Cryptanalysis of Symmetric Cryptographic Primitives

Symmetric key cryptographic primitives are the essential building blocks in modern information security systems. The overall security of such systems is crucially dependent on these mathematical functions, which makes the analysis of symmetric key primitives a goal of critical importance. The security argument for the majority of such primitives in use is only a heuristic one and therefore their respective security evaluation continually remains an open question. In this thesis, we provide cryptanalytic results for several relevant cryptographic hash functions and stream ciphers. First, we provide results concerning two hash functions: HAS-160 and SM3. In particular, we develop a new heuristic for finding compatible differential paths and apply it to the the Korean hash function standard HAS-160. Our heuristic leads to a practical second order collision attack over all of the HAS-160 function steps, which is the first practical-complexity distinguisher on this function. An example of a colliding quartet is provided. In case of SM3, which is a design that builds upon the SHA-2 hash and is published by the Chinese Commercial Cryptography Administration Office for the use in the electronic authentication service system, we study second order collision attacks over reduced-round versions and point out a structural slide-rotational property that exists in the function. Next, we examine the security of the following three stream ciphers: Loiss, SNOW 3G and SNOW 2.0. Loiss stream cipher is designed by Dengguo Feng et al. aiming to be implemented in byte-oriented processors. By exploiting some differential properties of a particular component utilized in the cipher, we provide an attack of a practical complexity on Loiss in the related-key model. As confirmed by our experimental results, our attack recovers 92 bits of the 128-bit key in less than one hour on a PC with 3 GHz Intel Pentium 4 processor. SNOW 3G stream cipher is used in 3rd Generation Partnership Project (3GPP) and the SNOW 2.0 cipher is an ISO/IEC standard (IS 18033-4). For both of these two ciphers, we show that the initialization procedure admits a sliding property, resulting in several sets of related-key pairs. In addition to allowing related-key key recovery attacks against SNOW 2.0 with 256-bit keys, the presented properties reveal non-random behavior of the primitives, yield related-key distinguishers for the two ciphers and question the validity of the security proofs of protocols based on the assumption that these ciphers behave like perfect random functions of the key-IV. Finally, we provide differential fault analysis attacks against two stream ciphers, namely, HC-128 and Rabbit. In this type of attacks, the attacker is assumed to have physical influence over the device that performs the encryption and is able to introduce random faults into the computational process. In case of HC-128, the fault model in which we analyze the cipher is the one in which the attacker is able to fault a random word of the inner state of the cipher but cannot control its exact location nor its new faulted value. Our attack requires about 7968 faults and recovers the complete internal state of HC-128 by solving a set of 32 systems of linear equations over Z2 in 1024 variables. In case of Rabbit stream cipher, the fault model in which the cipher is analyzed is the one in which a random bit of the internal state of the cipher is faulted, however, without control over the location of the injected fault. Our attack requires around 128 − 256 faults, precomputed table of size 2^41.6 bytes and recovers the complete internal state of Rabbit in about 2^38 steps

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

Development of a static feed water electrolysis system

A one person level oxygen generation subsystem was developed and production of the one person oxygen metabolic requirements, 0.82 kg, per day was demonstrated without the need for condenser/separators or electrolyte pumps. During 650 hours of shakedown, design verification, and endurance testing, cell voltages averaged 1.62 V at 206 mA/sq cm and at average operating temperature as low as 326 K, virtually corresponding to the state of the art performance previously established for single cells. This high efficiency and low waste heat generation prevented maintenance of the 339 K design temperature without supplemental heating. Improved water electrolysis cell frames were designed, new injection molds were fabricated, and a series of frames was molded. A modified three fluid pressure controller was developed and a static feed water electrolysis that requires no electrolyte in the static feed compartment was developed and successfully evaluated

Combining dynamical decoupling with fault-tolerant quantum computation

We study how dynamical decoupling (DD) pulse sequences can improve the reliability of quantum computers. We prove upper bounds on the accuracy of DD-protected quantum gates and derive sufficient conditions for DD-protected gates to outperform unprotected gates. Under suitable conditions, fault-tolerant quantum circuits constructed from DD-protected gates can tolerate stronger noise and have a lower overhead cost than fault-tolerant circuits constructed from unprotected gates. Our accuracy estimates depend on the dynamics of the bath that couples to the quantum computer and can be expressed either in terms of the operator norm of the bath’s Hamiltonian or in terms of the power spectrum of bath correlations; we explain in particular how the performance of recursively generated concatenated pulse sequences can be analyzed from either viewpoint. Our results apply to Hamiltonian noise models with limited spatial correlations

Flow estimation and fault diagnosis for automatic control valves in water supply networks

