social media and the nuances in effectively moderate them.

Malware affects millions of users worldwide, impacting the daily lives of many people as well as businesses. Malware infections are increasing in complexity and unfold over a number of stages. A malicious downloader often acts as the starting point as it fingerprints the victim’s machine and downloads one or more additional malware payloads. Although previous research was conducted on these malicious downloaders and their Pay-Per-Install networks, limited work has investigated how the profile of the victim machine, e.g., its characteristics and software configuration, affect the targeting choice of cybercriminals.

Modern defenses against cyberattacks increasingly rely on proactive approaches, e.g., to predict the adversary's next actions based on past events. Building accurate prediction models requires knowledge from many organizations; alas, this entails disclosing sensitive information, such as network structures, security postures, and policies, which might often be undesirable or outright impossible.

Previous security research efforts orbiting around graphs have been exclusively focusing on either (de-)anonymizing the graphs or understanding the security and privacy issues of graph neural networks. Little attention has been paid to understand the privacy risks of integrating the output from graph embedding models (e.g., node embeddings) with complex downstream machine learning pipelines. In this paper, we fill this gap and propose a novel model-agnostic graph recovery attack that exploits the implicit graph structural information preserved in the embeddings of graph nodes. We show that an adversary can recover edges with decent accuracy by only gaining access to the node embedding matrix of the original graph without interactions with the node embedding models. We demonstrate the effectiveness and applicability of our graph recovery attack through extensive experiments.

Chatbots are used in many applications, e.g., automated agents, smart home assistants, interactive characters in online games, etc. Therefore, it is crucial to ensure they do not behave in undesired manners, providing offensive or toxic responses to users. This is not a trivial task as state-of-the-art chatbot models are trained on large, public datasets openly collected from the Internet. This paper presents a first-of-its-kind, large-scale measurement of toxicity in chatbots. We show that publicly available chatbots are prone to providing toxic responses when fed toxic queries. Even more worryingly, some non-toxic queries can trigger toxic responses too. We then set out to design and experiment with an attack, ToxicBuddy, which relies on fine-tuning GPT-2 to generate non-toxic queries that make chatbots respond in a toxic manner. Our extensive experimental evaluation demonstrates that our attack is effective against public chatbot models and outperforms manually-crafted malicious queries proposed by previous work. We also evaluate three defense mechanisms against ToxicBuddy, showing that they either reduce the attack performance at the cost of affecting the chatbot's utility or are only effective at mitigating a portion of the attack. This highlights the need for more research from the computer security and online safety communities to ensure that chatbot models do not hurt their users. Overall, we are confident that ToxicBuddy can be used as an auditing tool and that our work will pave the way toward designing more effective defenses for chatbot safety.

We collected Instagram Direct Messages (DMs) from 100 adolescents and young adults (ages 13-21) who then flagged their own conversations as safe or unsafe. We performed a mixed-method analysis of the media files shared privately in these conversations to gain human-centered insights into the risky interactions experienced by youth. Unsafe conversations ranged from unwanted sexual solicitations to mental health related concerns, and images shared in unsafe conversations tended to be of people and convey negative emotions, while those shared in regular conversations more often conveyed positive emotions and contained objects. Further, unsafe conversations were significantly shorter, suggesting that youth disengaged when they felt unsafe. Our work uncovers salient characteristics of safe and unsafe media shared in private conversations and provides the foundation to develop automated systems for online risk detection and mitigation.

Growing evidence points to recurring influence campaigns on social media, often sponsored by state actors aiming to manipulate public opinion on sensitive political topics. Typically, campaigns are performed through instrumented accounts, known as troll accounts; despite their prominence, however, little work has been done to detect these accounts in the wild. In this paper, we present TROLLMAGNIFIER, a detection system for troll accounts. Our key observation, based on analysis of known Russian-sponsored troll accounts identified by Reddit, is that they show loose coordination, often interacting with each other to further specific narratives. Therefore, troll accounts controlled by the same actor often show similarities that can be leveraged for detection. TROLLMAGNIFIER learns the typical behavior of known troll accounts and identifies more that behave similarly. We train TROLLMAGNIFIER on a set of 335 known troll accounts and run it on a large dataset of Reddit accounts. Our system identifies 1,248 potential troll accounts; we then provide a multi-faceted analysis to corroborate the correctness of our classification. In particular, 66% of the detected accounts show signs of being instrumented by malicious actors (e.g., they were created on the same exact day as a known troll, they have since been suspended by Reddit, etc.). They also discuss similar topics as the known troll accounts and exhibit temporal synchronization in their activity. Overall, we show that using TROLLMAGNIFIER, one can grow the initial knowledge of potential trolls provided by Reddit by over 300%.

