Algebraic attacks on clock-controlled stream ciphers

Stream ciphers are encryption algorithms used for ensuring the privacy of digital telecommunications. They have been widely used for encrypting military communications, satellite communications, pay TV encryption and for voice encryption of both fixed lined and wireless networks. The current multi year European project eSTREAM, which aims to select stream ciphers suitable for widespread adoptation, reflects the importance of this area of research. Stream ciphers consist of a keystream generator and an output function. Keystream generators produce a sequence that appears to be random, which is combined with the plaintext message using the output function. Most commonly, the output function is binary addition modulo two. Cryptanalysis of these ciphers focuses largely on analysis of the keystream generators and of relationships between the generator and the keystream it produces. Linear feedback shift registers are widely used components in building keystream generators, as the sequences they produce are well understood. Many types of attack have been proposed for breaking various LFSR based stream ciphers. A recent attack type is known as an algebraic attack. Algebraic attacks transform the problem of recovering the key into a problem of solving multivariate system of equations, which eventually recover the internal state bits or the key bits. This type of attack has been shown to be effective on a number of regularly clocked LFSR based stream ciphers. In this thesis, algebraic attacks are extended to a number of well known stream ciphers where at least one LFSR in the system is irregularly clocked. Applying algebriac attacks to these ciphers has only been discussed previously in the open literature for LILI-128. In this thesis, algebraic attacks are first applied to keystream generators using stop-and go clocking. Four ciphers belonging to this group are investigated: the Beth-Piper stop-and-go generator, the alternating step generator, the Gollmann cascade generator and the eSTREAM candidate: the Pomaranch cipher. It is shown that algebraic attacks are very effective on the first three of these ciphers. Although no effective algebraic attack was found for Pomaranch, the algebraic analysis lead to some interesting findings including weaknesses that may be exploited in future attacks. Algebraic attacks are then applied to keystream generators using (p; q) clocking. Two well known examples of such ciphers, the step1/step2 generator and the self decimated generator are investigated. Algebraic attacks are shown to be very powerful attack in recovering the internal state of these generators. A more complex clocking mechanism than either stop-and-go or the (p; q) clocking keystream generators is known as mutual clock control. In mutual clock control generators, the LFSRs control the clocking of each other. Four well known stream ciphers belonging to this group are investigated with respect to algebraic attacks: the Bilateral-stop-and-go generator, A5/1 stream cipher, Alpha 1 stream cipher, and the more recent eSTREAM proposal, the MICKEY stream ciphers. Some theoretical results with regards to the complexity of algebraic attacks on these ciphers are presented. The algebraic analysis of these ciphers showed that generally, it is hard to generate the system of equations required for an algebraic attack on these ciphers. As the algebraic attack could not be applied directly on these ciphers, a different approach was used, namely guessing some bits of the internal state, in order to reduce the degree of the equations. Finally, an algebraic attack on Alpha 1 that requires only 128 bits of keystream to recover the 128 internal state bits is presented. An essential process associated with stream cipher proposals is key initialization. Many recently proposed stream ciphers use an algorithm to initialize the large internal state with a smaller key and possibly publicly known initialization vectors. The effect of key initialization on the performance of algebraic attacks is also investigated in this thesis. The relationships between the two have not been investigated before in the open literature. The investigation is conducted on Trivium and Grain-128, two eSTREAM ciphers. It is shown that the key initialization process has an effect on the success of algebraic attacks, unlike other conventional attacks. In particular, the key initialization process allows an attacker to firstly generate a small number of equations of low degree and then perform an algebraic attack using multiple keystreams. The effect of the number of iterations performed during key initialization is investigated. It is shown that both the number of iterations and the maximum number of initialization vectors to be used with one key should be carefully chosen. Some experimental results on Trivium and Grain-128 are then presented. Finally, the security with respect to algebraic attacks of the well known LILI family of stream ciphers, including the unbroken LILI-II, is investigated. These are irregularly clock- controlled nonlinear filtered generators. While the structure is defined for the LILI family, a particular paramater choice defines a specific instance. Two well known such instances are LILI-128 and LILI-II. The security of these and other instances is investigated to identify which instances are vulnerable to algebraic attacks. The feasibility of recovering the key bits using algebraic attacks is then investigated for both LILI- 128 and LILI-II. Algebraic attacks which recover the internal state with less effort than exhaustive key search are possible for LILI-128 but not for LILI-II. Given the internal state at some point in time, the feasibility of recovering the key bits is also investigated, showing that the parameters used in the key initialization process, if poorly chosen, can lead to a key recovery using algebraic attacks.

