Structural Health Monitoring (SHM) uses pervasive sensors to monitor the health and integrity of buildings and civil infrastructure, significantly reducing maintenance costs and improving safety. Despite its potential, widespread adoption of SHM is hindered by high deployment costs and privacy concerns from building tenants. This work introduces IceSpy as one solution to both problems: reducing the cost and improving the privacy of SHM applications. IceSpy uses a programmable multi-chip systolic array of small, low-power Commercial off-the Shelf (COTS) FPGAs to implement data filtering and differential privacy. Data filtering reduces cost by reducing power-hungry wireless data transmission, leading to smaller batteries and power harvesters. Meanwhile, local differential privacy removes privacy-sensitive information before transmitting it to an untrusted server. Even with additional differential privacy measures, IceSpy achieves a 3× reduction of power consumption and a consequent reduction in battery costs due to decreased wireless communication. This low-power privacy-preserving filtering technology reduces deployment costs and mitigates privacy concerns from potential deployment sites. With many obstacles removed, we are working on deploying IceSpy in dams and commercial buildings to obtain deeper insight into the structural health of buildings and infrastructure.

Foundation models are increasingly becoming better autonomous programmers, raising the prospect that they could also automate dangerous offensive cyber‑operations. Current frontier model audits probe the cybersecurity risks of such agents, but most fail to account for the degrees of freedom available to adversaries in the real world.In particular, with strong verifiers and financial incentives, agents for offensive cybersecurity are amenable to iterative improvement by would-be adversaries. We argue that assessments should take into account an expanded threat model in the context of cybersecurity, emphasizing the varying degrees of freedom that an adversary may possess in stateful and non-stateful environments within a fixed compute budget. We show that even with a relatively small compute budget (8 H100 GPU Hours in our study), adversaries can improve an agent's cybersecurity capability on InterCode CTF by more than 40\% relative to the baseline---without any external assistance. These results highlight the need to evaluate agents' cybersecurity risk in a dynamic manner, painting a more representative picture of risk.

Training a machine learning model with data following a meaningful order, i.e., from easy to hard, has been proven to be effective in accelerating the training process and achieving better model performance. The key enabling technique is curriculum learning (CL), which has seen great success and has been deployed in areas like image and text classification. Yet, how CL affects the privacy of machine learning is unclear. Given that CL changes the way a model memorizes the training data, its influence on data privacy needs to be thoroughly evaluated. To fill this knowledge gap, we perform the first study and leverage membership inference attack (MIA) and attribute inference attack (AIA) as two vectors to quantify the privacy leakage caused by CL. Our evaluation of 9 real-world datasets with attack methods (NN-based, metric-based, label-only MIA, and NN-based AIA) revealed new insights about CL. First, MIA becomes slightly more effective when CL is applied, but the impact is much more prominent to a subset of training samples ranked as difficult. Second, a model trained under CL is less vulnerable under AIA, compared to MIA. Third, the existing defense techniques like MemGuard and MixupMMD are not effective under CL. Finally, based on our insights into CL, we propose a new MIA, termed Diff-Cali, which exploits the difficulty scores for result calibration and is demonstrated to be effective against all CL methods and the normal training method. With this study, we hope to draw the community's attention to the unintended privacy risks of emerging machine-learning techniques and develop new attack benchmarks and defense solutions.

As the utilization of network traces for the network measurement research becomes increasingly prevalent, concerns regarding privacy leakage from network traces have garnered the public's attention. To safeguard network traces, researchers have proposed the trace synthesis that retains the essential properties of the raw data. However, previous works also show that synthesis traces with generative models are vulnerable under linkage attacks.

We present a global, large-scale measurement of Internet traffic shadowing, a less-studied yet covert format of on-path manipulation. As part of pervasive monitoring, data within packets is silently observed, retained, and then leveraged to produce additional, unsolicited requests. To depict the landscape of such behaviors, we generate a collection of decoy traffic that lures on-path exhibitors, spread them via 4,364 vantage points recruited from commercial VPN providers, and capture unsolicited requests triggered by them. We find traffic shadowing against DNS, HTTP, and TLS protocols; DNS queries to several public resolvers are most susceptible, by being observed on a wide range of Internet paths. Through hop-by-hop tracerouting, we find observers of DNS queries associated with destinations, while HTTP messages are mostly observed on the wire. User data can be retained for long, e.g., over 10 days, and can be leveraged for more than once. While a notable portion of unsolicited requests originate from addresses labeled by blocklists, we find most of them are performing reconnaissance, and we see no evidence of exploits attempted in the collected traffic.

DNS can be compared to a game of chess in that its rules are simple, yet the possibilities it presents are endless. While the fundamental rules of DNS are straightforward, DNS implementations can be extremely complex. In this study, we intend to explore the complexities and vulnerabilities in DNS response pre-processing by systematically analyzing DNS RFCs and DNS software implementations. We present the discovery of three new types of logic vulnerabilities, leading to the proposal of three novel attacks, namely the TuDoor attack. These attacks involve the use of malformed DNS response packets to carry out DNS cache poisoning, denial- of-service, and resource consuming attacks. By performing comprehensive experiments, we demonstrate the attack’s feasibility and significant real-world impacts of TUDOOR. In total, 24 mainstream DNS software, including BIND, PowerDNS, and Microsoft DNS, are affected by TuDoor. Attackers can instigate cache poisoning and denial-of-service attacks against vulnerable resolvers using a handful of crafted packets within 1 second or circumvent the query limit to deplete resolution resources (e.g., CPU). Besides, to determine the vulnerable resolver population in the wild, we collect and evaluate 16 popular Wi-Fi routers, 6 prevalent router OSes, 42 public DNS services, and around 1.8M open DNS resolvers. Our measurement results indicate that TUDOOR could exploit 7 routers (OSes), 18 public DNS services, and 424,652 (23.1%) open DNS resolvers. Following the best practice of responsible disclosure, we have reported these vulnerabilities to all affected vendors, and 18 of them, including BIND, Chrome, Cloudflare, and Microsoft, have acknowledged our findings and discussed mitigation solutions with us. Furthermore, 33 CVE IDs are assigned to our discovered vulnerabilities, and we provide an online detection tool as one of the mitigation measures. Our research highlights the urgent need for standardization of DNS response pre-processing logic to enhance the security of DNS.