Dynamic adaptability of water supply networks (WSNs) in terms of connectivity and hydraulic conditions is essential for their operation as there are increasing demands on serviceability (leakage, water quality, incident management and fire flow), resilience and cost efficiency. A common approach to achieve multiple control functions throughout networks is to employ automatic control valves (ACVs). Advances in low-powered electronics and micro-actuators enable a wide range of novel control methods in WSNs, including the flow-based pressure control (or flow modulation control (FM)). The implementation of FM schemes has been steadily increasing as it has a major advantage of a closed-loop (feedback) control by utilising measurements to define the flow-pressure control profile. The performance of the FM scheme relies on continuous and accurate flow measurements. Hence, to achieve robust control in WSNs, high-level reliability of the control solution is required. Herein, two methods for the reliable operation of ACVs are investigated, namely (i) Flow estimation and (ii) Fault detection and diagnosis. A novel flow estimation method for diaphragm-actuated globe valves has been developed and experimentally investigated. The method utilises three pressure measurements, namely the valve inlet pressure, the valve outlet pressure and the control chamber pressure (the 3P flow estimation method). The method relies upon the accurate computation of the valve stem position, the measured pressure differential across the valve and the flow coefficients of the valve (Cv, Kv). The developed valve stem position estimation model results in multiple solutions. Advances in signal processing are combined with a machine learning technique (support vector machine) to distinguish the correct solution. The proposed 3P method is compared with a method which uses sensor measurements of the valve stem position (the 2P&Pos method), and its performance validated against measurements from an electromagnetic flowmeter. The uncertainty bounds of the flow estimation methods are also derived. For fault diagnosis, methods for early fault detection and diagnosis (FDD) are investigated. Potential faults are categorised, and residuals and feature variables are defined to detect a fault and diagnose its likely cause. Experimental data have been generated and utilised from controlled laboratory conditions, from an operational network and also from a numerical simulation. The performance of the proposed schemes has been validated.Open Acces

Interregional compensatory mechanisms of motor functioning in progressing preclinical neurodegeneration.

Understanding brain reserve in preclinical stages of neurodegenerative disorders allows determination of which brain regions contribute to normal functioning despite accelerated neuronal loss. Besides the recruitment of additional regions, a reorganisation and shift of relevance between normally engaged regions are a suggested key mechanism. Thus, network analysis methods seem critical for investigation of changes in directed causal interactions between such candidate brain regions. To identify core compensatory regions, fifteen preclinical patients carrying the genetic mutation leading to Huntington's disease and twelve controls underwent fMRI scanning. They accomplished an auditory paced finger sequence tapping task, which challenged cognitive as well as executive aspects of motor functioning by varying speed and complexity of movements. To investigate causal interactions among brain regions a single Dynamic Causal Model (DCM) was constructed and fitted to the data from each subject. The DCM parameters were analysed using statistical methods to assess group differences in connectivity, and the relationship between connectivity patterns and predicted years to clinical onset was assessed in gene carriers. In preclinical patients, we found indications for neural reserve mechanisms predominantly driven by bilateral dorsal premotor cortex, which increasingly activated superior parietal cortices the closer individuals were to estimated clinical onset. This compensatory mechanism was restricted to complex movements characterised by high cognitive demand. Additionally, we identified task-induced connectivity changes in both groups of subjects towards pre- and caudal supplementary motor areas, which were linked to either faster or more complex task conditions. Interestingly, coupling of dorsal premotor cortex and supplementary motor area was more negative in controls compared to gene mutation carriers. Furthermore, changes in the connectivity pattern of gene carriers allowed prediction of the years to estimated disease onset in individuals. Our study characterises the connectivity pattern of core cortical regions maintaining motor function in relation to varying task demand. We identified connections of bilateral dorsal premotor cortex as critical for compensation as well as task-dependent recruitment of pre- and caudal supplementary motor area. The latter finding nicely mirrors a previously published general linear model-based analysis of the same data. Such knowledge about disease specific inter-regional effective connectivity may help identify foci for interventions based on transcranial magnetic stimulation designed to stimulate functioning and also to predict their impact on other regions in motor-associated networks

A review of convex approaches for control, observation and safety of linear parameter varying and Takagi-Sugeno systems

This paper provides a review about the concept of convex systems based on Takagi-Sugeno, linear parameter varying (LPV) and quasi-LPV modeling. These paradigms are capable of hiding the nonlinearities by means of an equivalent description which uses a set of linear models interpolated by appropriately defined weighing functions. Convex systems have become very popular since they allow applying extended linear techniques based on linear matrix inequalities (LMIs) to complex nonlinear systems. This survey aims at providing the reader with a significant overview of the existing LMI-based techniques for convex systems in the fields of control, observation and safety. Firstly, a detailed review of stability, feedback, tracking and model predictive control (MPC) convex controllers is considered. Secondly, the problem of state estimation is addressed through the design of proportional, proportional-integral, unknown input and descriptor observers. Finally, safety of convex systems is discussed by describing popular techniques for fault diagnosis and fault tolerant control (FTC).Peer ReviewedPostprint (published version