Biblio

Benefiting bythe large time-bandwidth product, chirp signals arefrequentlyadopted in modern radars. In this paper, the influence on thehigh-resolution range profile (HRRP) reconstruction of chirp waveform after sub-Nyquist sampling is investigated, where the (compressive sensing) CS-based dechirpingalgorithms are applied to achieve the range compression of the sub-Nyquist sampled chirp signals. The conditions that the HRRP can be recovered from the sub-Nyquist sampled chirp signals via CS-based dechirping are addressed. The simulated echoes, formed by the sub-Nyquist sampled chirp signals and scattered by moving targets, are collected by radars to yieldthe high-resolution range profile (HRRP) which validate the correctness of the analyses.

Using the sparse feature of the signal, compressed sensing theory can take a sample to compress data at a rate lower than the Nyquist sampling rate. The signal must be represented by the sparse matrix, however. Based on the above theory, this article puts forward a sparse degree of adaptive algorithms which can be used for the detection and reconstruction of the underwater target radiation signal. The received underwater target radiation signal, at first, transits the noise energy into signal energy under test by the stochastic resonance system, and then based on Gerschgorin disk criterion, it can make out the number of underwater target radiation signals in order to determine the optimal sparse degree of compressed sensing, and finally, the detection and reconstruction of the original signal can be realized by utilizing the compressed sensing technique. The simulation results show that this method can effectively detect underwater target radiation signals, and they can also be detected quite well under low signal-to-noise ratio(SNR).

Emerging and future HealthCare policies are fueling up an application-driven shift toward long-term monitoring of biosignals by means of embedded ultra-low power Wireless Body Sensor Networks (WBSNs). In order to break out, these applications needed the emergence of new technologies to allow the development of extremely power-efficient bio-sensing nodes. The PHIDIAS project aims at unlocking the development of ultra-low power bio-sensing WBSNs by tackling multiple and interlocking technological breakthroughs: (i) the development of new signal processing models and methods based on the recently proposed Compressive Sampling paradigm, which allows the design of energy-minimal computational architectures and analog front-ends, (ii) the efficient hardware implementation of components, both analog and digital, building upon an innovative ultra-low-power signal processing front-end, (iii) the evaluation of the global power reduction using a system wide integration of hardware and software components focused on compressed-sensing-based bio-signals analysis. PHIDIAS brought together a mixed consortium of academic and industrial research partners representing pan-European excellence in different fields impacting the energy-aware optimization of WBSNs, including experts in signal processing and digital/analog IC design. In this way, PHIDIAS pioneered a unique holistic approach, ensuring that key breakthroughs worked out in a cooperative way toward the global objective of the project.

Compressive sensing is a new technique by which sparse signals are sampled and recovered from a few measurements. To address the disadvantages of traditional space image compressing methods, a complete new compressing scheme under the compressive sensing framework was developed in this paper. Firstly, in the coding stage, a simple binary measurement matrix was constructed to obtain signal measurements. Secondly, the input image was divided into small blocks. The image blocks then would be used as training sets to get a dictionary basis for sparse representation with learning algorithm. At last, sparse reconstruction algorithm was used to recover the original input image. Experimental results show that both the compressing rate and image recovering quality of the proposed method are high. Besides, as the computation cost is very low in the sampling stage, it is suitable for on-board applications in astronomy.

Learning to rank (L2R) algorithms use a labeled training set to generate a ranking model that can be later used to rank new query results. These training sets are very costly and laborious to produce, requiring human annotators to assess the relevance or order of the documents in relation to a query. Active learning (AL) algorithms are able to reduce the labeling effort by actively sampling an unlabeled set and choosing data instances that maximize the effectiveness of a learning function. But AL methods require constant supervision, as documents have to be labeled at each round of the process. In this paper, we propose that certain characteristics of unlabeled L2R datasets allow for an unsupervised, compression-based selection process to be used to create small and yet highly informative and effective initial sets that can later be labeled and used to bootstrap a L2R system. We implement our ideas through a novel unsupervised selective sampling method, which we call Cover, that has several advantages over AL methods tailored to L2R. First, it does not need an initial labeled seed set and can select documents from scratch. Second, selected documents do not need to be labeled as the iterations of the method progress since it is unsupervised (i.e., no learning model needs to be updated). Thus, an arbitrarily sized training set can be selected without human intervention depending on the available budget. Third, the method is efficient and can be run on unlabeled collections containing millions of query-document instances. We run various experiments with two important L2R benchmarking collections to show that the proposed method allows for the creation of small, yet very effective training sets. It achieves full training-like performance with less than 10% of the original sets selected, outperforming the baselines in both effectiveness and scalability.