Despite the growing threat posed by the Android malware, the research community is still lacking a comprehensive view of common behaviors and emerging trends in malware families active on the platform. Without such view, researchers incur the risk of developing systems that only detect outdated threats, missing the most recent ones. In this article, we conduct the largest measurement of Android malware behavior to date, analyzing over 1.2 million malware samples that belong to 1.28K families over a period of eight years (from 2010 to 2017). We aim at understanding how Android malware has evolved over time, focusing onrepackagingmalware. In this type of threat different innocuous apps are piggybacked with a malicious payload (rider), allowing inexpensive malware manufacturing. One of the main challenges posed when studying repackaged malware is slicing the app to split benign components apart from the malicious ones. To address this problem, we use differential analysis to isolate software components that are irrelevant to the campaign and study the behavior of malicious riders alone. Our analysis framework relies on collective repositories and recent advances on the systematization of intelligence extracted from multiple anti-virus vendors. We find that since its infancy in 2010, the Android malware ecosystem has changed significantly, both in the type of malicious activity performed by malware and in the level of obfuscation used to avoid detection. Finally, we discuss what our findings mean for Android malware detection research, highlighting areas that need further attention by the research community. In particular, we show that riders of malware families evolve over time. This evidences important experimental bias in research works levering on automated systems for family identification without considering variants.

The news ecosystem encompasses a wide range of sources with varying levels of trustworthiness, and with public commentary giving different spins to the same stories. In this paper, we present a measurement pipeline able to identify news articles that discuss the same story and trace how they are shared on multiple online communities. We compile a list of 1,073 news websites and extract posts from four Web communities (Twitter, Reddit, 4chan, and Gab) that contain URLs from these sources. This yields a dataset of 38M posts containing 15.6M unique news URLs, spanning almost three years. We study the data along several axes, assessing the trustworthiness of shared news stories, analyzing how they are discussed, and measuring the influence various Web communities have in that. Our analysis shows that different communities discuss different types of news, with polarized communities like Gab and /r/The_Donald subreddit disproportionately referencing untrustworthy sources. We also find t hat f ringe c ommunities o ften h ave a disproportionate influence o n o ther p latforms w .r.t. p ushing n arratives around certain news, for example, about political elections, immigration, or foreign policy. In fact, fringe communities are seemingly successful in influencing the discussion on false narratives about news events on mainstream social networks.

Android's security model severely limits the capabilities of anti-malware software. Unlike commodity anti-malware solutions on desktop systems, their Android counterparts run as sandboxed applications without root privileges and are limited by Android's permission system. As such, PHAs on Android are usually willingly installed by victims, as they come disguised as useful applications with hidden malicious functionality, and are encountered on mobile app stores as suggestions based on the apps that a user previously installed. Users with similar interests and app installation history are likely to be exposed and to decide to install the same PHA. This observation gives us the opportunity to develop predictive approaches that can warn the user about which PHAs they will encounter and potentially be tempted to install in the near future. These approaches could then be used to complement commodity anti-malware solutions, which are focused on post-fact detection, closing the window of opportunity that existing solutions suffer from. In this paper we develop ANDRuspex, a system based on graph representation learning, allowing us to learn latent relationships between user devices and PHAs and leverage them for prediction. We test ANDRuspex on a real world dataset of PHA installations collected by a security company, and show that our approach achieves very high prediction results (up to 0.994 TPR at 0.0001 FPR), while at the same time outperforming alternative baseline methods. We also demonstrate that ANDRuspex is robust and its runtime performance is acceptable for a real world deployment.

The Android platform gives mobile device users the opportunity to extend the capabilities of their systems by installing developer-authored apps. Companies leverage this capability to reach their customers and conduct business operations such as financial transactions. End-users can obtain custom Android applications (apps) from the Google Play, some of which are security-sensitive due to the nature of the data that they handle, such as apps from the FINANCE category. Although there are recommendations and standardized guidelines for secure app development with various self-defense techniques, the adoption of such methods is not mandatory and is left to the discretion of developers. Unfortunately, malicious actors can tamper with the app runtime environment and then exploit the attack vectors which arise from the tampering, such as executing foreign code with elevated privileges on the mobile platform. In this paper, we present AppJITSU, a dynamic app analysis framework that evaluates the resiliency of security-critical apps. We exercise the most popular 455 financial apps in attack-specific hostile environments to demonstrate the current state of resiliency against known tampering methods. Our results indicate that 25.05% of the tested apps have no resiliency against any common hostile methods or tools, whereas only 10.77% employed all defensive methods.

The malware and botnet phenomenon is among the most significant threats to cybersecurity today. Consequently, law enforcement agencies, security companies, and researchers are constantly seeking to disrupt these malicious operations through so-called takedown counter-operations. Unfortunately, the success of these takedowns is mixed. Furthermore, very little is understood as to how botnets and malware delivery operations respond to takedown attempts. We present a comprehensive study of three malware delivery operations that were targeted for takedown in 2015–16 using global download metadata provided by Symantec. In summary, we found that: (1) Distributed delivery architectures were commonly used, indicating the need for better security hygiene and coordination by the (ab)used service providers. (2) A minority of malware binaries were responsible for the majority of download activity, suggesting that detecting these “super binaries” would yield the most benefit to the security community. (3) The malware operations exhibited displacing and defiant behaviours following their respective takedown attempts. We argue that these “predictable” behaviours could be factored into future takedown strategies. (4) The malware operations also exhibited previously undocumented behaviours, such as Dridex dropping competing brands of malware, or Dorkbot and Upatre heavily relying on upstream dropper malware. These “unpredictable” behaviours indicate the need for researchers to use better threat-monitoring techniques.

