Physical Characterization of Arbiter PUFs

Memory-based physical unclonable functions (mPUFs) are widely accepted as highly secure because of the unclonable and immutable nature of manufacturer process variations. Although numerous successful attacks have been proposed against PUFs, mPUFs are resistant to non-invasive attacks as the mPUF does not support the open-access protocol. Hence, existing attacks against mPUFs mostly rely on invasive/semi-invasive techniques or at least require physical access to the target device, which is not always feasible. In this paper, we experimentally demonstrate that signatures generated from two memory chips may have highly correlated properties if they possess the same set of specifications and a similar manufacturing facility, which is used to mount a non-invasive attack against memory-based PUFs. Our proposed technique shows that if an attacker has access to a device similar to the victim's one, the attacker might be able to guess up to ~45% of the challenge-response pairs of a 64-bit SRAM PUF.

Electronic payment systems have leveraged the advantages offered by the RFID technology, whose security is promised to be improved by applying the notion of Physically Unclonable Functions (PUFs). Along with the evolution of PUFs, numerous successful attacks against PUFs have been proposed in the literature. Among these are machine learning (ML) attacks, ranging from heuristic approaches to provable algorithms, that have attracted great attention. Our paper pursues this line of research by introducing a Fourier analysis based attack against PUFs. More specifically, this paper focuses on two main aspects of ML attacks, namely being provable and noise tolerant. In this regard, we prove that our attack is naturally integrated into a provable Probably Approximately Correct (PAC) model. Moreover, we show that our attacks against known PUF families are effective and applicable even in the presence of noise. Our proof relies heavily on the intrinsic properties of these PUF families, namely arbiter, Ring Oscillator (RO), and Bistable Ring (BR) PUF families. We believe that our new style of ML algorithms, which take advantage of the Fourier analysis principle, can offer better measures of PUF security.

In this chapter, we demonstrate that the primary security assumption on the infeasibility of direct delay measurements in delay-based PUFs is not valid. By performing an experiment on a PoC Arbiter PUF implementation on reconfigurable hardware, it becomes apparent that The Arbiter PUF family and more generally, the delay-based PUFs can be characterized by a high-resolution temporal photonic emission analysis. This approach does require neither any access to the PUF’s response nor a significant number of challenges to characterizing the PUF.

This paper presents a mathematical modeling with parameters identification of Unmanned Aerial Vehicle (UAV) system or hexarotor system using the Hamiltonian approach. The mathematical model of the hexarotor is derived from the Hamiltonian approach which involved the storage, dissipation, and routing of energy elements from the UAV. This UAV model parameters identification method is proposed as an alternative to the commonly used wind tunnel testing, which is complex and tedious. This Hamiltonian model is made of a fully actuated subsystem with roll, pitch, and yaw angles as output, as well as an under-actuated subsystem with position coordinates as its output. Thrust constant, drag constant and speed of hexarotor are determined through the experimental setup while moment of inertia is determined by physical measurement and calculation. The outcome from this research works demonstrates an undemanding, yet effective method of modeling an UAV, and is useful for designing nonlinear controller to perform the important UAV tasks such as taking off, hovering, and landing.

My research studies the relation between trust and conformity with a group of robots and what specific aspects of robots causes people to trust in them. An experimental setup was devised which allows us to measure if a group of robots can cause conformity in participants. In our setup, participants play a game in which they give a preliminary and a final answer. We measure how often participants change their answer to match the answer of the group of robots. Results in preliminary studies show that robots are capable of causing conformity and that trust in the robots plays a role in deciding to conform. Future research will study different factors (for example the type of robots or the size of group) that play a role in deciding whether to trust and therefore conform to a group of robots.

Classic cloud data sharing systems are based on a network architecture and large storage providers. However, these providers are responsible for transferring and storing the data. This type of cloud storage model has some weaknesses, such as data availability, centralized data storage, high operational cost and security issues. In this paper, we study the security problem related to the data stored in the cloud by focusing on the use of blockchain combined with the attribute-based encryption (ABE) algorithm. The studied system meets the security requirements of the cloud, such as confidentiality and non-forgery. In addition, the smart contract solves the problem of cloud storage, such as the return of erroneous results as in the traditional cloud server.

We present an experimental set-up permitting Raman and luminescence spectroscopy studies in a commercial Physical Properties Measurement System (PPMS) from Quantum Design. Using this experimental set-up, gaseous, liquid and solid materials, in bulk or thin film form, may be investigated. The set-up is particularly suitable for the study of the spin-lattice coupling in strongly correlated oxide materials utilizing several different stimuli, e.g. magnetic and electric fields, high pressure and low temperatures. Details for the Raman extension, sample holder assembly and optical design, as well as data acquisition and measurement routine are described. Finally, we present exemplary results collected using the set-up, measured on reference materials, as well as on a correlated transition metal oxide.