The World Wide Web has become the most common platform for building applications and delivering content. Yet despite years of research, the web continues to face severe security challenges related to data integrity and confidentiality. Rather than continuing the exploit-and-patch cycle, we propose addressing these challenges at an architectural level, by supplementing the web's existing connection-based and server-based security models with a new approach: content-based security. With this approach, content is directly signed and encrypted at rest, enabling it to be delivered via any path and then validated by the browser. We explore how this new architectural approach can be applied to the web and analyze its security benefits. We then discuss a broad research agenda to realize this vision and the challenges that must be overcome.

SSL and TLS are used to secure the most commonly used Internet protocols. As a result, the ecosystem of SSL certificates has been thoroughly studied, leading to a broad understanding of the strengths and weaknesses of the certificates accepted by most web browsers. Prior work has naturally focused almost exclusively on "valid" certificates–those that standard browsers accept as well-formed and trusted–and has largely disregarded certificates that are otherwise "invalid." Surprisingly, however, this leaves the majority of certificates unexamined: we find that, on average, 65% of SSL certificates advertised in each IPv4 scan that we examine are actually invalid. In this paper, we demonstrate that despite their invalidity, much can be understood from these certificates. Specifically, we show why the web's SSL ecosystem is populated by so many invalid certificates, where they originate from, and how they impact security. Using a dataset of over 80M certificates, we determine that most invalid certificates originate from a few types of end-user devices, and possess dramatically different properties than their valid counterparts. We find that many of these devices periodically reissue their (invalid) certificates, and develop new techniques that allow us to track these reissues across scans. We present evidence that this technique allows us to uniquely track over 6.7M devices. Taken together, our results open up a heretofore largely-ignored portion of the SSL ecosystem to further study.

Cross-site scripting (XSS) attacks keep plaguing the Web. Supported by most modern browsers, Content Security Policy (CSP) prescribes the browser to restrict the features and communication capabilities of code on a web page, mitigating the effects of XSS.

This paper puts a spotlight on the problem of data exfiltration in the face of CSP. We bring attention to the unsettling discord in the security community about the very goals of CSP when it comes to preventing data leaks.

As consequences of this discord, we report on insecurities in the known protection mechanisms that are based on assumptions about CSP that turn out not to hold in practice.

To illustrate the practical impact of the discord, we perform a systematic case study of data exfiltration via DNS prefetching and resource prefetching in the face of CSP.

Our study of the popular browsers demonstrates that it is often possible to exfiltrate data by both resource prefetching and DNS prefetching in the face of CSP. Further, we perform a crawl of the top 10,000 Alexa domains to report on the cohabitance of CSP and prefetching in practice. Finally, we discuss directions to control data exfiltration and, for the case study, propose measures ranging from immediate fixes for the clients to prefetching-aware extensions of CSP.

Modern web browsers are incredibly complex, with millions of lines of code and over one thousand JavaScript functions and properties available to website authors. This work investigates how these browser features are used on the modern, open web. We find that JavaScript features differ wildly in popularity, with over 50% of provided features never used on the web's 10,000 most popular sites according to Alexa We also look at how popular ad and tracking blockers change the features used by sites, and identify a set of approximately 10% of features that are disproportionately blocked (prevented from executing by these extensions at least 90% of the time they are used). We additionally find that in the presence of these blockers, over 83% of available features are executed on less than 1% of the most popular 10,000 websites. We further measure other aspects of browser feature usage on the web, including how many features websites use, how the length of time a browser feature has been in the browser relates to its usage on the web, and how many security vulnerabilities have been associated with related browser features.