We argue that existing security, privacy, and antiabuse protections fail to address the growing threat of online hate and harassment. In order for our community to understand and address this gap, we propose a taxonomy for reasoning about online hate and harassment. Our taxonomy draws on over 150 interdisciplinary research papers that cover disparate threats ranging from intimate partner violence to coordinated mobs. In the process, we identify seven classes of attacks—such as toxic content and surveillance—that each stem from different attacker capabilities and intents. We also provide longitudinal evidence from a three-year survey that hate and harassment is a pervasive, growing experience for online users, particularly for at-risk communities like young adults and people who identify as LGBTQ+. Responding to each class of hate and harassment requires a unique strategy and we highlight five such potential research directions that ultimately empower individuals, communities, and platforms to do so.

Online fringe communities offer fertile grounds to users seeking and sharing ideas fueling suspicion of mainstream news and conspiracy theories. Among these, the QAnon conspiracy theory emerged in 2017 on 4chan, broadly supporting the idea that powerful politicians, aristocrats, and celebrities are closely engaged in a global pedophile ring. Simultaneously, governments are thought to be controlled by “puppet masters,” as democratically elected officials serve as a fake showroom of democracy.

The outbreak of the COVID-19 pandemic has changed our lives in unprecedented ways. In the face of the projected catastrophic consequences, most countries have enacted social distancing measures in an attempt to limit the spread of the virus. Under these conditions, the Web has become an indispensable medium for information acquisition, communication, and entertainment. At the same time, unfortunately, the Web is being exploited for the dissemination of potentially harmful and disturbing content, such as the spread of conspiracy theories and hateful speech towards specific ethnic groups, in particular towards Chinese people and people of Asian descent since COVID-19 is believed to have originated from China.

While the Internet has dramatically increased the exposure that research can receive, it has also facilitated harassment against scholars. To understand the impact that these attacks can have on the work of researchers, we perform a series of systematic interviews with researchers including academics, journalists, and activists, who have experienced targeted, Internet-facilitated harassment. We provide a framework for understanding the types of harassers that target researchers, the harassment that ensues, and the personal and professional impact on individuals and academic freedom. We then study preventative and remedial strategies available, and the institutions that prevent some of these strategies from being more effective. Finally, we discuss the ethical structures that could facilitate more equitable access to participating in research without serious personal suffering.

Cyberbullying is a growing problem across social media platforms, inflicting short and long-lasting effects on victims. To mitigate this problem, research has looked into building automated systems, powered by machine learning, to detect cyberbullying incidents, or the involved actors like victims and perpetrators. In the past, systematic reviews have examined the approaches within this growing body of work, but with a focus on the computational aspects of the technical innovation, feature engineering, or performance optimization, without centering around the roles, beliefs, desires, or expectations of humans. In this paper, we present a human-centered systematic literature review of the past 10 years of research on automated cyberbullying detection. We analyzed 56 papers based on a three-prong human-centeredness algorithm design framework - spanning theoretical, participatory, and speculative design. We found that the past literature fell short of incorporating human-centeredness across multiple aspects, ranging from defining cyberbullying, establishing the ground truth in data annotation, evaluating the performance of the detection models, to speculating the usage and users of the models, including potential harms and negative consequences. Given the sensitivities of the cyberbullying experience and the deep ramifications cyberbullying incidents bear on the involved actors, we discuss takeaways on how incorporating human-centeredness in future research can aid with developing detection systems that are more practical, useful, and tuned to the diverse needs and contexts of the stakeholders.

Despite the increasingly important role played by image memes, we do not yet have a solid understanding of the elements that might make a meme go viral on social media. In this paper, we investigate what visual elements distinguish image memes that are highly viral on social media from those that do not get re-shared, across three dimensions: composition, subjects, and target audience. Drawing from research in art theory, psychology, marketing, and neuroscience, we develop a codebook to characterize image memes, and use it to annotate a set of 100 image memes collected from 4chan's Politically Incorrect Board (/pol/). On the one hand, we find that highly viral memes are more likely to use a close-up scale, contain characters, and include positive or negative emotions. On the other hand, image memes that do not present a clear subject the viewer can focus attention on, or that include long text are not likely to be re-shared by users. We train machine learning models to distinguish between image memes that are likely to go viral and those that are unlikely to be re-shared, obtaining an AUC of 0.866 on our dataset. We also show that the indicators of virality identified by our model can help characterize the most viral memes posted on mainstream online social networks too, as our classifiers are able to predict 19 out of the 20 most popular image memes posted on Twitter and Reddit between 2016 and 2018. Overall, our analysis sheds light on what indicators characterize viral and non-viral visual content online, and set the basis for developing better techniques to create or moderate content that is more likely to catch the viewer's attention.

YouTube is by far the largest host of user-generated video content worldwide. Alas, the platform has also come under fire for hosting inappropriate, toxic, and hateful content. One community that has often been linked to sharing and publishing hateful and misogynistic content are the Involuntary Celibates (Incels), a loosely defined movement ostensibly focusing on men's issues. In this paper, we set out to analyze the Incel community on YouTube by focusing on this community's evolution over the last decade and understanding whether YouTube's recommendation algorithm steers users towards Incel-related videos. We collect videos shared on Incel communities within Reddit and perform a data-driven characterization of the content posted on YouTube. Among other things, we find that the Incel community on YouTube is getting traction and that, during the last decade, the number of Incel-related videos and comments rose substantially. We also find that users have a 6.3% chance of being suggested an Incel-related video by YouTube's recommendation algorithm within five hops when starting from a non Incel-related video. Overall, our findings paint an alarming picture of online radicalization: not only Incel activity is increasing over time, but platforms may also play an active role in steering users towards such extreme content.