Cyber-attacks have become increasingly sophisticated, which also drives the development of security analytics that produce countermeasures by mining organizational logs, e.g., network and authentication logs. Graph security analytics (GSA) that can model the complex communication patterns between users/hosts/processes have been extensively developed and deployed. Among the techniques that power GSAs, Unsupervised Network Representation Learning (UNRL) is gaining traction, which learns a latent graph representation, i.e., node embedding, and customizes it for different downstream tasks. Prominent advantages have been demonstrated by UNRL-based GSAs, as UNRL trains a detection model in an unsupervised way and exempts the model developers from the duty of feature engineering.In this paper, we revisit the designs of previous UNRL-based GSAs to understand how they perform in real-world settings. We found their performance is questionable on large-scale, noisy log datasets like LANL authentication dataset, and the main reason is that they follow the standard UNRL framework that trains a generic model in an attack-agnostic way. We argue that generic attack characteristics should be considered, and propose Argus, a UNRL-based GSA with new encoder and decoder designs. Argus is also designed to work on discrete temporal graphs (DTG) to exploit the graph temporal dynamics. Our evaluation of two large-scale datasets, LANL and OpTC, shows it can outperform the state-of-the-art approaches by a large margin.

Although the prevention of AI vulnerabilities is critical to preserve the safety and privacy of users and businesses, educational tools for robust AI are still underdeveloped worldwide. We present the design, implementation, and assessment of Maestro. Maestro is an effective open-source game-based platform that contributes to the advancement of robust AI education. Maestro provides "goal-based scenarios" where college students are exposed to challenging life-inspired assignments in a "competitive programming" environment. We assessed Maestro's influence on students' engagement, motivation, and learning success in robust AI. This work also provides insights into the design features of online learning tools that promote active learning opportunities in the robust AI domain. We analyzed the reflection responses (measured with Likert scales) of 147 undergraduate students using Maestro in two quarterly college courses in AI. According to the results, students who felt the acquisition of new skills in robust AI tended to appreciate highly Maestro and scored highly on material consolidation, curiosity, and maestry in robust AI. Moreover, the leaderboard, our key gamification element in Maestro, has effectively contributed to students' engagement and learning. Results also indicate that Maestro can be effectively adapted to any course length and depth without losing its educational quality.

Recent studies have shown that systems with limited resources like Metadata-private Messenger (MPM) suffer from side-channel attacks under resource allocation (RA). In the case of MPM, which is designed to keep the identities and activities of both callers and callees private from network adversaries, an attacker can compromise a victim’s friends and keep calling the victim to infer whether the victim is busy, which breaks the privacy guarantee of MPM.

Recent research efforts on 3D point cloud semantic segmentation (PCSS) have achieved outstanding performance by adopting neural networks. However, the robustness of these complex models have not been systematically analyzed. Given that PCSS has been applied in many safety-critical applications like autonomous driving, it is important to fill this knowledge gap, especially, how these models are affected under adversarial samples. As such, we present a comparative study of PCSS robustness. First, we formally define the attacker's objective under performance degradation and object hiding. Then, we develop new attack by whether to bound the norm. We evaluate different attack options on two datasets and three PCSS models. We found all the models are vulnerable and attacking point color is more effective. With this study, we call the attention of the research community to develop new approaches to harden PCSS models.

Training tools targeting robust AI are still in their infancy. We present Maestro, an effective open-source game-based platform for robust AI training in higher education, which includes counter- measures and prevention of AI vulnerabilities. Maestro provides goal-based scenarios (GBSs) where students are exposed to challenging life-inspired assignments in a competitive programming environment. The assessment of Maestro showed that its leader-board, a key gamification element, has been crucial for effective student learning. Students who felt the acquisition of new skills in robust AI tended to appreciate highly Maestro and scored highly on material consolidation, curiosity and maestry in robust AI.

Natural language processing (NLP) models are known vulnerable to adversarial examples, similar to image processing models. Studying adversarial texts is an essential step to improve the robustness of NLP models. However, existing studies mainly focus on generating adversarial texts for English, with no prior knowledge that whether those attacks could be applied to Chinese. After analyzing the differences between Chinese and English, we propose a novel adversarial Chinese text generation solution Argot, by utilizing the method for adversarial English examples and several novel methods developed on Chinese characteristics. Argot could effectively and efficiently generate adversarial Chinese texts with good readability in both white-box and black-box settings. Argot could also automatically generatetargetedChinese adversarial texts, achieving a high success rate and ensuring the readability of the generated texts. Furthermore, we apply Argot to the spam detection task in both local detection models and a public toxic content detection system from a well-known security company. Argot achieves a relatively high bypass success rate with fluent readability, which proves that the real-world toxic content detection system is vulnerable to adversarial example attacks. We also evaluate some available defense strategies, and the results indicate that Argot can still achieve high attack success rates.

The security of the Autonomous Driving (AD) system has been gaining researchers’ and public’s attention recently. Given that AD companies have invested a huge amount of resources in developing their AD models, e.g., localization models, these models, especially their parameters, are important intellectual property and deserve strong protection.

Domain name system (DNS) is the address book of the Internet and domain names are queried before almost every network activity. Since the entities like recursive resolvers can monitor users' DNS queries, privacy concerns such as user tracking arise. Though a number of prior works have looked into this issue, they all focus on the closed-world setting, which means that victim users must be known to the adversary. We argue that it does not reflect the adversary's true capabilities. Moreover, there lacks an effective approach to defend against DNS-based user tracking. In this work, we revisit these issues by investigating the attack surface in both open-world and closed-world settings and studying how to protect users. First, we introduce a new tracking mechanism DSCorr which incorporates domain-based word embedding to capture the fine-grained distance between domain names, and automatic threshold generation for fine-tuning the attack outcome. The evaluation result on a real-world DNS dataset shows DSCorr is able to outperform the existing works by a large margin especially in the open-world setting. On the defense side, we develop a system called LDPResolve, which incorporates a recently proposed differential privacy notion ULDP (Utility-optimized Local Differential Privacy) and a new technique named parallel domain resolving, to provide privacy guarantees without damaging the utility of legitimate applications. The evaluation result on the same dataset shows the DNS-based user tracking can be effectively curbed, e.g., tracking accuracy degraded from 93% to 10.1%.