Browser fingerprinting is a widely used technique to uniquely identify web users and to track their online behavior. Until now, different tools have been proposed to protect the user against browser fingerprinting. However, these tools have usability restrictions as they deactivate browser features and plug-ins (like Flash) or the HTML5 canvas element. In addition, all of them only provide limited protection, as they randomize browser settings with unrealistic parameters or have methodical flaws, making them detectable for trackers. In this work we demonstrate the first anti-fingerprinting strategy, which protects against Flash fingerprinting without deactivating it, provides robust and undetectable anti-canvas fingerprinting, and uses a large set of real word data to hide the actual system and browser properties without losing usability. We discuss the methods and weaknesses of existing anti-fingerprinting tools in detail and compare them to our enhanced strategies. Our evaluation against real world fingerprinting tools shows a successful fingerprinting protection in over 99% of 70.000 browser sessions.

Modern websites use multiple authentication cookies to allow visitors to the site different levels of access. The complexity of modern web applications can make it difficult for a web application programmer to ensure that the use of authentication cookies does not introduce vulnerabilities. Even when a programmer has access to all of the source code, this analysis can be challenging; the problem becomes even more vexing when web programmers cobble together off-the-shelf libraries to implement authentication. We have assembled a checklist for modern web programmers to verify that the cookie based authentication mechanism is securely implemented. Then, we developed a tool, Newton, to help a web application programmer to identify authentication cookies for specific parts of the website and to verify that they are securely implemented according to the checklist. We used Newton to analyze 149 sites, including the Alexa top-200 and many other popular sites across a range of categories including search, shopping, and finance. We found that 113 of them–-including high-profile sites such as Yahoo, Amazon, and Fidelity–-were vulnerable to hijacking attacks. Many websites have already acknowledged and fixed the vulnerabilities that we found using Newton and reported to them.

Content Security Policy (CSP) is an emerging W3C standard introduced to mitigate the impact of content injection vulnerabilities on websites. We perform a systematic, large-scale analysis of four key aspects that impact on the effectiveness of CSP: browser support, website adoption, correct configuration and constant maintenance. While browser support is largely satisfactory, with the exception of few notable issues, our analysis unveils several shortcomings relative to the other three aspects. CSP appears to have a rather limited deployment as yet and, more crucially, existing policies exhibit a number of weaknesses and misconfiguration errors. Moreover, content security policies are not regularly updated to ban insecure practices and remove unintended security violations. We argue that many of these problems can be fixed by better exploiting the monitoring facilities of CSP, while other issues deserve additional research, being more rooted into the CSP design.

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.

The market of wearable healthcare monitoring devices has exploded in recent years as healthcare consciousness has increased. These types of devices usually consist of several biosensors, which can be worn on human bodies, such as the head, arms, and feet. The health status of a user can be analyzed according to the user's real-time vital signs that are collected from different biosensors. Due to personal medical data being transmitted through a wireless network, the data have to be encrypted. In this paper, a key agreement protocol for biosensors within Wireless Body Sensor Networks (WBSN) has been proposed based on the n-Party Diffie-Hellman key exchange protocol. In order to prevent the man-in-the-middle attacks, we have used Advance Encryption Standard (AES) encryption with Electrocardiography-based (ECG-based) keys to secure the key exchange procedures. The security and performance analysis show the feasibility of the proposed method.

Proliferation of electronics and their increasing connectivity pose formidable challenges for information security. At the most fundamental level, nanostructures and nanomaterials offer an unprecedented opportunity to introduce new approaches to securing electronic devices. First, we discuss engineering nanomaterials, (e.g., carbon nanotubes (CNTs), graphene, and layered transition metal dichalcogenides (TMDs)) to make unclonable cryptographic primitives. These security primitives not only can supplement existing solutions in silicon integrated circuits (ICs) but can also be used for emerging applications in flexible and wearable electronics. Second, we discuss security engineering of advanced nanostructures such as reactive materials.