In the era of big data and artificial intelligence, online risk detection has become a popular research topic. From detecting online harassment to the sexual predation of youth, the state-of-the-art in computational risk detection has the potential to protect particularly vulnerable populations from online victimization. Yet, this is a high-risk, high-reward endeavor that requires a systematic and human-centered approach to synthesize disparate bodies of research across different application domains, so that we can identify best practices, potential gaps, and set a strategic research agenda for leveraging these approaches in a way that betters society. Therefore, we conducted a comprehensive literature review to analyze 73 peer-reviewed articles on computational approaches utilizing text or meta-data/multimedia for online sexual risk detection. We identified sexual grooming (75%), sex trafficking (12%), and sexual harassment and/or abuse (12%) as the three types of sexual risk detection present in the extant literature. Furthermore, we found that the majority (93%) of this work has focused on identifying sexual predators after-the-fact, rather than taking more nuanced approaches to identify potential victims and problematic patterns that could be used to prevent victimization before it occurs. Many studies rely on public datasets (82%) and third-party annotators (33%) to establish ground truth and train their algorithms. Finally, the majority of this work (78%) mostly focused on algorithmic performance evaluation of their model and rarely (4%) evaluate these systems with real users. Thus, we urge computational risk detection researchers to integrate more human-centered approaches to both developing and evaluating sexual risk detection algorithms to ensure the broader societal impacts of this important work.

When toxic online communities on mainstream platforms face moderation measures, such as bans, they may migrate to other platforms with laxer policies or set up their own dedicated websites. Previous work suggests that within mainstream platforms, community-level moderation is effective in mitigating the harm caused by the moderated communities. It is, however, unclear whether these results also hold when considering the broader Web ecosystem. Do toxic communities continue to grow in terms of their user base and activity on the new platforms? Do their members become more toxic and ideologically radicalized? In this paper, we report the results of a large-scale observational study of how problematic online communities progress following community-level moderation measures. We analyze data from r/The_Donald and r/Incels, two communities that were banned from Reddit and subsequently migrated to their own standalone websites. Our results suggest that, in both cases, moderation measures significantly decreased posting activity on the new platform, reducing the number of posts, active users, and newcomers. In spite of that, users in one of the studied communities (r/The_Donald) showed increases in signals associated with toxicity and radicalization, which justifies concerns that the reduction in activity may come at the expense of a more toxic and radical community. Overall, our results paint a nuanced portrait of the consequences of community-level moderation and can inform their design and deployment.

Botnets continue to be a threat to organizations, thus various machine learning-based botnet detectors have been proposed. However, the capability of such systems in detecting new or unseen botnets is crucial to ensure its robustness against the rapid evolution of botnets. Moreover, it prolongs the effectiveness of the system in detecting bots, avoiding frequent and time-consuming classifier re-training. We present BOTection, a privacy-preserving bot detection system that models the bot network flow behavior as a Markov Chain. The Markov Chain state transitions capture the bots' network behavior using high-level flow features as states, producing content-agnostic and encryption resilient behavioral features. These features are used to train a classifier to first detect flows produced by bots, and then identify their bot families. We evaluate our system on a dataset of over 7M malicious flows from 12 botnet families, showing its capability of detecting bots' network traffic with 99.78% F-measure and classifying it to a malware family with a 99.09% F-measure. Notably, due to the modeling of general bot network behavior by the Markov Chains, BOTection can detect traffic belonging to unseen bot families with an F-measure of 93.03% making it robust against malware evolution.

Android applications (apps) are a combination of code written by the developers as well as third-party libraries that carry out most commonly used functionalities such as advertisement and payments. Running apps in a monitoring environment allows researchers to measure how much network traffic is exchanged between an app and remote endpoints. However, current systems currently do not have the ability to reliably distinguish traffic that is generated by different libraries. This is important, because while mobile users are paying for data traffic without distinctions, some of this traffic is useful (e.g., data for core app functionalities), whereas the rest of the traffic can be considered a nuisance (e.g., excessive advertisements). In this paper, we present Libspector, a system that precisely attributes network traffic coming from an Android app to the library that generated it. To this end, we instrument the Android Framework to inspect the network connections initiated by apps, provide fine-grained information on the libraries in use, and calculate method coverage information while performing dynamic analysis. We then perform a measurement on 25,000 popular Android apps and investigate the relation between different categories of apps with the use of specific libraries. We analyze the method coverage of our dynamic analysis method, and further characterize the endpoint connections established by the Android apps. Our results indicate that advertisement libraries account for over a quarter of the total data transmission. We further observe that there is no strict 1-to-1 correlation between the similar categories of network endpoints and libraries which initiated the data transfer.