Cache side-channel attacks lead to severe security threats to the settings where a CPU is shared across users, e.g., in the cloud. The majority of attacks rely on sensing the micro-architectural state changes made by victims, but this assumption can be invalidated by combining spatial (e.g., Intel CAT) and temporal isolation. In this work, we advance the state of cache side-channel attacks by showing stateless cache side-channel attacks on server-grade CPUs, that can bypass both spatial and temporal isolation. Unlike stateful cache side-channel attacks that rely on the timing difference between a cache hit or miss, our attack exploits the timing difference caused by the interconnect congestion. Specifically, to complete cache transactions, for Intel server CPUs, which use non-inclusive and mesh interconnect, cache lines would travel across cores via the CPU mesh and UPI interconnects. Nonetheless, the interconnects are shared by all cores, and cache isolation does not segregate the traffic. An attacker can generate traffic to contend with a victim on a link, measure the extra delay, deduce the memory access pattern of the victim’s program, and infer its sensitive data. Based on this idea, we implement MESHUP, a stateless cache side-channel against mesh interconnect, and test it against the existing RSA implementations of JDK for the cross-core attack and application fingerprinting for the the cross-CPU attack. We found the RSA private key used by a victim process can be partially recovered and the co-running application can be inferred at high accuracy.

Permission is the fundamental security mechanism for protecting user data and privacy on Android. Given its importance, security researchers have studied the design and usage of permissions from various aspects. However, most of the previous research focused on the security issues ofsystem permissions. Overlooked by many researchers, an app can usecustom permissionsto share its resources and capabilities with other apps. However, the security implications of using custom permissions have not been fully understood. In this paper, we systematically evaluate the design and implementation of Android custom permissions. Notably, we built an automatic fuzzing tool, calledCuPerFuzzer+, to detect custom permission related vulnerabilities existing in the Android OS.CuPerFuzzer+treats the operations of the permission mechanism as a black-box and executes massive targeted test cases to trigger privilege escalation. In the experiments,CuPerFuzzer+discovered 5,932 effective cases with 47 critical paths successfully. Through investigating these vulnerable cases and analyzing the source code of Android OS, we further identified a series of severe design shortcomings lying in the Android permission framework, includingdangling custom permission,inconsistent permission-group mapping,custom permission elevating,inconsistent permission definition,dormant permission group, andinconsistent permission type. Exploiting these shortcomings, a malicious app can access unauthorized platform resources. On top of these observations, we propose three general design guidelines to secure custom permissions. Our findings have been acknowledged by the Android security team and assignedCVE-2020-0418,CVE-2021-0306,CVE-2021-0307, andCVE-2021-0317.

Although spearphishing is a well-known security issue and has been widely researched, it is still an evolving threat with emerging forms. In recent years, Short Message Service (SMS) has been revealed as a new distribution channel for spearphishing messages, which already has caused a serious impact in the real world, but has not yet attracted enough attention from the academic community. In this paper, we report the first systemic study to spotlight this emerging threat, SMS spearphishing attack. Through cooperating with a leading security vendor, we obtain 31.96M real-world spam messages that span three months. We design and implement a novel NLP-based detection algorithm, and uncover 90,801 spearphishing messages on the entire dataset. And then, a large-scale measurement was performed on the detected messages to reveal and understand the characteristics of SMS spearphishing attack. Our findings are multi-fold. We discover that SMS spearphishing has a significant negative impact on the real-world, and a large number of victims have been affected. And the distribution of active illicit types between spearphishing message and common spam is quite inconsistent. At the micro-level, to evade detection and increase the probability of success, adversary campaigns have evolved a set of sophisticated strategies. Our research highlights the impact of SMS spearphishing attack is prominent. We call on different communities to work together to mitigate this emerging security threat.

Face verification system (FVS), which can automatically verify a person’s identity, has been increasingly deployed in the real-world settings. Key to its success is the inclusion of face embedding, a technique that can detect similar photos of the same person by deep neural networks.

We study the problem of publishing a stream of real-valued data satisfying differential privacy (DP). One major challenge is that the maximal possible value in the stream can be quite large, leading to enormous DP noise and bad utility. To reduce the maximal value and noise, one way is to estimate a threshold so that values above it can be truncated. The intuition is that, in many scenarios, only a few values are large; thus truncation does not change the original data much. We develop such a method that finds a suitable threshold with DP. Given the threshold, we then propose an online hierarchical method and several post-processing techniques.

HTTPS secures communications in the web and heavily relies on the Web PKI for authentication. In the Web PKI, Certificate Authorities (CAs) are organizations that provide trust and issue digital certificates. Web clients rely on public root stores maintained by operating systems or browsers, with hundreds of audited CAs as trust anchors. However, as reported by security incidents, hidden root CAs beyond the public root programs have been imported into local root stores, which allows adversaries to gain trust from web clients.

Android emulators today are not only acting as a debugging tool for developers but also serving the massive end-users. These end-user Android emulators have attracted millions of users due to their advantages of running mobile apps on desktops and are especially appealing for mobile game players who demand larger screens and better performance. Besides, they commonly provide some customized assistant functionalities to improve the user experience, such as keyboard mapping and app installation from the host. To implement these services, emulators inevitably introduce communication channels between host OS and Android OS (in the Virtual Machine), thus forming a unique architecture which mobile phone does not have. However, it is unknown whether this architecture brings any new security risks to emulators.

Deep Neural Network (DNN) models have been extensively developed by companies for a wide range of applications. The development of a customized DNN model with great performance requires costly investments, and its structure (layers and hyper-parameters) is considered intellectual property and holds immense value. However, in this paper, we found the model secret is vulnerable when a cloud-based FPGA accelerator executes it. We demonstrate an end-to-end attack based on remote power side-channel analysis and machine-learning-based secret inference against different DNN models. The evaluation result shows that an attacker can reconstruct the layer and hyper-parameter sequence at over 90% accuracy using our method, which can significantly reduce her model development workload. We believe the threat presented by our attack is tangible, and new defense mechanisms should be developed against this threat.