The proliferation of wearable devices, e.g., smartwatches and activity trackers, with embedded sensors has already shown its great potential on monitoring and inferring human daily activities. This paper reveals a serious security breach of wearable devices in the context of divulging secret information (i.e., key entries) while people accessing key-based security systems. Existing methods of obtaining such secret information relies on installations of dedicated hardware (e.g., video camera or fake keypad), or training with labeled data from body sensors, which restrict use cases in practical adversary scenarios. In this work, we show that a wearable device can be exploited to discriminate mm-level distances and directions of the user's fine-grained hand movements, which enable attackers to reproduce the trajectories of the user's hand and further to recover the secret key entries. In particular, our system confirms the possibility of using embedded sensors in wearable devices, i.e., accelerometers, gyroscopes, and magnetometers, to derive the moving distance of the user's hand between consecutive key entries regardless of the pose of the hand. Our Backward PIN-Sequence Inference algorithm exploits the inherent physical constraints between key entries to infer the complete user key entry sequence. Extensive experiments are conducted with over 5000 key entry traces collected from 20 adults for key-based security systems (i.e. ATM keypads and regular keyboards) through testing on different kinds of wearables. Results demonstrate that such a technique can achieve 80% accuracy with only one try and more than 90% accuracy with three tries, which to our knowledge, is the first technique that reveals personal PINs leveraging wearable devices without the need for labeled training data and contextual information.

Recent computing paradigms like cloud computing and big data have become very appealing to outsource computation and storage, making it easier to realize personalized and patient centric healthcare through real-time analytics on user data. Although these technologies can significantly complement resource constrained mobile and wearable devices to store and process personal health information, privacy concerns are keeping patients from reaping the full benefits. In this paper, we present and evaluate a practical smart-watch based lifelog application for diabetics that leverages the cloud and homomorphic encryption for caregivers to analyze blood glucose, insulin values, and other parameters in a privacy friendly manner to ensure confidentiality such that even a curious cloud service provider remains oblivious of sensitive health data.

In this demo, we will display a smartphone authentication system that can automatically validate every touch interaction made on a smartphone using a smart watch worn by the phone's owner. The IMU sensors on a smart watch monitor the motion of the hand for specific signal characteristics, which is relayed to the phone. If the signal features match certain criteria then the touch is authenticated and the phone responds appropriately. If not, the phone's screen remains locked/unresponsive to the touch action. The challenge here is to be able to validate every touch gesture within acceptable limits of human perception.

Wearable devices, such as smartwatches, are furnished with state-of-the-art sensors that enable a range of context-aware applications. However, malicious applications can misuse these sensors, if access is left unaudited. In this paper, we demonstrate how applications that have access to motion or inertial sensor data on a modern smartwatch can recover text typed on an external QWERTY keyboard. Due to the distinct nature of the perceptible motion sensor data, earlier research efforts on emanation based keystroke inference attacks are not readily applicable in this scenario. The proposed novel attack framework characterizes wrist movements (captured by the inertial sensors of the smartwatch worn on the wrist) observed during typing, based on the relative physical position of keys and the direction of transition between pairs of keys. Eavesdropped keystroke characteristics are then matched to candidate words in a dictionary. Multiple evaluations show that our keystroke inference framework has an alarmingly high classification accuracy and word recovery rate. With the information recovered from the wrist movements perceptible by a smartwatch, we exemplify the risks associated with unaudited access to seemingly innocuous sensors (e.g., accelerometers and gyroscopes) of wearable devices. As part of our efforts towards preventing such side-channel attacks, we also develop and evaluate a novel context-aware protection framework which can be used to automatically disable (or downgrade) access to motion sensors, whenever typing activity is detected.

Securely pairing wearables with another device is the key to many promising applications, such as mobile payment, sensitive data transfer and secure interactions with smart home devices. This paper presents Touch-And-Guard (TAG), a system that uses hand touch as an intuitive manner to establish a secure connection between a wristband wearable and the touched device. It generates secret bits from hand resonant properties, which are obtained using accelerometers and vibration motors. The extracted secret bits are used by both sides to authenticate each other and then communicate confidentially. The ubiquity of accelerometers and motors presents an immediate market for our system. We demonstrate the feasibility of our system using an experimental prototype and conduct experiments involving 12 participants with 1440 trials. The results indicate that we can generate secret bits at a rate of 7.84 bit/s, which is 58% faster than conventional text input PIN authentication. We also show that our system is resistant to acoustic eavesdroppers in proximity.