Online romance scams are a prevalent form of mass-marketing fraud in the West, and yet few studies have presented data-driven responses to this problem. In this type of scam, fraudsters craft fake profiles and manually interact with their victims. Because of the characteristics of this type of fraud and how dating sites operate, traditional detection methods (e.g., those used in spam filtering) are ineffective. In this paper, we investigate the archetype of online dating profiles used in this form of fraud, including their use of demographics, profile descriptions, and images, shedding light on both the strategies deployed by scammers to appeal to victims and the traits of victims themselves. Furthermore, in response to the severe financial and psychological harm caused by dating fraud, we develop a system to detect romance scammers on online dating platforms. This paper presents the first fully described system for automatically detecting this fraud. Our aim is to provide an early detection system to stop romance scammers as they create fraudulent profiles or before they engage with potential victims. Previous research has indicated that the victims of romance scams score highly on scales for idealized romantic beliefs. We combine a range of structured, unstructured, and deep-learned features that capture these beliefs in order to build a detection system. Our ensemble machine-learning approach is robust to the omission of profile details and performs at high accuracy (97%) in a hold-out validation set. The system enables development of automated tools for dating site providers and individual users.

We present a longitudinal measurement of malicious file distribution on the Web. Following a data-driven approach, we identify network infrastructures and the files that they download. We then study their characteristics over a short period (one day), over a medium period (daily, over one month) as well as in the long term (weekly, over one year). This analysis offers us an unprecedented view of the malicious file delivery ecosystem and its dynamics. We find that the malicious file delivery landscape can be divided into two distinct ecosystems: a much larger, tightly connected set of networks that is mostly responsible for the delivery of potentially unwanted programs (PUP), and a number of disjoint network infrastructures that are responsible for delivering malware on victim computers. We find that these two ecosystems are mostly disjoint, but it is not uncommon to see malware downloaded from the PUP Ecosystem, and vice versa. We estimate the proportions of PUP-to-malware in the wild to be heavily skewed towards PUP (17:2) and compare their distribution patterns. We observe periodicity in the activity of malicious network infrastructures, and we find that although malicious file operations present a high degree of volatility, 75% of the observed malicious networks remain active for more than six weeks, with 26% surviving for an entire year. We then reason on how our findings can help the research and law enforcement communities in developing better takedown techniques.

Assigning family labels to malicious apps is a common practice for grouping together malware with identical behavior. However, recent studies show that apps labeled as belonging to the same family do not necessarily behave similarly: one app may lack or have extra capabilities compared to others in the same family, and, conversely, two apps labeled as belonging to different families may exhibit close behavior. To reveal these inconsistencies, this paper presents AndrEnsemble, a characterization system for Android malware families based on ensembles of sensitive API calls extracted from aggregated call graphs of different families. Our method has several advantages over similar characterization approaches, including a greater reduction ratio with respect to original call graphs, robustness against transformation attacks, and flexibility to be applied at different granularity levels. We experimentally validate our approach and discuss three specific use cases: mobile ransomware, SMS Trojans and banking Trojans. This left us with some interesting findings. First of all, malicious operations in these types of malware are not necessarily exercised by using several sensitive API calls all together. Second, SMS Trojans have larger ensembles of API calls compared to the other types. Last but not least, we identified several samples with identical ensembles though being labeled as part of different families.

Companies adopt Bring Your Own Device (BYOD) policies extensively, for both convenience and cost management. The compelling way of putting private and business related applications (apps) on the same device leads to the widespread usage of employee owned devices to access sensitive company data and services. Such practices create a security risk as a legitimate app may send business-sensitive data to third party servers through detrimental app functions or packaged libraries. In this paper, we propose BorderPatrol, a system for extracting contextual data that businesses can leverage to enforce access control in BYOD-enabled corporate networks through fine-grained policies. BorderPatrol extracts contextual information, which is the stack trace of the app function that generated the network traffic, on provisioned user devices and transfers this data in IP headers to enforce desired policies at network routers. BorderPatrol provides a way to selectively prevent undesired functionalities, such as analytics activities or advertisements, and help enforce information dissemination policies of the company while leaving other functions of the app intact. Using 2,000 apps, we demonstrate that BorderPatrol is effective in preventing packets which originate from previously identified analytics and advertisement libraries from leaving the network premises. In addition, we show BorderPatrol's capability in selectively preventing undesirable app functions using case studies.

Smartphones are increasingly augmented with sensors for a variety of purposes. In this paper, we show how magnetic field emissions can be used to fingerprint smartphones. Previous work on identification rely on specific characteristics that vary with the settings and components available on a device. This limits the number of devices on which one approach is effective. By contrast, all electronic devices emit a magnetic field which is accessible either through the API or measured through an external device.

Under increasing scrutiny, many web companies now offer bespoke mechanisms allowing any third party to file complaints (e.g., requesting the de-listing of a URL from a search engine). While this self-regulation might be a valuable web governance tool, it places huge responsibility within the hands of these organisations that demands close examination. We present the first large-scale study of web complaints (over 1 billion URLs). We find a range of complainants, largely focused on copyright enforcement. Whereas the majority of organisations are occasional users of the complaint system, we find a number of bulk senders specialised in targeting specific types of domain. We identify a series of trends and patterns amongst both the domains and complainants. By inspecting the availability of the domains, we also observe that a sizeable portion go offline shortly after complaints are generated. This paper sheds critical light on how complaints are issued, who they pertain to and which domains go offline after complaints are issued.