Permission is the fundamental security mechanism for protecting user data and privacy on Android. Given its importance, security researchers have studied the design and usage of permissions from various aspects. However, most of the previous research focused on the security issues of system permissions. Overlooked by many researchers, an app can use custom permissions to share its resources and capabilities with other apps. However, the security implications of using custom permissions have not been fully understood.In this paper, we systematically evaluate the design and implementation of Android custom permissions. Notably, we built an automatic fuzzing tool, called CuPerFuzzer, to detect custom permissions related vulnerabilities existing in the Android OS. CuPerFuzzer treats the operations of the permission mechanism as a black-box and executes massive targeted test cases to trigger privilege escalation. In the experiments, CuPerFuzzer discovered 2,384 effective cases with 30 critical paths successfully. Through investigating these vulnerable cases and analyzing the source code of Android OS, we further identified a series of severe design shortcomings lying in the Android permission framework, including dangling custom permission, inconsistent permission-group mapping, custom permission elevating, and inconsistent permission definition. Exploiting any of these shortcomings, a malicious app can obtain dangeroussystem permissions without user consent and further access unauthorized platform resources. On top of these observations, we propose some general design guidelines to secure custom permissions. Our findings have been acknowledged by the Android security team and rated as High severity.

PCIe (Peripheral Component Interconnect express) protocol is the de facto protocol to bridge CPU and peripheral devices like GPU, NIC, and SSD drive. There is an increasing demand to install more peripheral devices on a single machine, but the PCIe interfaces offered by Intel CPUs are fixed. To resolve such contention, PCIe switch, PCH (Platform Controller Hub), or virtualization cards are installed on the machine to allow multiple devices to share a PCIe interface. Congestion happens when the collective PCIe traffic from the devices overwhelm the PCIe link capacity, and transmission delay is then introduced.In this work, we found the PCIe delay not only harms device performance but also leaks sensitive information about a user who uses the machine. In particular, as user’s activities might trigger data movement over PCIe (e.g., between CPU and GPU), by measuring PCIe congestion, an adversary accessing another device can infer the victim’s secret indirectly. Therefore, the delay resulted from I/O congestion can be exploited as a side-channel. We demonstrate the threat from PCIe congestion through 2 attack scenarios and 4 victim settings. Specifically, an attacker can learn the workload of a GPU in a remote server by probing a RDMA NIC that shares the same PCIe switch and measuring the delays. Based on the measurement, the attacker is able to know the keystroke timings of the victim, what webpage is rendered on the GPU, and what machine-learning model is running on the GPU. Besides, when the victim is using a low-speed device, e.g., an Ethernet NIC, an attacker controlling an NVMe SSD can launch a similar attack when they share a PCH or virtualization card. The evaluation result shows our attack can achieve high accuracy (e.g., 96.31% accuracy in inferring webpage visited by a victim).

Attacks against the domain name system (DNS) have long plagued the Internet, requiring continual investigation and vigilance to prevent the abuse of this critical infrastructure. Among these attacks, DNS hijacking has repeatedly asserted itself as one of the most serious threats. In recent years, the severity of DNS hijacking has motivated renewed interest in developing more robust defenses. The size, dynamism, and diversity of the DNS ecosystem present nontrivial challenges to crafting an effective and scalable defense. Further, the relative rarity of documented DNS hijacking attacks makes them difficult to study in-depth. In this paper, we attempt to address the challenges in two thrusts. We first conduct an analysis based on the reports of confirmed DNS hijacking attacks and passive DNS records to characterize known DNS hijacking attacks and identify features for building defense mechanisms. Then we explore the extent to which the characteristic features can be used to build a DNS hijacking detection mechanism and evaluate its effectiveness from the perspective of a network gateway.

Deep Neural Network (DNN) models have been extensively developed by companies for a wide range of applications. The development of a customized DNN model with great performance requires costly investments, and its structure (layers and hyper-parameters) is considered intellectual property and holds immense value. However, in this paper, we found the model secret is vulnerable when a cloud-based FPGA accelerator executes it. We demonstrate an end-to-end attack based on remote power side-channel analysis and machine-learning-based secret inference against different DNN models. The evaluation result shows that an attacker can reconstruct the layer and hyper-parameter sequence at over 90% accuracy using our method, which can significantly reduce her model development workload. We believe the threat presented by our attack is tangible, and new defense mechanisms should be developed against this threat.

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.

The IoT (Internet of Things) technology has been widely adopted in recent years and has profoundly changed the people's daily lives. However, in the meantime, such a fast-growing technology has also introduced new privacy issues, which need to be better understood and measured. In this work, we look into how private information can be leaked from network traffic generated in the smart home network. Although researchers have proposed techniques to infer IoT device types or user behaviors under clean experiment setup, the effectiveness of such approaches become questionable in the complex but realistic network environment, where common techniques like Network Address and Port Translation (NAPT) and Virtual Private Network (VPN) are enabled. To this aim, we propose a traffic analysis framework based on sequence-learning techniques like LSTM and leveraged the temporal relations between packets for the attack of device identification. We evaluated it under different environment settings (e.g., pure-IoT and noisy environment with multiple non-IoT devices). The results showed our framework was able to differentiate device types with a high accuracy. This result suggests IoT network communications pose prominent challenges to users' privacy, even when they are protected by encryption and morphed by the network gateway. As such, new privacy protection methods on IoT traffic need to be developed towards mitigating this new issue.

