Biblio
In this paper, we present a new secure message transmission scheme using hyperchaotic discrete primary and auxiliary chaotic systems. The novelty lies on the use of auxiliary chaotic systems for the encryption purposes. We have used the modified Henon hyperchaotic discrete-time system. The use of the auxiliary system allows generating the same keystream in the transmitter and receiver side and the initial conditions in the auxiliary systems combined with other transmitter parameters suffice the role of the key. The use of auxiliary systems will mean that the information of keystream used in the encryption function will not be present on the transmitted signal available to the intruders, hence the reconstructing of the keystream will not be possible. The encrypted message is added on to the dynamics of the transmitter using inclusion technique and the dynamical left inversion technique is employed to retrieve the unknown message. The simulation results confirm the robustness of the method used and some comments are made about the key space from the cryptographic viewpoint.
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.
Wearable personal health monitoring systems can offer a cost effective solution for human healthcare. These systems must provide both highly accurate, secured and quick processing and delivery of vast amount of data. In addition, wearable biomedical devices are used in inpatient, outpatient, and at home e-Patient care that must constantly monitor the patient's biomedical and physiological signals 24/7. These biomedical applications require sampling and processing multiple streams of physiological signals with strict power and area footprint. The processing typically consists of feature extraction, data fusion, and classification stages that require a large number of digital signal processing and machine learning kernels. In response to these requirements, in this paper, a low-power, domain-specific many-core accelerator named Power Efficient Nano Clusters (PENC) is proposed to map and execute the kernels of these applications. Experimental results show that the manycore is able to reduce energy consumption by up to 80% and 14% for DSP and machine learning kernels, respectively, when optimally parallelized. The performance of the proposed PENC manycore when acting as a coprocessor to an Intel Atom processor is compared with existing commercial off-the-shelf embedded processing platforms including Intel Atom, Xilinx Artix-7 FPGA, and NVIDIA TK1 ARM-A15 with GPU SoC. The results show that the PENC manycore architecture reduces the energy by as much as 10X while outperforming all off-the-shelf embedded processing platforms across all studied machine learning classifiers.
Ethernet technology dominates enterprise and home network installations and is present in datacenters as well as parts of the backbone of the Internet. Due to its wireline nature, Ethernet networks are often assumed to intrinsically protect the exchanged data against attacks carried out by eavesdroppers and malicious attackers that do not have physical access to network devices, patch panels and network outlets. In this work, we practically evaluate the possibility of wireless attacks against wired Ethernet installations with respect to resistance against eavesdropping by using off-the-shelf software-defined radio platforms. Our results clearly indicate that twisted-pair network cables radiate enough electromagnetic waves to reconstruct transmitted frames with negligible bit error rates, even when the cables are not damaged at all. Since this allows an attacker to stay undetected, it urges the need for link layer encryption or physical layer security to protect confidentiality.
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.
This article presents a systematic review on the challenges and recent progress of timing and carrier synchronization techniques for high-speed optical transmission systems using single-carrier-based coherent optical modulation formats.
Sampling and reconstruction (S&R) are used in virtually all areas of science and technology. The classical sampling theorem is a theoretical foundation of S&R. However, for a long time, only sampling rates and ways of the sampled signals representation were derived from it. The fact that the design of S&R circuits (SCs and RCs) is based on a certain interpretation of the sampling theorem was mostly forgotten. The traditional interpretation of this theorem was selected at the time of the theorem introduction because it offered the only feasible way of S&R realization then. At that time, its drawbacks did not manifest themselves. By now, this interpretation has largely exhausted its potential and inhibits future progress in the field. This tutorial expands the theoretical foundation of S&R. It shows that the traditional interpretation, which is indirect, can be replaced by the direct one or by various combinations of the direct and indirect interpretations that enable development of novel SCs and RCs (NSCs and NRCs) with advanced properties. The tutorial explains the basic principles of the NSCs and NRCs design, their advantages, as well as theoretical problems and practical challenges of their realization. The influence of the NSCs and NRCs on the architectures of SDRs and CRs is also discussed.
The main focus of this work is the estimation of a complex valued signal assumed to have a sparse representation in an uncountable dictionary of signals. The dictionary elements are parameterized by a real-valued vector and the available observations are corrupted with an additive noise. By applying a linearization technique, the original model is recast as a constrained sparse perturbed model. The problem of the computation of the involved multiple parameters is addressed from a nonconvex optimization viewpoint. A cost function is defined including an arbitrary Lipschitz differentiable data fidelity term accounting for the noise statistics, and an ℓ0-like penalty. A proximal algorithm is then employed to solve the resulting nonconvex and nonsmooth minimization problem. Experimental results illustrate the good practical performance of the proposed approach when applied to 2D spectrum analysis.