A common approach when setting up a website is to utilize third party Web hosting and content delivery networks. Without taking this trend into account, any measurement study inspecting the deployment and operation of websites can be heavily skewed. Unfortunately, the research community lacks generalizable tools that can be used to identify how and where a given website is hosted. Instead, a number of ad hoc techniques have emerged, e.g., using Autonomous System databases, domain prefixes for CNAME records. In this work we propose Pythia , a novel lightweight approach for identifying Web content hosted on third-party infrastructures, including both traditional Web hosts and content delivery networks. Our framework identifies the organization to which a given Web page belongs, and it detects which Web servers are self-hosted and which ones leverage third-party services to provide contents. To test our framework we run it on 40,000 URLs and evaluate its accuracy, both by comparing the results with similar services and with a manually validated groundtruth. Our tool achieves an accuracy of 90% and detects that under 11% of popular domains are self-hosted. We publicly release our tool to allow other researchers to reproduce our findings, and to apply it to their own studies.

Video sharing platforms like YouTube are increasingly targeted by aggression and hate attacks. Prior work has shown how these attacks often take place as a result of "raids," i.e., organized efforts by ad-hoc mobs coordinating from third-party communities. Despite the increasing relevance of this phenomenon, however, online services often lack effective countermeasures to mitigate it. Unlike well-studied problems like spam and phishing, coordinated aggressive behavior both targets and is perpetrated by humans, making defense mechanisms that look for automated activity unsuitable. Therefore, the de-facto solution is to reactively rely on user reports and human moderation. In this paper, we propose an automated solution to identify YouTube videos that are likely to be targeted by coordinated harassers from fringe communities like 4chan. First, we characterize and model YouTube videos along several axes (metadata, audio transcripts, thumbnails) based on a ground truth dataset of videos that were targeted by raids. Then, we use an ensemble of classifiers to determine the likelihood that a video will be raided with very good results (AUC up to 94%). Overall, our work provides an important first step towards deploying proactive systems to detect and mitigate coordinated hate attacks on platforms like YouTube.

As Android has become increasingly popular, so has malware targeting it, thus motivating the research community to propose different detection techniques. However, the constant evolution of the Android ecosystem, and of malware itself, makes it hard to design robust tools that can operate for long periods of time without the need for modifications or costly re-training. Aiming to address this issue, we set to detect malware from a behavioral point of view, modeled as the sequence of abstracted API calls. We introduce M A M A D ROID , a static-analysis-based system that abstracts app’s API calls to their class, package, or family, and builds a model from their sequences obtained from the call graph of an app as Markov chains. This ensures that the model is more resilient to API changes and the features set is of manageable size. We evaluate M A M A D ROID using a dataset of 8.5K benign and 35.5K malicious apps collected over a period of 6 years, showing that it effectively detects malware (with up to 0.99 F-measure) and keeps its detection capabilities for long periods of time (up to 0.87 F-measure 2 years after training). We also show that M A M A D ROID remarkably overperforms D ROID APIM INER , a state-of-the-art detection system that relies on the frequency of ( raw ) API calls. Aiming to assess whether M A M A D ROID ’s effectiveness mainly stems from the API abstraction or from the sequencing modeling, we also evaluate a variant of it that uses frequency (instead of sequences), of abstracted API calls. We find that it is not as accurate, failing to capture maliciousness when trained on malware samples that include API calls that are equally or more frequently used by benign apps.

Cyberbullying and cyberaggression are increasingly worrisome phenomena affecting people across all demographics. More than half of young social media users worldwide have been exposed to such prolonged and/or coordinated digital harassment. Victims can experience a wide range of emotions, with negative consequences such as embarrassment, depression, isolation from other community members, which embed the risk to lead to even more critical consequences, such as suicide attempts. In this work, we take the first concrete steps to understand the characteristics of abusive behavior in Twitter, one of today’s largest social media platforms. We analyze 1.2 million users and 2.1 million tweets, comparing users participating in discussions around seemingly normal topics like the NBA, to those more likely to be hate-related, such as the Gamergate controversy, or the gender pay inequality at the BBC station. We also explore specific manifestations of abusive behavior, i.e., cyberbullying and cyberaggression, in one of the hate-related communities (Gamergate). We present a robust methodology to distinguish bullies and aggressors from normal Twitter users by considering text, user, and network-based attributes. Using various state-of-the-art machine-learning algorithms, we classify these accounts with over 90% accuracy and AUC. Finally, we discuss the current status of Twitter user accounts marked as abusive by our methodology and study the performance of potential mechanisms that can be used by Twitter to suspend users in the future.

Botnets in online social networks are increasingly often affecting the regular flow of discussion, attacking regular users and their posts, spamming them with irrelevant or offensive content, and even manipulating the popularity of messages and accounts. Researchers and cybercriminals are involved in an arms race, and new and updated botnets designed to defeat current detection systems are constantly developed, rendering such detection systems obsolete.

With the increased complexity of modern computer attacks, there is a need for defenders not only to detect malicious activity as it happens, but also to predict the specific steps that will be taken by an adversary when performing an attack. However this is still an open research problem, and previous research in predicting malicious events only looked at binary outcomes (eg. whether an attack would happen or not), but not at the specific steps that an attacker would undertake. To fill this gap we present Tiresias xspace, a system that leverages Recurrent Neural Networks (RNNs) to predict future events on a machine, based on previous observations. We test Tiresias xspace on a dataset of 3.4 billion security events collected from a commercial intrusion prevention system, and show that our approach is effective in predicting the next event that will occur on a machine with a precision of up to 0.93. We also show that the models learned by Tiresias xspace are reasonably stable over time, and provide a mechanism that can identify sudden drops in precision and trigger a retraining of the system. Finally, we show that the long-term memory typical of RNNs is key in performing event prediction, rendering simpler methods not up to the task.