SummaryCognitive radio (CR) is an emerging technology for optimum spectrum utilization. Spectrum sensing (SS) is the first step of CR. Important challenge in the next generations is wideband SS. Compressive sensing (CS) can be used as an SS in CR to solve this challenge. Sensing matrix (SM) plays an important role in CS. For better performance, SM must have low mutual coherence. The existing matrices in the literature are random and chaotic. All random matrices have a good performance, but they have problems of large memory storage, complexity, sharing bandwidth, and low security. Chaotic matrices (CMs) are the best but still have some problems including the use of sample distance in chaotic sequence that consumes high storage resources, low performance and compression under low signal‐to‐noise ratio (SNR), and low security. In this paper, we propose a new 1‐D chaotic map for constructing a novel SM. The proposed map has simple structure, wide range chaotic behavior, and high security. Its chaotic behavior is confirmed using Lyapunov exponent, bifurcation, and trajectory. The CS performance based on novel CM is measured using absolute error ( , mutual coherence, memory cost, computational complexity, and MSE (mean square error), while evaluated through comparisons with matrices in the literature. The simulation and mathematical results show that the proposed map is a hyperchaotic with efficient chaotic behavior in wide range of parameters and low memory storage and complexity. The results also show that it has low , MSE, and mutual coherence at low SNR with high compression.

The purpose of this study is to help reduce high noise levels experienced by patients in intensive care unit (ICU) environments. The study has designed and developed the active noise control (ANC) system based on filtered x least mean square (FxLMS) to reduce the interfering noise around ICU patients’ beds. A multi-channel feed-forward ANC algorithm has been implemented in a real-time digital signal processing system based on TMS320 DSP C6713. This noise cancellation system has been tested in a patient bed with recorded real ICU noise as the noise source to mimic the ICU noisy environment. The experiment setup has been presented. The real-time experiment results show that ANC is a promising solution to solve high noise level problem inside ICU.

The development of technologies leads to the expansion of the range of services provided on the Internet, the online business is actively developing. As a rule, when creating a new Web resource for business, the main emphasis is on the need to stand out among the sites of competitors. Often, the owners of Web resources understood the possible consequences of cyber-incident only after when their resource was attacked. This paper discusses the frequent causes of attacks of Web-applications created with the content management systems. A content management system allows to create sites without directly writing code. The main sources of information about frequent security problems of Web-resources are documents of organizations OWASP, SANS, Positive Technologies. Due to the high activity of intruders, it is necessary to create methods for assessing the security of the Web-application and methods for countering attacks. In the paper, the need to assess the probability of a successful attack of Web-applications is conditioned. In practice, it is impossible to determine all possible attack scenarios, because each Web-application has its own functionality. The frequent attack scenarios on which the tree was built are investigated. The method of estimating the probabilities of basic events using expert assessments based on the results of the preliminary implementation of a set of measures to identify the security problems is used. The developed method of assessing security allows to consider not only possible vulnerabilities in the source code, but also possible security policy violations. The proposed method can be applied by business entities working in the field of information security, when choosing security measures for a particular Web-application. A further direction of research is the development of a method for choosing countermeasures based on the described method. The method should demonstrate the effect of each countermeasure on the probability of a successful attack

Finding differences between programs with similar functionality is an important security problem as such differences can be used for fingerprinting or creating evasion attacks against security software like Web Application Firewalls (WAFs) which are designed to detect malicious inputs to web applications. In this paper, we present SFADIFF, a black-box differential testing framework based on Symbolic Finite Automata (SFA) learning. SFADIFF can automatically find differences between a set of programs with comparable functionality. Unlike existing differential testing techniques, instead of searching for each difference individually, SFADIFF infers SFA models of the target programs using black-box queries and systematically enumerates the differences between the inferred SFA models. All differences between the inferred models are checked against the corresponding programs. Any difference between the models, that does not result in a difference between the corresponding programs, is used as a counterexample for further refinement of the inferred models. SFADIFF's model-based approach, unlike existing differential testing tools, also support fully automated root cause analysis in a domain-independent manner. We evaluate SFADIFF in three different settings for finding discrepancies between: (i) three TCP implementations, (ii) four WAFs, and (iii) HTML/JavaScript parsing implementations in WAFs and web browsers. Our results demonstrate that SFADIFF is able to identify and enumerate the differences systematically and efficiently in all these settings. We show that SFADIFF is able to find differences not only between different WAFs but also between different versions of the same WAF. SFADIFF is also able to discover three previously-unknown differences between the HTML/JavaScript parsers of two popular WAFs (PHPIDS 0.7 and Expose 2.4.0) and the corresponding parsers of Google Chrome, Firefox, Safari, and Internet Explorer. We confirm that all these differences can be used to evade the WAFs and launch successful cross-site scripting attacks.

Sensor applications often leverage in-network aggregation to extract aggregates, such as predicate count and average, from the network. With in-network aggregation, a malicious sensor can easily manipulate the intermediate aggregation results and corrupt the final answer. Most existing secure aggregation schemes aim to defend against stealth attacks and can only raise an alarm when the final answer is corrupted, without being able to pinpoint and revoke the malicious sensors. While some recent protocols can pinpoint and revoke malicious sensors, they need to rely on expensive public key cryptography to be robust against certain attacks. Using only symmetric key cryptography, this paper aims to strictly diminish the capability of adversaries whenever they launch a successful attack, so that malicious sensors can only ruin the aggregation result for a small number of times before they are fully revoked. To this end, we propose VMAT (verifiable minimum with audit trail), a novel secure aggregation protocol with malicious sensor revocation capability. VMAT relies on symmetric key cryptography only, and provides provable guarantees that each execution can either produce the correct aggregation result efficiently, or revoke some key held by the adversary.

