In recent years, the growth of mobile advertising has been driven by in-app programmatic advertising and technologies like Real-Time Bidding (RTB). However, this growth has also led to an increase in ad fraud, such as click injection, background ad activity, etc. While existing studies have primarily concentrated on ad fraud within individual apps or devices, this paper introduces a new form of collusion-based ad fraud, named ad attribution laundering fraud (ALF). ALF involves multiple apps collaborating to deceive advertisers by misrepresenting the app where ads are displayed. The collusion-based approach allows lower-quality apps to exploit the reputable identities of seemingly legitimate apps. This deceives advertisers or ad networks into believing that the advertisements they place are reaching potentially valid end-users on the legitimate app. The seemingly legitimate ad events and ad attribution procedures employed by individual apps in such attacks can evade detection by existing tools.

The blooming of the Internet of Things (IoT) has led to the demand for new connectivity technologies. Zigbee, with its low-power consumption and flexible network structure, has become one of the essential wireless communication protocols in the IoT ecosystem and is adopted by many major companies. There have been over thousands of certified Zigbee products, ranging from sensors, to link light products, to smart energy devices. A common belief is that Zigbee is comparatively secure, due to the nature of closed networks and the use of encryption.

Along with gaining popularity of Real-Time Bidding (RTB) based programmatic advertising, the click farm based invalid traffic, which leverages massive real smartphones to carry out large-scale ad fraud campaigns, is becoming one of the major threats against online advertisement. In this study, we take an initial step towards the detection and large-scale measurement of the click farm based invalid traffic. Our study begins with a measurement on the device's features using a real-world labeled dataset, which reveals a series of features distinguishing the fraudulent devices from the benign ones. Based on these features, we develop EvilHunter, a system for detecting fraudulent devices through ad bid request logs with a focus on clustering fraudulent devices. EvilHunter functions by 1) building a classifier to distinguish fraudulent and benign devices; 2) clustering devices based on app usage patterns; and 3) relabeling devices in clusters through majority voting. EvilHunter demonstrates 97% precision and 95% recall on a real-world labeled dataset. By investigating a super click farm, we reveal several cheating strategies that are commonly adopted by fraudulent clusters. We further reduce the overhead of EvilHunter and discuss how to deploy the optimized EvilHunter in a real-world system. We are in partnership with a leading ad verification company to integrate EvilHunter into their industrial platform.

As the world’s leading software development platform, GitHub has become a social networking site for programmers and recruiters who leverage its social features, such as star and fork, for career and business development. However, in this paper, we found a group of GitHub accounts that conducted promotion services in GitHub, called “promoters”, by performing paid star and fork operations on specified repositories. We also uncovered a stealthy way of tampering with historical commits, through which these promoters are able to fake commits retroactively. By exploiting such a promotion service, any GitHub user can pretend to be a skillful developer with high influence.

Fake base station (FBS) has been exploited by criminals to attack mobile users by spamming fraudulent messages for over a decade. Despite that prior work has proposed several techniques to mitigate this issue, FBS spam is still a long-standing challenging issue in some countries, such as China, and causes billions of dollars of financial loss every year. Therefore, understanding and exploring the thematic strategies in the FBS spam ecosystem at a large scale would improve the defense mechanisms.

HTTPS is principally designed for secure end-to-end communication, which adds confidentiality and integrity to sensitive data transmission. While several man-in-the-middle attacks (e.g., SSL Stripping) are available to break the secured connections, state-of-the-art security policies (e.g., HSTS) have significantly increased the cost of successful attacks. However, the TLS certificates shared by multiple domains make HTTPS hijacking attacks possible again.

Content Delivery Networks (CDNs) aim to improve network performance and protect against web attack traffic for their hosting websites. And the HTTP range request mechanism is majorly designed to reduce unnecessary network transmission. However, we find the specifications failed to consider the security risks introduced when CDNs meet range requests. In this study, we present a novel class of HTTP amplification attack, Range-based Amplification (RangeAmp) Attacks. It allows attackers to massively exhaust not only the outgoing bandwidth of the origin servers deployed behind CDNs but also the bandwidth of CDN surrogate nodes. We examined the RangeAmp attacks on 13 popular CDNs to evaluate the feasibility and real-world impacts. Our experiment results show that all these CDNs are affected by the RangeAmp attacks. We also disclosed all security issues to affected CDN vendors and already received positive feedback from 12 vendors.