Following the increasing popularity of the mobile ecosystem, cybercriminals have increasingly targeted mobile ecosystems, designing and distributing malicious apps that steal information or cause harm to the device's owner. Aiming to counter them, detection techniques based on either static or dynamic analysis that model Android malware, have been proposed. While the pros and cons of these analysis techniques are known, they are usually compared in the context of their limitations e.g., static analysis is not able to capture runtime behaviors, full code coverage is usually not achieved during dynamic analysis, etc. Whereas, in this paper, we analyze the performance of static and dynamic analysis methods in the detection of Android malware and attempt to compare them in terms of their detection performance, using the same modeling approach.To this end, we build on MAMADROID, a state-of-the-art detection system that relies on static analysis to create a behavioral model from the sequences of abstracted API calls. Then, aiming to apply the same technique in a dynamic analysis setting, we modify CHIMP, a platform recently proposed to crowdsource human inputs for app testing, in order to extract API calls' sequences from the traces produced while executing the app on a CHIMP virtual device. We call this system AUNTIEDROID and instantiate it by using both automated (Monkey) and usergenerated inputs. We find that combining both static and dynamic analysis yields the best performance, with $F -$measure reaching 0.92. We also show that static analysis is at least as effective as dynamic analysis, depending on how apps are stimulated during execution, and investigate the reasons for inconsistent misclassifications across methods.

While there is an increasing flow of media stories reporting cases of cyberbullying, particularly within online social media, research efforts in the academic community are scattered over different topics across the social science and computer science academic disciplines. In this work, we explored research pertaining to cyberbullying, conducted across disciplines. We mainly sought to understand scholarly activity on intelligence techniques for the detection of cyberbullying when it occurs. Our findings suggest that the vast majority of academic contributions on cyberbullying focus on understanding the phenomenon, risk factors, and threats, with the prospect of suggesting possible protection strategies. There is less work on intelligence techniques for the detection of cyberbullying when it occurs, while currently deployed algorithms seem to detect the problem only up to some degree of success. The article summarises the current trends aiming to encourage discussion and research with a new scope; we call for more research tackling the problem by leveraging statistical models and computational mechanisms geared to detect, intervene, and prevent cyberbullying. Coupling intelligence techniques with specific web technology problems can help combat this social menace. We argue that a multidisciplinary approach is needed, with expertise from human–computer interaction, psychology, computer science, and sociology, for current challenges to be addressed and significant progress to be made.

Effective malware detection approaches need not only high accuracy, but also need to be robust to changes in the modus operandi of criminals. In this paper, we propose Marmite, a feature-agnostic system that aims at propagating known malicious reputation of certain files to unknown ones with the goal of detecting malware. Marmite does this by looking at a graph that encapsulates a comprehensive view of how files are downloaded (by which hosts and from which servers) on a global scale. The reputation of files is then propagated across the graph using semi-supervised label propagation with Bayesian confidence. We show that Marmite is able to reach high accuracy (0.94 G-mean on average) over a 10-day dataset of 200 million download events. We also demonstrate that Marmite's detection capabilities do not significantly degrade over time, by testing our system on a 30-day dataset of 660 million download events collected six months after the system was tuned and validated. Marmite still maintains a similar accuracy after this period of time.

Web browsers have become the predominant means for developing and deploying applications, and thus they often handle sensitive data such as social interactions or financial credentials and information. As a consequence, defensive measures such as TLS, the Same-Origin Policy (SOP), and Content Security Policy (CSP) are critical for ensuring that sensitive data remains in trusted hands.

Similar to criminals in the physical world, cyber-criminals use a variety of illegal and immoral means to achieve monetary gains. Recently, malware known as ransomware started to leverage strong cryptographic primitives to hold victims' computer files "hostage" until a ransom is paid. Victims, with no way to defend themselves, are often advised to simply pay. Existing defenses against ransomware rely on ad-hoc mitigations that target the incorrect use of cryptography rather than generic live protection. To fill this gap in the defender's arsenal, we describe the approach, prototype implementation, and evaluation of a novel, automated, and most importantly proactive defense mechanism against ransomware. Our prototype, called PayBreak, effectively combats ransomware, and keeps victims' files safe.

Cybercriminals have found in online social networks a propitious medium to spread spam and malicious content. Existing techniques for detecting spam include predicting the trustworthiness of accounts and analyzing the content of these messages. However, advanced attackers can still successfully evade these defenses.

As the number and the diversity of news outlets on the Web grows, so does the opportunity for "alternative" sources of information to emerge. Using large social networks like Twitter and Facebook, misleading, false, or agenda-driven information can quickly and seamlessly spread online, deceiving people or influencing their opinions. Also, the increased engagement of tightly knit communities, such as Reddit and 4chan, further compounds the problem, as their users initiate and propagate alternative information, not only within their own communities, but also to different ones as well as various social media. In fact, these platforms have become an important piece of the modern information ecosystem, which, thus far, has not been studied as a whole.

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.

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.