Important omitted spatial variables in safety models: Understanding contributing crash causes at intersections

Advances in safety research—trying to improve the collective understanding of motor vehicle crash causation—rests upon the pursuit of numerous lines of inquiry. The research community has focused on analytical methods development (negative binomial specifications, simultaneous equations, etc.), on better experimental designs (before-after studies, comparison sites, etc.), on improving exposure measures, and on model specification improvements (additive terms, non-linear relations, etc.). One might think of different lines of inquiry in terms of ‘low lying fruit’—areas of inquiry that might provide significant improvements in understanding crash causation. It is the contention of this research that omitted variable bias caused by the exclusion of important variables is an important line of inquiry in safety research. In particular, spatially related variables are often difficult to collect and omitted from crash models—but offer significant ability to better understand contributing factors to crashes. This study—believed to represent a unique contribution to the safety literature—develops and examines the role of a sizeable set of spatial variables in intersection crash occurrence. In addition to commonly considered traffic and geometric variables, examined spatial factors include local influences of weather, sun glare, proximity to drinking establishments, and proximity to schools. The results indicate that inclusion of these factors results in significant improvement in model explanatory power, and the results also generally agree with expectation. The research illuminates the importance of spatial variables in safety research and also the negative consequences of their omissions.

Renewable energy sources and frequency regulation : survey and new perspectives

As the use of renewable energy sources (RESs) increases worldwide, there is a rising interest on their impacts on power system operation and control. An overview of the key issues and new challenges on frequency regulation concerning the integration of renewable energy units into the power systems is presented. Following a brief survey on the existing challenges and recent developments, the impact of power fluctuation produced by variable renewable sources (such as wind and solar units) on sysstem frequency performance is also presented. An updated LFC model is introduced, and power system frequency response in the presence of RESs and associated issues is analysed. The need for the revising of frequency performance standards is emphasised. Finally, non-linear time-domain simulations on the standard 39-bus and 24-bus test systems show that the simulated results agree with those predicted analytically.

Regulation of train service by coasting control in metro railway system

To maximise the capacity of the rail lineand provide a reliable service for pas-sengers throughout the day, regulation of train service to maintain steady service headway is es-sential. In most current metro systems, train usually starts coasting at a fixed distance from the departed station to achieve service regulation. However, this approach is only effective with re-spect to a nominal operational condition of train schedule but not necessarily the current service demand. Moreover, it is not simply to identify the necessary starting point for coasting under the run time constraints of current service conditions since train movement is attributed by a large number of factors, most of which are non-linear and inter-dependent. This paper presents an ap-plication of classical measures to search for the appropriate coasting point to meet a specified inter-station run time and they can be integrated in the on-board Automatic Train Operation (ATO) system and have the potential for on-line implementation in making a set of coasting command decisions.

A two-Dimensional finite volume method for variable density flow and solute transport through saturated-unsaturated media

Extensive groundwater withdrawal has resulted in a severe seawater intrusion problem in the Gooburrum aquifers at Bundaberg, Queensland, Australia. Better management strategies can be implemented by understanding the seawater intrusion processes in those aquifers. To study the seawater intrusion process in the region, a two-dimensional density-dependent, saturated and unsaturated flow and transport computational model is used. The model consists of a coupled system of two non-linear partial differential equations. The first equation describes the flow of a variable-density fluid, and the second equation describes the transport of dissolved salt. A two-dimensional control volume finite element model is developed for simulating the seawater intrusion into the heterogeneous aquifer system at Gooburrum. The simulation results provide a realistic mechanism by which to study the convoluted transport phenomena evolving in this complex heterogeneous coastal aquifer.

Coasting control of train operation by genetic algorithm

Balancing between the provision of high quality of service and running within a tight budget is one of the biggest challenges for most metro railway operators around the world. Conventionally, one possible approach for the operator to adjust the time schedule is to alter the stop time at stations, if other system constraints, such as traction equipment characteristic, are not taken into account. Yet it is not an effective, flexible and economical method because the run-time of a train simply cannot be extended without limitation, and a balance between run-time and energy consumption has to be maintained. Modification or installation of a new signalling system not only increases the capital cost, but also affects the normal train service. Therefore, in order to procure a more effective, flexible and economical means to improve the quality of service, optimisation of train performance by coasting point identification has become more attractive and popular. However, identifying the necessary starting points for coasting under the constraints of current service conditions is no simple task because train movement is attributed by a large number of factors, most of which are non-linear and inter-dependent. This paper presents an application of genetic algorithms (GA) to search for the appropriate coasting points and investigates the possible improvement on computation time and fitness of genes.

Linearity within the SMS4 Block Cipher

We present several new observations on the SMS4 block cipher, and discuss their cryptographic significance. The crucial observation is the existence of fixed points and also of simple linear relationships between the bits of the input and output words for each component of the round functions for some input words. This implies that the non-linear function T of SMS4 does not appear random and that the linear transformation provides poor diffusion. Furthermore, the branch number of the linear transformation in the key scheduling algorithm is shown to be less than optimal. The main security implication of these observations is that the round function is not always non-linear. Due to this linearity, it is possible to reduce the number of effective rounds of SMS4 by four. We also investigate the susceptibility of SMS4 to further cryptanalysis. Finally, we demonstrate a successful differential attack on a slightly modified variant of SMS4. These findings raise serious questions on the security provided by SMS4.