In this study, we present WindTalker, a novel and practical keystroke inference framework that allows an attacker to infer the sensitive keystrokes on a mobile device through WiFi-based side-channel information. WindTalker is motivated from the observation that keystrokes on mobile devices will lead to different hand coverage and the finger motions, which will introduce a unique interference to the multi-path signals and can be reflected by the channel state information (CSI). The adversary can exploit the strong correlation between the CSI fluctuation and the keystrokes to infer the user's number input. WindTalker presents a novel approach to collect the target's CSI data by deploying a public WiFi hotspot. Compared with the previous keystroke inference approach, WindTalker neither deploys external devices close to the target device nor compromises the target device. Instead, it utilizes the public WiFi to collect user's CSI data, which is easy-to-deploy and difficult-to-detect. In addition, it jointly analyzes the traffic and the CSI to launch the keystroke inference only for the sensitive period where password entering occurs. WindTalker can be launched without the requirement of visually seeing the smart phone user's input process, backside motion, or installing any malware on the tablet. We implemented Windtalker on several mobile phones and performed a detailed case study to evaluate the practicality of the password inference towards Alipay, the largest mobile payment platform in the world. The evaluation results show that the attacker can recover the key with a high successful rate.

We develop and evaluate a data hiding method that enables smartphones to encrypt and embed sensitive information into carrier streams of sensor data. Our evaluation considers multiple handsets and a variety of data types, and we demonstrate that our method has a computational cost that allows real-time data hiding on smartphones with negligible distortion of the carrier stream. These characteristics make it suitable for smartphone applications involving privacy-sensitive data such as medical monitoring systems and digital forensics tools.

When filling out privacy-related forms in public places such as hospitals or clinics, people usually are not aware that the sound of their handwriting leaks personal information. In this paper, we explore the possibility of eavesdropping on handwriting via nearby mobile devices based on audio signal processing and machine learning. By presenting a proof-of-concept system, WritingHacker, we show the usage of mobile devices to collect the sound of victims' handwriting, and to extract handwriting-specific features for machine learning based analysis. WritingHacker focuses on the situation where the victim's handwriting follows certain print style. An attacker can keep a mobile device, such as a common smart-phone, touching the desk used by the victim to record the audio signals of handwriting. Then the system can provide a word-level estimate for the content of the handwriting. To reduce the impacts of various writing habits and writing locations, the system utilizes the methods of letter clustering and dictionary filtering. Our prototype system's experimental results show that the accuracy of word recognition reaches around 50% - 60% under certain conditions, which reveals the danger of privacy leakage through the sound of handwriting.

Hardware security has emerged as an important topic in the wake of increasing threats on integrated circuits which include reverse engineering, intellectual property (IP) piracy and overbuilding. This paper explores obfuscation of circuits as a hardware security measure and specifically targets digital signal processing (DSP) circuits which are part of most modern systems. The idea of using desired and undesired modes to design obfuscated DSP functions is illustrated using the fast Fourier transform (FFT) as an example. The selection of a mode is dependent on a key input to the circuit. The system is said to work in its desired mode of operation only if the correct key is applied. Other undesired modes are built into the design to confuse an adversary. The approach to obfuscating the design involves control-flow modifications which alter the computations from the desired mode. We present simulation and synthesis results on a reconfigurable, 2-parallel FFT and discuss the security of this approach. It is shown that the proposed approach results in a reconfigurable and flexible design at an area overhead of 8% and a power overhead of 10%.

In this paper, we address the design an implementation of low power embedded systems for real-time tracking of humans and vehicles. Such systems are important in applications such as activity monitoring and border security. We motivate the utility of mobile devices in prototyping the targeted class of tracking systems, and demonstrate a dataflow-based and cross-platform design methodology that enables efficient experimentation with key aspects of our tracking system design, including real-time operation, experimentation with advanced sensors, and streamlined management of design versions on host and mobile platforms. Our experiments demonstrate the utility of our mobile-device-targeted design methodology in validating tracking algorithm operation; evaluating real-time performance, energy efficiency, and accuracy of tracking system execution; and quantifying trade-offs involving use of advanced sensors, which offer improved sensing accuracy at the expense of increased cost and weight. Additionally, through application of a novel, cross-platform, model-based design approach, our design requires no change in source code when migrating from an initial, host-computer-based functional reference to a fully-functional implementation on the targeted mobile device.