This paper presents a novel finite-time adaptive fuzzy asymptotic tracking control framework specifically designed to meet the high-precision trajectory tracking requirements of quadrotor unmanned aerial vehicles (QUAVs) under external disturbances and system uncertainties. First, an asymptotic tracking control strategy is developed to ensure that all tracking errors converge to zero, eliminating steady-state deviations and enhancing operational accuracy. Building on this foundation, a finite-time command-filtered control framework is introduced, which retains the structural advantages of the backstepping approach while guaranteeing rapid convergence within a finite time. Additionally, an error compensation mechanism is incorporated to mitigate filtering errors induced by command filters, further improving control precision. Furthermore, adaptive laws are integrated with fuzzy logic systems to effectively estimate and compensate for the effects from external disturbances and system uncertainties. The effectiveness and feasibility of the proposed method are validated through numerical simulations and experiments on a QUAV prototype.

Peer-to-peer (P2P) systems such as BitTorrent and Bitcoin are susceptible to serious attacks from byzantine nodes that join as peers. Due to well-known impossibility results for designing P2P primitives in unrestricted byzantine settings, research has explored many adversarial models with additional assumptions, ranging from mild (such as pre-established PKI) to strong (such as the existence of common random coins). One such widely-studied model is the general-omission model, which yields simple protocols with good efficiency, but has been considered impractical or unrealizable since it artificially limits the adversary only to omitting messages.In this work, we study the setting of a synchronous network wherein peer nodes have CPUs equipped with a recent trusted computing mechanism called Intel SGX. In this model, we observe that the byzantine adversary reduces to the adversary in the general-omission model. As a first result, we show that by leveraging SGX features, we eliminate any source of advantage for a byzantine adversary beyond that gained by omitting messages, making the general-omission model realizable. Our evaluation of 1000 nodes running on 40 DeterLab machines confirms theoretical efficiency claim.

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.

The similarity of the user interfaces (UIs) among Android apps is an important factor to indicate abnormal behaviors in Android apps. For example, phishing apps use UIs similar to those of their target apps to lure users to input sensitive information. As another example, repackaged apps preserve the UIs of the original apps while adding malicious code. In this paper, we propose a novel solution, GeminiScope, to robustly detect similar UIs among Android apps. Our approach analyzes the UI layouts of Android apps, extracts the fundamental features of the UIs' visual appearance, and rates the similarity among the UIs of apps. We evaluated GeminiScope using a few sets of apps from various sources. It reliably detected apps with similar UIs, showing the potential of using visual similarity as a unique feature to detect malicious apps.

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.

Process-based isolation, suggested by several research prototypes, is a cornerstone of modern browser security architectures. Google Chrome is the first commercial browser that adopts this architecture. Unlike several research prototypes, Chrome's process-based design does not isolate different web origins, but primarily promises to protect "the local system" from "the web". However, as billions of users now use web-based cloud services (e.g., Dropbox and Google Drive), which are integrated into the local system, the premise that browsers can effectively isolate the web from the local system has become questionable. In this paper, we argue that, if the process-based isolation disregards the same-origin policy as one of its goals, then its promise of maintaining the "web/local system (local)" separation is doubtful. Specifically, we show that existing memory vulnerabilities in Chrome's renderer can be used as a stepping-stone to drop executables/scripts in the local file system, install unwanted applications and misuse system sensors. These attacks are purely data-oriented and do not alter any control flow or import foreign code. Thus, such attacks bypass binary-level protection mechanisms, including ASLR and in-memory partitioning. Finally, we discuss various full defenses and present a possible way to mitigate the attacks presented.

Abstract The peer-assisted CDN is a new content distribution paradigm supported by CDNs (e.g., Akamai), which enables clients to cache and distribute web content on behalf of a website. Peer-assisted CDNs bring significant bandwidth savings to website operators and reduce network latency for users. In this work, we show that the current designs of peer-assisted CDNs expose clients to privacy-invasive attacks, enabling one client to infer the set of browsed resources of another client. To alleviate this, we propose an anonymous peer-assisted CDN (APAC), which employs content delivery while providing initiator anonymity (i.e., hiding who sends the resource request) and responder anonymity (i.e., hiding who responds to the request) for peers. APAC can be a web service, compatible with current browsers and requiring no client-side changes. Our anonymity analysis shows that our APAC design can preserve a higher level of anonymity than state-of-the-art peer-assisted CDNs. In addition, our evaluation demonstrates that APAC can achieve desired performance gains.

Mobile OSes and applications form a large, complex and vulnerability-prone software stack. In such an environment, security techniques to strongly protect sensitive data in mobile devices are important and challenging. To address such challenges, we introduce the concept of the trusted data vault, a small trusted engine that securely manages the storage and usage of sensitive data in an untrusted mobile device. In this paper, we design and build Droid Vault - the first realization of a trusted data vault on the Android platform. Droid Vault establishes a secure channel between data owners and data users while allowing data owners to enforce strong control over the sensitive data with a minimal trusted computing base (TCB). We prototype Droid Vault via the novel use of hardware security features of ARM processors, i.e., Trust Zone. Our evaluation demonstrates its functionality for processing sensitive data and its practicality for adoption in the real world.