The popularity of online gambling could bring negative social impact, and many countries ban or restrict online gambling. Taking China for example, online gambling violates Chinese laws and hence is illegal. However, illegal online gambling websites are still thriving despite strict restrictions, since they are able to make tremendous illicit profits by trapping and cheating online players. In this paper, we conduct the first deep analysis on illegal online gambling targeting Chinese to unveil its profit chain. After successfully identifying more than 967,954 suspicious illegal gambling websites, we inspect these illegal gambling websites from five aspects, including webpage structure similarity, SEO (Search Engine Optimization) methods, the abuse of Internet infrastructure, third-party online payment, and gambling group. Then we conduct a measurement study on the profit chain of illegal online gambling, investigating the upstream and downstream of these illegal gambling websites. We mainly focus on promotion strategies, third-party online payment, the abuse of third-party live chat services, and network infrastructures. Our findings shed the light on the ecosystem of online gambling and help the security community thwart illegal online gambling.

Illicit traffic monetization is a type of Internet fraud that hijacks users' web requests and reroutes them to a traffic network (e.g., advertising network), in order to unethically gain monetary rewards. Despite its popularity among Internet fraudsters, our understanding of the problem is still limited. Since the behavior is highly dynamic (can happen at any place including client-side, transport-layer and server-side) and selective (could target a regional network), prior approaches like active probing can only reveal a small piece of the entire ecosystem. So far, questions including how this fraud works at a global scale and what fraudsters' preferred methods are, still remain unanswered. To fill the missing pieces, we developed TraffickStop the first system that can detect this fraud passively. Our key contribution is a novel algorithm that works on large-scale DNS logs and efficiently discovers abnormal domain correlations. TraffickStop enables the first landscape study of this fraud, and we have some interesting findings. By analyzing over 231 billion DNS logs of two weeks, we discovered 1,457 fraud sites. Regarding its scale, the fraud sites receive more than 53 billion DNS requests within one year, and a company could lose up to 53K dollars per day due to fraud traffic. We also discovered two new strategies that are leveraged by fraudsters to evade inspection. Our work provides new insights into illicit traffic monetization, raises its public awareness, and contributes to a better understanding and ultimate elimination of this threat.

DNS packets are designed to travel in unencrypted form through the Internet based on its initial standard. Recent discoveries show that real-world adversaries are actively exploiting this design vulnerability to compromise Internet users' security and privacy. To mitigate such threats, several protocols have been proposed to encrypt DNS queries between DNS clients and servers, which we jointly term as DNS-over-Encryption. While some proposals have been standardized and are gaining strong support from the industry, little has been done to understand their status from the view of global users.

Misspelled keywords have become an appealing target in search poisoning, since they are less competitive to promote than the correct queries and account for a considerable amount of search traffic. Search engines have adopted several countermeasure strategies, e.g., Google applies automated corrections on queried keywords and returns search results of the corrected versions directly. However, a sophisticated class of attack, which we term as linguistic-collision misspelling, can evade auto-correction and poison search results. Cybercriminals target special queries where the misspelled terms are existent words, even in other languages (e.g., "idobe", a misspelling of the English word "adobe", is a legitimate word in the Nigerian language). In this paper, we perform the first large-scale analysis on linguistic-collision search poisoning attacks. In particular, we check 1.77 million misspelled search terms on Google and Baidu and analyze both English and Chinese languages, which are the top two languages used by Internet users. We leverage edit distance operations and linguistic properties to generate misspelling candidates. To more efficiently identify linguistic-collision search terms, we design a deep learning model that can improve collection rate by 2.84x compared to random sampling. Our results show that the abuse is prevalent: around 1.19% of linguistic-collision search terms on Google and Baidu have results on the first page directing to malicious websites. We also find that cybercriminals mainly target categories of gambling, drugs, and adult content. Mobile-device users disproportionately search for misspelled keywords, presumably due to small screen for input. Our work highlights this new class of search engine poisoning and provides insights to help mitigate the threat.