Machine learning has been attracting strong interests in recent years. Numerous companies have invested great efforts and resources to develop customized deep-learning models, which are their key intellectual properties. In this work, we investigate to what extent the secret of deep-learning models can be inferred by attackers. In particular, we focus on the scenario that a model developer and an adversary share the same GPU when training a Deep Neural Network (DNN) model. We exploit the GPU side-channel based on context-switching penalties. This side-channel allows us to extract the fine-grained structural secret of a DNN model, including its layer composition and hyper-parameters. Leveraging this side-channel, we developed an attack prototype named MosConS, which applies LSTM-based inference models to identify the structural secret. Our evaluation of MosConS shows the structural information can be accurately recovered. Therefore, we believe new defense mechanisms should be developed to protect training against the GPU side-channel.

Natural language processing (NLP) models are known vulnerable to adversarial examples, similar to image processing models. Studying adversarial texts is an essential step to improve the robustness of NLP models. However, existing studies mainly focus on analyzing English texts and generating adversarial examples for English texts. There is no work studying the possibility and effect of the transformation to another language, e.g, Chinese. In this paper, we analyze the differences between Chinese and English, and explore the methodology to transform the existing English adversarial generation method to Chinese. We propose a novel black-box adversarial Chinese texts generation solution Argot, by utilizing the method for adversarial English samples and several novel methods developed on Chinese characteristics. Argot could effectively and efficiently generate adversarial Chinese texts with good readability. Furthermore, Argot could also automatically generate targeted Chinese adversarial text, achieving a high success rate and ensuring readability of the Chinese.

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.

DNS over TLS (DoT) protects the confidentiality and integrity of DNS communication by encrypting DNS messages transmitted between users and resolvers. In recent years, DoT has been deployed by popular recursive resolvers like Cloudflare and Google. While DoT is supposed to prevent on-path adversaries from learning and tampering with victims' DNS requests and responses, it is unclear how much information can be deduced through traffic analysis on DoT messages. To answer this question, in this work, we develop a DoT fingerprinting method to analyze DoT traffic and determine if a user has visited websites of interest to adversaries. Given that a visit to a website typically introduces a sequence of DNS packets, we can infer the visited websites by modeling the temporal patterns of packet sizes. Our method can identify DoT traffic for websites with a false negative rate of less than 17% and a false positive rate of less than 0.5% when DNS messages are not padded. Moreover, we show that information leakage is still possible even when DoT messages are padded. These findings highlight the challenges of protecting DNS privacy, and indicate the necessity of a thorough analysis of the threats underlying DNS communications for effective defenses.

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.

An emerging Internet business is residential proxy (RESIP) as a service, in which a provider utilizes the hosts within residential networks (in contrast to those running in a datacenter) to relay their customers' traffic, in an attempt to avoid server- side blocking and detection. With the prominent roles the services could play in the underground business world, little has been done to understand whether they are indeed involved in Cybercrimes and how they operate, due to the challenges in identifying their RESIPs, not to mention any in-depth analysis on them. In this paper, we report the first study on RESIPs, which sheds light on the behaviors and the ecosystem of these elusive gray services. Our research employed an infiltration framework, including our clients for RESIP services and the servers they visited, to detect 6 million RESIP IPs across 230+ countries and 52K+ ISPs. The observed addresses were analyzed and the hosts behind them were further fingerprinted using a new profiling system. Our effort led to several surprising findings about the RESIP services unknown before. Surprisingly, despite the providers' claim that the proxy hosts are willingly joined, many proxies run on likely compromised hosts including IoT devices. Through cross-matching the hosts we discovered and labeled PUP (potentially unwanted programs) logs provided by a leading IT company, we uncovered various illicit operations RESIP hosts performed, including illegal promotion, Fast fluxing, phishing, malware hosting, and others. We also reverse engi- neered RESIP services' internal infrastructures, uncovered their potential rebranding and reselling behaviors. Our research takes the first step toward understanding this new Internet service, contributing to the effective control of their security risks.

Enterprises are targeted by various malware activities at a staggering rate. To counteract the increased sophistication of cyber attacks, most enterprises deploy within their perimeter a number of security technologies, including firewalls, antivirus software, and web proxies, as well as specialized teams of security analysts forming Security Operations Centers (SOCs).

QR-code mobile payment becomes increasingly popular, being offered by major banks (e.g., ICBC) and payment service providers (e.g., PayPal). Unlike mobile payment solutions provided by hardware vendors (e.g., Apple Pay and Samsung Pay), QR code payment schemes do not rely on any hardware support and can therefore be easily deployed. However, the security guarantee of the new scheme is less clear: in the absence of hardware protection, users' digital wallet can be vulnerable to an OS-level adversary, who could steal her secret for generating payment tokens.

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.

Input Method Editor (IME) is an indispensable component on current smartphones. With its assistance, the number of key presses is reduced, and non-Latin characters could be inputted. Furthermore, modern IMEs integrate several personalized features like reordering suggestion lists and predicting the next words based on user’s input history. Such optimization improves the user experience but turns the IME dictionary into a pool of user privacy. Previous works have discussed the privacy risks coming from malicious IMEs. Indeed, they could cause security and privacy issues if installed by common users, but their impact is limited as the majority of IMEs are well-behaved. However, whether legitimate IMEs are bullet-proof is not answered before. In this paper, we make the first attempt to study the security implications of IME personalization and the back-end infrastructure on Android devices. In the end, we identify a critical vulnerability lying under the Android KeyEvent processing framework, which can be exploited to launch cross-app KeyEvent injection (CAKI) attack and bypass the app-isolation mechanism. By abusing such design flaw, an adversary can harvest entries from the personalized user dictionary of IME through an ostensibly innocuous app only asking for common permissions. Our evaluation over a broad spectrum of Android OSes, devices, and IMEs suggests such issue should be fixed immediately. All Android versions we examined (from very old 2.3.4 to the latest 6.0.1) and most IME apps we surveyed (11 out of 18) are vulnerable. User’s private information, like contact names, location, etc., can be easily exfiltrated. Up to hundreds of millions of mobile users are under this threat. To mitigate this security issue, we propose a practical defense mechanism which augments the existing KeyEvent processing framework without forcing any change to IME apps.