To define the physical parameters needed to characterize a particle beam in order to allow intercomparison of different experiments performed using different ions at the same facility and using the same ion at different facilities. At the request of the National Cancer Institute (NCI), a special panel was convened to review the current status of the field and to provide suggested metrics for reporting the physical parameters of particle beams to be used for biological research. A set of physical parameters and measurements that should be performed by facilities and understood and reported by researchers supported by NCI to perform pre-clinical radiobiology and medical physics of heavy ions were generated. Standard measures such as radiation delivery technique, beam modifiers used, nominal energy, field size, physical dose and dose rate should all be reported. However, more advanced physical measurements, including detailed characterization of beam quality by microdosimetric spectrum and fragmentation spectra, should also be established and reported. Details regarding how such data should be incorporated into Monte Carlo simulations and the proper reporting of simulation details are also discussed. In order to allow for a clear relation of physical parameters to biological effects, facilities and researchers should establish and report detailed physical characteristics of the irradiation beams utilized including both standard and advanced measures. Biological researchers are encouraged to actively engage facility staff and physicists in the design and conduct of experiments. Modeling individual experimental setups will allow for the reporting of the uncertainties in the measurement or calculation of physical parameters which should be routinely reported.

The axioms of insecurity on commodity computer systems [1] suggest that an adversary will have an asymmetric advantage over any defender forever. This implies that the defender-adversary arms race on such systems always favors the adversary, as often emphasized by conventional security wisdom. In this presentation, I illustrate how a defender can win against any adversary by establishing root of trust on a commodity system unconditionally; e.g., without any tradeoffs. Then I will show how to maintain the defender's advantage in protecting selected applications, and explain why this is still uncommon on commodity systems.Establishing root of trust unconditionally. Suppose that a small and simple trusted verifier must boot a trustworthy program on a system that may contain persistent malware. Establishing root of trust (RoT) assures the system has all and only the content chosen by a trusted verifier or the verifier discovers unaccounted content, with high probability. Hence, verifiable boot takes place in a malware-free state. Obtaining such assurance is challenging because a remote adversary's malware can survive repeated secure- and trusted-boot operations and detection by any anti-malware tool; e.g., these tools do not have malware-unmediated access to device controllers' firmware nor prevent remote malware connections over the internet. In this presentation, I will show how to establish RoT unconditionally; i.e., without secrets, trusted hardware modules (e.g., TPMs, RoMs, HSMs), or adversary computation bounds. I will also argue that this is the only unconditional solution to any security or cryptography problem to date [2].Maintaining root of trust selectively. Establishing root of trust makes all persistent malware ephemeral and forces the adversary to repeat a malware-insertion attack. Nevertheless repeated successful attacks in commodity systems and applications are hard to deny because of the inherent size and complexity of their software components; aka, the giants [1, 3]. To win against an adversary, small and simple software components with rather limited function and high-assurance security properties (aka, the wimps) must be available, since they can, in principle, counter all attacks [3]. In this setting, maintaining root of trust selectively assures a defender that a commodity computer's wimps are isolated from, and safely co-exist with, adversary-controlled giants.Maintaining RoT selectively also implies that, regardless how secure wimp isolation may be, I/O separation for wimps must also be provably achieved despite use of commodity systems that encourage I/O hardware sharing, not isolation [4]. In this presentation I will also illustrate the basic challenges of I/O separation for wimps and giants, and present an example of an experimental system for on-demand separated I/O transfers, which was designed and implemented at CMU's CyLab.

The problem of information security is instantaneous, which involves the basic problems of science and technology and development. The analysis and integration of traditional information security big data are completed manually, which increases the time cost and labor cost to a certain extent, and greatly prolongs the time problem that information security incidents need to be processed in real time. Based on the above problems, an algorithm model is constructed to deal with the multi-source problem of information security big data with information technology. Quickly analyze and fuse similar data. In order to meet the actual requirements, this paper gives the corresponding algorithm process for data preprocessing, data correlation and other issues, mainly including noise filtering, correlation analysis and logic function for statistical logic problems caused by different amounts of data, and gives solutions to solve the basic data analysis layer problems. In view of the storage problem, it is necessary to judge the fusion degree of information security data, select the data in the form of database, add time series labels and then store them, which makes the security events have natural physical labels, and facilitate database operations such as output and retrieval. The construction of the algorithm can not only make an issue in theory, but also need to gather practice, which is the only and effective way to test the performance of the algorithm. Therefore, this paper puts forward seven simulation perspectives based on the common problems of information security big data, and takes several data evaluation criteria as simulation objects to explore and lay a theoretical foundation and experimental data for the long-term development of the algorithm. The full-text construction data analysis layer algorithm faces not cold data but social information with practical utility. Excellent analysis ability provides favorable and real objective underlying data for social decision makers, and provides substantive help for decision-making.