Internationalized Domain Names (IDNs) are domain names containing non-ASCII characters. Despite its installation in DNS for more than 15 years, little has been done to understand how this initiative was developed and its security implications. In this work, we aim to fill this gap by studying the IDN ecosystem and cyber-attacks abusing IDN. In particular, we performed by far the most comprehensive measurement study using IDNs discovered from 56 TLD zone files. Through correlating data from auxiliary sources like WHOIS, passive DNS and URL blacklists, we gained many insights. Our discoveries are multi-faceted. On one hand, 1.4 million IDNs were actively registered under over 700 registrars, and regions within east Asia have seen prominent development in IDN registration. On the other hand, most of the registrations were opportunistic: they are currently not associated with meaningful websites and they have severe configuration issues (e.g., shared SSL certificates). What is more concerning is the rising trend of IDN abuse. So far, more than 6K IDNs were determined as malicious by URL blacklists and we also identified 1,516 and 1,497 IDNs showing high visual and semantic similarity to reputable brand domains (e.g., apple.com). Meanwhile, brand owners have only registered a few of these domains. Our study suggests the development of IDN needs to be re-examined. New solutions and proposals are needed to address issues like its inadequate usage and new attack surfaces.

Security research has made extensive use of exhaustive Internet-wide scans over the recent years, as they can provide significant insights into the overall state of security of the Internet, and ZMap made scanning the entire IPv4 address space practical. However, the IPv4 address space is exhausted, and a switch to IPv6, the only accepted long-term solution, is inevitable. In turn, to better understand the security of devices connected to the Internet, including in particular Internet of Things devices, it is imperative to include IPv6 addresses in security evaluations and scans. Unfortunately, it is practically infeasible to iterate through the entire IPv6 address space, as it is 2^96 times larger than the IPv4 address space. Therefore, enumeration of active hosts prior to scanning is necessary. Without it, we will be unable to investigate the overall security of Internet-connected devices in the future. In this paper, we introduce a novel technique to enumerate an active part of the IPv6 address space by walking DNSSEC-signed IPv6 reverse zones. Subsequently, by scanning the enumerated addresses, we uncover significant security problems: the exposure of sensitive data, and incorrectly controlled access to hosts, such as access to routing infrastructure via administrative interfaces, all of which were accessible via IPv6. Furthermore, from our analysis of the differences between accessing dual-stack hosts via IPv6 and IPv4, we hypothesize that the root cause is that machines automatically and by default take on globally routable IPv6 addresses. This is a practice that the affected system administrators appear unaware of, as the respective services are almost always properly protected from unauthorized access via IPv4. Our findings indicate (i) that enumerating active IPv6 hosts is practical without a preferential network position contrary to common belief, (ii) that the security of active IPv6 hosts is currently still lagging behind the security state of IPv4 hosts, and (iii) that unintended IPv6 connectivity is a major security issue for unaware system administrators.

Content Delivery Networks (CDNs) are critical Internet infrastructure. Besides high availability and high performance, CDNs also provide security services such as anti-DoS and Web Application Firewalls to CDN-powered websites. However, the massive resources of CDNs may also be leveraged by attackers exploiting their architectural, implementation, or operational weaknesses. In this paper, we show that today's CDN operation is overly loose in customer-controlled forwarding policy and the lack of origin validation leads to a wide range of abuse cases such as DoS attack and stealthy port scan. We systematically study these abuse cases and demonstrate their feasibility in popular CDNs. Further, we evaluate the impact of these abuses by discovering that there are millions of CDN edge servers, and a substantial fraction of them can be abused. Lastly, we propose mitigation solutions against such abuses and discuss their feasibility.

The evolution of mobile malware poses a serious threat to smartphone security. Today, sophisticated attackers can adapt by maximally sabotaging machine-learning classifiers via polluting training data , rendering most recent machine learning-based malware detection tools (such as D rebin , D roid APIM iner , and M a M a D roid ) ineffective. In this paper, we explore the feasibility of constructing crafted malware samples ; examine how machine-learning classifiers can be misled under three different threat models; then conclude that injecting carefully crafted data into training data can significantly reduce detection accuracy. To tackle the problem, we propose K uafu D et , a two-phase learning enhancing approach that learns mobile malware by adversarial detection. K uafu D et includes an offline training phase that selects and extracts features from the training set, and an online detection phase that utilizes the classifier trained by the first phase. To further address the adversarial environment, these two phases are intertwined through a self-adaptive learning scheme, wherein an automated camouflage detector is introduced to filter the suspicious false negatives and feed them back into the training phase. We finally show that K uafu D et can significantly reduce false negatives and boost the detection accuracy by at least 15%. Experiments on more than 250,000 mobile applications demonstrate that K uafu D et is scalable and can be highly effective as a standalone system.