Domain names have been exploited for illicit online activities for decades. In the past, miscreants mostly registered new domains for their attacks. However, the domains registered for malicious purposes can be deterred by existing reputation and blacklisting systems. In response to the arms race, miscreants have recently adopted a new strategy, called domain shadowing, to build their attack infrastructures. Specifically, instead of registering new domains, miscreants are beginning to compromise legitimate ones and spawn malicious subdomains under them. This has rendered almost all existing countermeasures ineffective and fragile because subdomains inherit the trust of their apex domains, and attackers can virtually spawn an infinite number of shadowed domains.

Single Root I/O Virtualization (SRIOV) allows one physical device to be used by multiple virtual machines simultaneously without the mediation from the hypervisor. Such technique significantly decreases the overhead of I/O virtualization. But according to our latest findings, in the meantime, it introduces a high-risk security issue that enables an adversary-controlled VM to cut off the connectivity of the host machine, given the limited filtering capabilities provided by the SRIOV devices.

Online underground economy is an important channel that connects the merchants of illegal products and their buyers, which is also constantly monitored by legal authorities. As one common way for evasion, the merchants and buyers together create a vocabulary of jargons (called "black keywords" in this paper) to disguise the transaction (e.g., "smack" is one street name for "heroin" [1]). Black keywords are often "unfriendly" to the outsiders, which are created by either distorting the original meaning of common words or tweaking other black keywords. Understanding black keywords is of great importance to track and disrupt the underground economy, but it is also prohibitively difficult: the investigators have to infiltrate the inner circle of criminals to learn their meanings, a task both risky and time-consuming. In this paper, we make the first attempt towards capturing and understanding the ever-changing black keywords. We investigated the underground business promoted through blackhat SEO (search engine optimization) and demonstrate that the black keywords targeted by the SEOers can be discovered through a fully automated approach. Our insights are two-fold: first, the pages indexed under black keywords are more likely to contain malicious or fraudulent content (e.g., SEO pages) and alarmed by off-the-shelf detectors, second, people tend to query multiple similar black keywords to find the merchandise. Therefore, we could infer whether a search keyword is "black" by inspecting the associated search results and then use the related search queries to extend our findings. To this end, we built a system called KDES (Keywords Detection and Expansion System), and applied it to the search results of Baidu, China's top search engine. So far, we have already identified 478,879 black keywords which were clustered under 1,522 core words based on text similarity. We further extracted the information like emails, mobile phone numbers and instant messenger IDs from the pages and domains relevant to the underground business. Such information helps us gain better understanding about the underground economy of China in particular. In addition, our work could help search engine vendors purify the search results and disrupt the channel of the underground market. Our co-authors from Baidu compared our results with their blacklist, found many of them (e.g., long-tail and obfuscated keywords) were not in it, and then added them to Baidu's internal blacklist.

Abstract According to previous reports, information could be leaked from GPU memory; however, the security implications of such a threat were mostly over-looked, because only limited information could be indirectly extracted through side-channel attacks. In this paper, we propose a novel algorithm for recovering raw data directly from the GPU memory residues of many popular applications such as Google Chrome and Adobe PDF reader. Our algorithm enables harvesting highly sensitive information including credit card numbers and email contents from GPU memory residues. Evaluation results also indicate that nearly all GPU-accelerated applications are vulnerable to such attacks, and adversaries can launch attacks without requiring any special privileges both on traditional multi-user operating systems, and emerging cloud computing scenarios.

To adapt to the rapidly evolving landscape of cyber threats, security professionals are actively exchanging Indicators of Compromise (IOC) (e.g., malware signatures, botnet IPs) through public sources (e.g. blogs, forums, tweets, etc.). Such information, often presented in articles, posts, white papers etc., can be converted into a machine-readable OpenIOC format for automatic analysis and quick deployment to various security mechanisms like an intrusion detection system. With hundreds of thousands of sources in the wild, the IOC data are produced at a high volume and velocity today, which becomes increasingly hard to manage by humans. Efforts to automatically gather such information from unstructured text, however, is impeded by the limitations of today's Natural Language Processing (NLP) techniques, which cannot meet the high standard (in terms of accuracy and coverage) expected from the IOCs that could serve as direct input to a defense system. In this paper, we present iACE, an innovation solution for fully automated IOC extraction. Our approach is based upon the observation that the IOCs in technical articles are often described in a predictable way: being connected to a set of context terms (e.g., "download") through stable grammatical relations. Leveraging this observation, iACE is designed to automatically locate a putative IOC token (e.g., a zip file) and its context (e.g., "malware", "download") within the sentences in a technical article, and further analyze their relations through a novel application of graph mining techniques. Once the grammatical connection between the tokens is found to be in line with the way that the IOC is commonly presented, these tokens are extracted to generate an OpenIOC item that describes not only the indicator (e.g., a malicious zip file) but also its context (e.g., download from an external source). Running on 71,000 articles collected from 45 leading technical blogs, this new approach demonstrates a remarkable performance: it generated 900K OpenIOC items with a precision of 95% and a coverage over 90%, which is way beyond what the state-of-the-art NLP technique and industry IOC tool can achieve, at a speed of thousands of articles per hour. Further, by correlating the IOCs mined from the articles published over a 13-year span, our study sheds new light on the links across hundreds of seemingly unrelated attack instances, particularly their shared infrastructure resources, as well as the impacts of such open-source threat intelligence on security protection and evolution of attack strategies.

Many new specialized hardware components have been integrated into Android smartphones to improve mobility and usability, such as touchscreen, Bluetooth module, and NFC controller. At the system level, the kernel of Android is built on Linux and inherits its device management mechanisms. However, the security implications surfaced from the integration of new hardware components and the tailored Linux kernel are not fully understood. In this paper, we make the first attempt to evaluate such implications. As a result, we identify a critical information leakage channel from the interrupt handling mechanism, which can be exploited to launch inference attacks without any permission. On Android, all reported interrupts are counted by Linux kernel and the statistical information is logged in a system file /proc/interrupts, which is public to any process. Such statistical information reveals the running status of all integrated devices, and could be exploited by attackers to infer sensitive information passing through them. To assess this new threat, we propose a general attack approach -- interrupt timing analysis and apply it to interrupt logs. As showcases, we present two concrete inference attacks against user's unlock pattern and foreground app status respectively. Through analyzing the interrupt time series produced from touchscreen controller, attacker's chance of cracking user's unlock pattern is increased substantially. The interrupt time series produced from Display Sub-System reveals unique UI refreshing patterns and could be leveraged as fingerprints to identify the app running in the foreground. Such information can serve as the stepping stone for the subsequent phishing attacks. The experiment results suggest our inference attacks are highly effective, and the risks should be mitigated immediately.