This work uses statistical classification techniques to learn about the different network behavior patterns demonstrated by targeted malware and generic malware. Targeted malware is a recent type of threat, involving bespoke software that has been created to target a specific victim. It is considered a more dangerous threat than generic malware, because a targeted attack can cause more serious damage to the victim. Our work aims to automatically distinguish between the network activity generated by the two types of malware, which then allows samples of malware to be classified as being either targeted or generic. For a network administrator, such knowledge can be important because it assists to understand which threats require particular attention. Because a network administrator usually manages more than an alarm simultaneously, the aim of the work is particularly relevant. We set up a sandbox and infected virtual machines with malware, recording all resulting malware activity on the network. Using the network packets produced by the malware samples, we extract features to classify their behavior. Before performing classification, we carefully analyze the features and the dataset to study all their details and gain a deeper understanding of the malware under study. Our use of statistical classifiers is shown to give excellent results in some cases, where we achieved an accuracy of almost 96% in distinguishing between the two types of malware. We can conclude that the network behaviors of the two types of malicious code are very different.

Credit card fraud has seen rampant increase in the past years, as customers use credit cards and similar financial instruments frequently. Both online and brick-and-mortar outfits repeatedly fall victim to cybercriminals who siphon off credit card information in bulk. Despite the many and creative ways that attackers use to steal and trade credit card information, the stolen information can rarely be used to withdraw money directly, due to protection mechanisms such as PINs and cash advance limits. As such, cybercriminals have had to devise more advanced monetization schemes to work around the current restrictions. One monetization scheme that has been steadily gaining traction are reshipping scams. In such scams, cybercriminals purchase high-value or highly-demanded products from online merchants using stolen payment instruments, and then ship the items to a credulous citizen. This person, who has been recruited by the scammer under the guise of "work-from-home" opportunities, then forwards the received products to the cybercriminals, most of whom are located overseas. Once the goods reach the cybercriminals, they are then resold on the black market for an illicit profit. Due to the intricacies of this kind of scam, it is exceedingly difficult to trace, stop, and return shipments, which is why reshipping scams have become a common means for miscreants to turn stolen credit cards into cash.

A spammer needs three elements to run a spam operation: a list of victim email addresses, content to be sent, and a botnet to send it. Each of these three elements are critical for the success of the spam operation: a good email list should be composed of valid email addresses, a good email content should be both convincing to the reader and evades anti-spam filters, and a good botnet should efficiently sent spam. Given how critical these three elements are, figures specialized on one of these elements have emerged in the spam ecosystem. Email harvesters crawl the web and compile email lists, botmasters infect victim computers and maintain efficient botnets for spam dissemination, and spammers rent botnets and buy email lists to run spam campaigns. Previous research suggested that email harvesters and botmasters sell their services to spammers in a prosperous underground economy. No rigorous research has been performed, however, on understanding the relations between these three actors. This paper aims to shed some light on the relations between harvesters, botmasters, and spammers. By disseminating email addresses on the Internet, fingerprinting the botnets that contact these addresses, and looking at the content of these emails, we can infer the relations between the actors involved in the spam ecosystem. Our observations can be used by researchers to develop more effective anti-spam systems.

Online advertising drives the economy of the World Wide Web. Modern websites of any size and popularity include advertisements to monetize visits from their users. To this end, they assign an area of their web page to an advertising company (so called ad exchange) that will use it to display promotional content. By doing this, the website owner implicitly trusts that the advertising company will offer legitimate content and it will not put the site's visitors at risk of falling victims of malware campaigns and other scams.

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.

The web is one of the most popular vectors to spread malware. Attackers lure victims to visit compromised web pages or entice them to click on malicious links. These victims are redirected to sites that exploit their browsers or trick them into installing malicious software using social engineering.

The users of microblogging services, such as Twitter, use the count of followers of an account as a measure of its reputation or influence. For those unwilling or unable to attract followers naturally, a growing industry of "Twitter follower markets" provides followers for sale. Some markets use fake accounts to boost the follower count of their customers, while others rely on a pyramid scheme to turn non-paying customers into followers for each other, and into followers for paying customers. In this paper, we present a detailed study of Twitter follower markets, report in detail on both the static and dynamic properties of customers of these markets, and develop and evaluate multiple techniques for detecting these activities. We show that our detection system is robust and reliable, and can detect a significant number of customers in the wild.

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.

Live security exercises are a powerful educational tool to motivate students to excel and foster research and development of novel security solutions. Our insight is to design a live security exercise to provide interesting datasets in a specific area of security research. In this paper we validated this insight, and we present the design of a novel kind of live security competition centered on the concept of Cyber Situational Awareness. The competition was carried out in December 2010, and involved 72 teams (900 students) spread across 16 countries, making it the largest educational live security exercise ever performed. We present both the innovative design of this competition and the novel dataset we collected. In addition, we define Cyber Situational Awareness metrics to characterize the toxicity and effectiveness of the attacks performed by the participants with respect to the missions carried out by the targets of the attack.

Social networking has become a popular way for users to meet and interact online. Users spend a significant amount of time on popular social network platforms (such as Facebook, MySpace, or Twitter), storing and sharing a wealth of personal information. This information, as well as the possibility of contacting thousands of users, also attracts the interest of cybercriminals. For example, cybercriminals might exploit the implicit trust relationships between users in order to lure victims to malicious websites. As another example, cybercriminals might find personal information valuable for identity theft or to drive targeted spam campaigns.