The popularity of cloud hosting services also brings in new security chal- lenges: it has been reported that these services are increasingly utilized by miscreants for their malicious online activities. Mitigating this emerging threat, posed by such “bad repositories” (simply Bar), is challenging due to the different hosting strategy to traditional hosting service, the lack of direct observations of the repositories by those outside the cloud, the reluctance of the cloud provider to scan its customers’ repositories without their consent, and the unique evasion strategies employed by the adversary. In this paper, we took the first step toward understanding and detecting this emerging threat. Using a small set of “seeds” (i.e., confirmed Bars), we identified a set of collective features from the websites they serve (e.g., attempts to hide Bars), which uniquely characterize the Bars. These features were utilized to build a scanner that detected over 600 Bars on leading cloud platforms like Amazon, Google, and 150 K sites, including popular ones like , using them. Highlights of our study include the pivotal roles played by these repositories on malicious infrastructures and other important discoveries include how the adversary exploited legitimate cloud repositories and why the adversary uses Bars in the first place that has never been reported. These findings bring such malicious services to the spotlight and contribute to a better understanding and ultimately eliminating this new threat.

Device drivers are an essential part in modern Unix-like systems to handle operations on physical devices, from hard disks and printers to digital cameras and Bluetooth speakers. The surge of new hardware, particularly on mobile devices, introduces an explosive growth of device drivers in system kernels. Many such drivers are provided by third-party developers, which are susceptible to security vulnerabilities and lack proper vetting. Unfortunately, the complex input data structures for device drivers render traditional analysis tools, such as fuzz testing, less effective, and so far, research on kernel driver security is comparatively sparse. In this paper, we present DIFUZE, an interface-aware fuzzing tool to automatically generate valid inputs and trigger the execution of the kernel drivers. We leverage static analysis to compose correctly-structured input in the userspace to explore kernel drivers. DIFUZE is fully automatic, ranging from identifying driver handlers, to mapping to device file names, to constructing complex argument instances. We evaluate our approach on seven modern Android smartphones. The results show that DIFUZE can effectively identify kernel driver bugs, and reports 32 previously unknown vulnerabilities, including flaws that lead to arbitrary code execution.

Domain names play a critical role in cybercrime, because they identify hosts that serve malicious content (such as malware, Trojan binaries, or malicious scripts), operate as command-and-control servers, or carry out some other role in the malicious network infrastructure. To defend against Internet attacks and scams, operators widely use blacklisting to detect and block malicious domain names and IP addresses. Existing blacklists are typically generated by crawling suspicious domains, manually or automatically analyzing malware, and collecting information from honeypots and intrusion detection systems. Unfortunately, such blacklists are difficult to maintain and are often slow to respond to new attacks. Security experts set up and join mailing lists to discuss and share intelligence information, which provides a better chance to identify emerging malicious activities. In this paper, we design Gossip, a novel approach to automatically detect malicious domains based on the analysis of discussions in technical mailing lists (particularly on security-related topics) by using natural language processing and machine learning techniques. We identify a set of effective features extracted from email threads, users participating in the discussions, and content keywords, to infer malicious domains from mailing lists, without the need to actually crawl the suspect websites. Our result shows that Gossip achieves high detection accuracy. Moreover, the detection from our system is often days or weeks earlier than existing public blacklists.

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.

Miscreants register thousands of new domains every day to launch Internet-scale attacks, such as spam, phishing, and drive-by downloads. Quickly and accurately determining a domain's reputation (association with malicious activity) provides a powerful tool for mitigating threats and protecting users. Yet, existing domain reputation systems work by observing domain use (e.g., lookup patterns, content hosted) often too late to prevent miscreants from reaping benefits of the attacks that they launch. As a complement to these systems, we explore the extent to which features evident at domain registration indicate a domain's subsequent use for malicious activity. We develop PREDATOR, an approach that uses only time-of-registration features to establish domain reputation. We base its design on the intuition that miscreants need to obtain many domains to ensure profitability and attack agility, leading to abnormal registration behaviors (e.g., burst registrations, textually similar names). We evaluate PREDATOR using registration logs of second-level .com and .net domains over five months. PREDATOR achieves a 70% detection rate with a false positive rate of 0.35%, thus making it an effective and early first line of defense against the misuse of DNS domains. It predicts malicious domains when they are registered, which is typically days or weeks earlier than existing DNS blacklists.