Dynamic analysis technique has been widely used in Android malware detection. Previous works on evading dynamic analysis focus on discovering the fingerprints of emulators. However, such method has been challenged since the introduction of real devices in recent works. In this paper, we propose a new approach to evade automated runtime analysis through detecting programmed interactions. This approach, in essence, tries to tell the identity of the current app controller (human user or automated exploration tool), by finding intrinsic differences between human user and machine tester in interaction patterns. The effectiveness of our approach has been demonstrated through evaluation against 11 real-world online dynamic analysis services.

One rising trend in today's consumer electronics is the wearable devices, e.g., smartwatches. With tens of millions of smartwatches shipped, however, the security implications of such devices are not fully understood. Although previous studies have pointed out some privacy concerns about the data that can be collected, like personalized health information, the threat is considered low as the leaked data is not highly sensitive and there is no real attack implemented. In this paper we investigate a security problem coming from sensors in smartwatches, especially the accelerometer. The results show that the actual threat is much beyond people's awareness. Being worn on the wrist, the accelerometer built within a smartwatch can track user's hand movements, which makes inferring user inputs on keyboards possible in theory. But several challenges need to be addressed ahead in the real-world settings: e.g., small and irregular hand movements occur persistently during typing, which degrades the tracking accuracy and sometimes even overwhelms useful signals.

Recent years have seen the rise of sophisticated attacks including advanced persistent threats (APT) which pose severe risks to organizations and governments. Additionally, new malware strains appear at a higher rate than ever before. Since many of these malware evade existing security products, traditional defenses deployed by enterprises today often fail at detecting infections at an early stage. We address the problem of detecting early-stage APT infection by proposing a new framework based on belief propagation inspired from graph theory. We demonstrate that our techniques perform well on two large datasets. We achieve high accuracy on two months of DNS logs released by Los Alamos National Lab (LANL), which include APT infection attacks simulated by LANL domain experts. We also apply our algorithms to 38TB of web proxy logs collected at the border of a large enterprise and identify hundreds of malicious domains overlooked by state-of-the-art security products.

While we have a good understanding of how cyber crime is perpetrated and the profits of the attackers, the harm experienced by humans is less well understood, and reducing this harm should be the ultimate goal of any security intervention. This paper presents a strategy for quantifying the harm caused by the cyber crime of typo squatting via the novel technique of intent inference. Intent inference allows us to define a new metric for quantifying harm to users, develop a new methodology for identifying typo squatting domain names, and quantify the harm caused by various typo squatting perpetrators. We find that typo squatting costs the typical user 1.3 seconds per typo squatting event over the alternative of receiving a browser error page, and legitimate sites lose approximately 5% of their mistyped traffic over the alternative of an unregistered typo. Although on average perpetrators increase the time it takes a user to find their intended site, many typo squatters actually improve the latency between a typo and its correction, calling into question the necessity of harsh penalties or legal intervention against this flavor of cyber crime.

Compromised websites that redirect web traffic to malicious hosts play a critical role in organized web crimes, serving as doorways to all kinds of malicious web activities (e.g., drive-by downloads, phishing etc.). They are also among the most elusive components of a malicious web infrastructure and extremely difficult to hunt down, due to the simplicity of redirect operations, which also happen on legitimate sites, and extensive use of cloaking techniques. Making the detection even more challenging is the recent trend of injecting redirect scripts into JavaScript (JS) files, as those files are not indexed by search engines and their infections are therefore more difficult to catch. In our research, we look at the problem from a unique angle: the adversary's strategy and constraints for deploying redirect scripts quickly and stealthily. Specifically, we found that such scripts are often blindly injected into both JS and HTML files for a rapid deployment, changes to the infected JS files are often made minimum to evade detection and also many JS files are actually JS libraries (JS-libs) whose uninfected versions are publicly available. Based upon those observations, we developed JsRED, a new technique for the automatic detection of unknown redirect-script injections. Our approach analyzes the difference between a suspicious JS-lib file and its clean counterpart to identify malicious redirect scripts and further searches for similar scripts in other JS and HTML files. This simple, lightweight approach is found to work effectively against redirect injection campaigns: our evaluation shows that JsRED captured most of compromised websites with almost no false positives, significantly outperforming a commercial detection service in terms of finding unknown JS infections. Based upon the compromised websites reported by JsRED, we further conducted a measurement study that reveals interesting features of redirect payloads and a new Peer-to-Peer network the adversary constructed to evade detection.

Side-channel information leaks have been reported in various online applications, especially, in wireless local area networks (WLANs) due to the shared-medium nature of wireless links and the ease of eavesdropping. Even when Wi-Fi traffic is encrypted, its characteristics are identifiable, which can be used to infer sensitive user activities and data. Existing countermeasures do not offer effective and efficient protection: packet padding and traffic morphing often bring in substantial communication overheads; attempts to anonymize user identifiers are vulnerable to the analysis based upon traffic statistics. In this paper, we present a new technique, called traffic demultiplexing, which offers effective protection against Wi-Fi traffic analysis without incurring noticeable overhead and performance degradation. Our approach utilizes Media Access Control (MAC) layer virtualization and packet scheduling over multiple virtual MAC interfaces to shape the traffic on each virtual MAC interface, so as to hide the original traffic characteristics. Traffic demultiplexing operates at the MAC layer and is transparent to users and other protocol stacks. We implemented our technique over Multiband Atheros Driver for Wi-Fi (MadWifi) and evaluated it in real WLAN environments. Our experimental study demonstrates that traffic demultiplexing is effective and efficient in defending against traffic analysis attacks and easy to deploy.

Malicious Web activities continue to be a major threat to the safety of online Web users. Despite the plethora forms of attacks and the diversity of their delivery channels, in the back end, they are all orchestrated through malicious Web infrastructures, which enable miscreants to do business with each other and utilize others' resources. Identifying the linchpins of the dark infrastructures and distinguishing those valuable to the adversaries from those disposable are critical for gaining an upper hand in the battle against them. In this paper, using nearly 4 million malicious URL paths crawled from different attack channels, we perform a large-scale study on the topological relations among hosts in the malicious Web infrastructure. Our study reveals the existence of a set of topologically dedicated malicious hosts that play orchestrating roles in malicious activities. They are well connected to other malicious hosts and do not receive traffic from legitimate sites. Motivated by their distinctive features in topology, we develop a graph-based approach that relies on a small set of known malicious hosts as seeds to detect dedicate malicious hosts in a large scale. Our method is general across the use of different types of seed data, and results in an expansion rate of over 12 times in detection with a low false detection rate of 2%. Many of the detected hosts operate as redirectors, in particular Traffic Distribution Systems (TDSes) that are long-lived and receive traffic from new attack campaigns over time. These TDSes play critical roles in managing malicious traffic flows. Detecting and taking down these dedicated malicious hosts can therefore have more impact on the malicious Web infrastructures than aiming at short-lived doorways or exploit sites.

With the Internet becoming the dominant channel for marketing and promotion, online advertisements are also increasingly used for illegal purposes such as propagating malware, scamming, click frauds, etc. To understand the gravity of these malicious advertising activities, which we call malvertising, we perform a large-scale study through analyzing ad-related Web traces crawled over a three-month period. Our study reveals the rampancy of malvertising: hundreds of top ranking Web sites fell victims and leading ad networks such as DoubleClick were infiltrated.

The wide use of Flash technologies makes the security risks posed by Flash content an increasingly serious issue. Such risks cannot be effectively addressed by the Flash player, which either completely blocks Flash content's access to web resources or grants it unconstrained access. Efforts to mitigate this threat have to face the practical challenges that Adobe Flash player is closed source, and any changes to it need to be distributed to a large number of web clients. We demonstrate in this paper, however, that it is completely feasible to avoid these hurdles while still achieving fine-grained control of the interactions between Flash content and its hosting page. Our solution is FIRM, a system that embeds an inline reference monitor (IRM) within the web page hosting Flash content. The IRM effectively mediates the interactions between the content and DOM objects, and those between different Flash applications, using the capability tokens assigned by the web designer. FIRM can effectively protect the integrity of its IRM and the confidentiality of capability tokens. It can be deployed without making any changes to browsers. Our evaluation based upon real-world web applications and Flash applications demonstrates that FIRM effectively protects valuable user information and incurs small overhead.

A web application is a "two-part" program, with its components deployed both in the browser and in the web server. The communication between these two components inevitably leaks out the program's internal states to those eavesdropping on its web traffic, simply through the side channel features of the communication such as packet length and timing, even if the traffic is entirely encrypted. Our recent study shows that such side-channel leaks are both fundamental and realistic: a set of popular web applications are found to disclose highly sensitive user data such as one's family incomes, health profiles, investment secrets and more through their side channels. Our study also shows that an significant improvement of the current web-application development practice is necessary to mitigate this threat. To answer this urgent call, we present in this paper a suite of new techniques for automatic detection and quantification of side-channel leaks in web applications. Our approach, called Sidebuster, can automatically analyze an application's source code to detect its side channels and then perform a rerun test to assess the amount of information disclosed through such channels (quantified as the entropy loss). Sidebuster has been designed to work on event-driven applications and can effectively handle the AJAX GUI widgets used in most web applications. In our research, we implemented a prototype of our technique for analyzing GWT applications and evaluated it using complicated web applications. Our study shows that Sidebuster can effectively identify the side-channel leaks in these applications and assess their severity, with a small overhead.

Mashup is a representative of Web 2.0 technology that needs both convenience of cross-domain access and protection against the security risks it brings in. Solutions proposed by prior research focused on mediating access to the data in different domains, but little has been done to control the use of the data after the access. In this paper, we present Mash-IF, a new technique for information-flow control within mashups. Our approach allows cross-domain communications within a browser, but disallows disclosure of sensitive information to remote parties without the user's permission. It mediates the cross-domain channels in existing mashups and works on the client without collaborations from other parties. Also of particular interest is a novel technique that automatically generates declassification rules for a script by statically analyzing its code. Such rules can be efficiently enforced through monitoring the script's call sequences and DOM operations.

In this paper, we present a new approach to performing important classes of genomic computations (e.g., search for homologous genes) that makes a significant step towards privacy protection in this domain. Our approach leverages a key property of the human genome, namely that the vast majority of it is shared across humans (and hence public), and consequently relatively little of it is sensitive. Based on this observation, we propose a privacy-protection framework that partitions a genomic computation, distributing the part on sensitive data to the data provider and the part on the pubic data to the user of the data. Such a partition is achieved through program specialization that enables a biocomputing program to perform a concrete execution on public data and a symbolic execution on sensitive data. As a result, the program is simplified into an efficient query program that takes only sensitive genetic data as inputs. We prove the effectiveness of our techniques on a set of dynamic programming algorithms fundamental to genomic computing. We develop a program transformation tool that automatically instruments a legacy program for specialization operations. We also demonstrate that our techniques can greatly facilitate secure multi-party computations on large biocomputing problems.

Trust model is an effort to guarantee secure and high quality interactions in P2P(peer-to-peer) file sharing systems. Traditional trust models always depend on past performance quantified in term of reputation or recommendation. This paper presents PerformTrust -a history and current performance based trust supporting framework which includes an intensive trust model for offering trustworthiness of peers quantitatively and comparatively based on a history performance feedback system and a current performance evaluating system with a decentralized implementation over a structured P2P network. PerformTrust has two main features. First, we introduce a virtual domain in every peer that would take responsible of collecting information about current performance of peers, including files integrity and peers response time. Second, PerformTrust considers both history and current performance of a peer for evaluating trustworthiness. Other contributions include adjustment scenario of the trust model, and a set of experiment that show the effectiveness and benefit of our approach.