The popularity of cloud hosting services also brings in new security challenges: it has been reported that these services are increasingly utilized by miscreants for their malicious online activities. Mitigating this emerging threat, posed by such "bad repositories" (simply Bar), is challenging due to the different hosting strategy to traditional hosting service, the lack of direct observations of the repositories by those outside the cloud, the reluctance of the cloud provider to scan its customers' repositories without their consent, and the unique evasion strategies employed by the adversary. In this paper, we took the first step toward understanding and detecting this emerging threat. Using a small set of "seeds" (i.e., confirmed Bars), we identified a set of collective features from the websites they serve (e.g., attempts to hide Bars), which uniquely characterize the Bars. These features were utilized to build a scanner that detected over 600 Bars on leading cloud platforms like Amazon, Google, and 150K sites, including popular ones like groupon.com, using them. Highlights of our study include the pivotal roles played by these repositories on malicious infrastructures and other important discoveries include how the adversary exploited legitimate cloud repositories and why the adversary uses Bars in the first place that has never been reported. These findings bring such malicious services to the spotlight and contribute to a better understanding and ultimately eliminating this new threat.

The popularity of long-tail search engine optimization (SEO) brings with new security challenges: incidents of long-tail keyword poisoning to lower competition and increase revenue have been reported. The emergence of cloud web hosting services provides a new and effective platform for long-tail SEO spam attacks. There is growing evidence that large-scale long-tail SEO campaigns are being carried out on cloud hosting platforms because they offer low-cost, high-speed hosting services. In this paper, we take the first step toward understanding how long-tail SEO spam is implemented on cloud hosting platforms. After identifying 3,186 cloud directories and 318,470 doorway pages on the leading cloud platforms for long-tail SEO spam, we characterize their abusive behavior. One highlight of our findings is the effectiveness of the cloud-based long-tail SEO spam, with 6% of the doorway pages successfully appearing in the top 10 search results of the poisoned long-tail keywords.

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.

Spammers register a tremendous number of domains to evade blacklisting and takedown efforts. Current techniques to detect such domains rely on crawling spam URLs or monitoring lookup traffic. Such detection techniques are only effective after the spammers have already launched their campaigns, and thus these countermeasures may only come into play after the spammer has already reaped significant benefits from the dissemination of large volumes of spam. In this paper we examine the registration process of such domains, with a particular eye towards features that might indicate that a given domain likely has a malicious purpose at registration time, before it is ever used for an attack. Our assessment includes exploring the characteristics of registrars, domain life cycles, registration bursts, and naming patterns. By investigating zone changes from the .com TLD over a 5-month period, we discover that spammers employ bulk registration, that they often re-use domains previously registered by others, and that they tend to register and host their domains over a small set of registrars. Our findings suggest steps that registries or registrars could use to frustrate the efforts of miscreants to acquire domains in bulk, ultimately reducing their agility for mounting large-scale attacks.

Attackers often use URLs to advertise scams or propagate malware. Because the reputation of a domain can be used to identify malicious behavior, miscreants often register these domains "just in time" before an attack. This paper explores the DNS behavior of attack domains, as identified by appearance in a spam trap, shortly after the domains were registered. We explore the behavioral properties of these domains from two perspectives: (1) the DNS infrastructure associated with the domain, as is observable from the resource records; and (2) the DNS lookup patterns from networks who are looking up the domains initially. Our analysis yields many findings that may ultimately be useful for early detection of malicious domains. By monitoring the infrastructure for these malicious domains, we find that about 55% of scam domains occur in attacks at least one day after registration, suggesting the potential for early discovery of malicious domains, solely based on properties of the DNS infrastructure that resolves those domains. We also find that there are a few regions of IP address space that host name servers and other types of servers for only malicious domains. Malicious domains have resource records that are distributed more widely across IP address space, and they are more quickly looked up by a variety of different networks. We also identify a set of "tainted" ASes that are used heavily by bad domains to host resource records. The features we observe are often evident before any attack even takes place; ultimately, they might serve as the basis for a DNS-based early warning system for attacks.