Use of ITS to improve level crossings safety : open issues

Level crossing crashes have been shown to result in enormous human and financial cost to society. According to the Australian Transport Safety Bureau (ATSB) [5] a total of 632 Railway Level crossing (RLX) collisions, between trains and road vehicles, occurred in Australia between 2001 and June 2009. The cost of RLX collisions runs into the tens of millions of dollars each year in Australia [6]. In addition, loss of life and injury are commonplace in instances where collisions occur. Based on estimates that 40% of rail related fatalities occur at level crossings [12], it is estimated that 142 deaths between 2001 and June 2009 occurred at RLX. The aim of this paper is to (i) summarise crash patterns in Australia, (ii) review existing international ITS interventions to improve level crossing and (iii) highlights open human factors research related issues. Human factors (e.g., driver error, lapses or violations) have been evidenced as a significant contributing factor in RLX collisions, with drivers of road vehicles particularly responsible for many collisions. Unintentional errors have been found to contribute to 46% of RLX collisions [6] and appear to be far more commonplace than deliberate violations. Humans have been found to be inherently inadequate at using the sensory information available to them to facilitate safe decision-making at RLX and tend to underestimate the speed of approaching large objects due to the non-linear increases in perceived size [6]. Collisions resulting from misjudgements of train approach speed and distance are common [20]. Thus, a fundamental goal for improved RLX safety is the provision of sufficient contextual information to road vehicle drivers to facilitate safe decision-making regarding crossing behaviours.

Analysis of cardiac and epileptic signals using higher order spectra

The theory of nonlinear dyamic systems provides some new methods to handle complex systems. Chaos theory offers new concepts, algorithms and methods for processing, enhancing and analyzing the measured signals. In recent years, researchers are applying the concepts from this theory to bio-signal analysis. In this work, the complex dynamics of the bio-signals such as electrocardiogram (ECG) and electroencephalogram (EEG) are analyzed using the tools of nonlinear systems theory. In the modern industrialized countries every year several hundred thousands of people die due to sudden cardiac death. The Electrocardiogram (ECG) is an important biosignal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computerbased intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are non-linear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of non-linear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and four classes of arrhythmia. This thesis presents some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. Several features were extracted from the HOS and subjected an Analysis of Variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, seven features were extracted from the heart rate signals using HOS and fed to a support vector machine (SVM) for classification. The performance evaluation protocol in this thesis uses 330 subjects consisting of five different kinds of cardiac disease conditions. The classifier achieved a sensitivity of 90% and a specificity of 89%. This system is ready to run on larger data sets. In EEG analysis, the search for hidden information for identification of seizures has a long history. Epilepsy is a pathological condition characterized by spontaneous and unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic early detection of the seizure onsets would help the patients and observers to take appropriate precautions. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, these features are used to train both a Gaussian mixture model (GMM) classifier and a Support Vector Machine (SVM) classifier. Results show that the classifiers were able to achieve 93.11% and 92.67% classification accuracy, respectively, with selected HOS based features. About 2 hours of EEG recordings from 10 patients were used in this study. This thesis introduces unique bispectrum and bicoherence plots for various cardiac conditions and for normal, background and epileptic EEG signals. These plots reveal distinct patterns. The patterns are useful for visual interpretation by those without a deep understanding of spectral analysis such as medical practitioners. It includes original contributions in extracting features from HRV and EEG signals using HOS and entropy, in analyzing the statistical properties of such features on real data and in automated classification using these features with GMM and SVM classifiers.

Shaping and re-shaping social capital in buyer-supplier relationships

Social capital plays an important role in explaining how value is created from firms' network relationships, but little is understood about how social capital is shaped over time and how it is re-shaped when firms consolidate their network ties. In response, this study explores the evolution of social capital in buyer–supplier relationships through a case study of a company undertaking radical product innovation, and examines the corresponding changes in the firm's network of buyer–supplier relationships. The analysis shows that social capital is built in a decidedly non-linear and non-uniform manner. The study also reveals considerable interaction among the dimensions of social capital throughout the evolution of the firm's network, and emphasizes the importance of the cognitive dimension—a feature receiving little attention thus far. The evidence shows, too, that efforts to strengthen social capital need to increase when network ties are sacrificed to prevent unintended consequences for firms' longer-term value creation.

Defending the patch : a labour geography analysis of unions’ construction of and responses to the problem of declining union membership amongst youth

If the trade union movement is to remain an influential force in the industrial, economic and socio/political arenas of industrialised nations it is vital that its recruitment of young members improve dramatically. Australian union membership levels have declined markedly over the last three decades and youth union membership levels have decreased more than any age group. Currently around 10% of young workers aged between 16-24 years are members of unions in Australia compared to 26% of workers aged 45-58 (Oliver, 2008). This decline has occurred throughout the union movement, in all states and in almost all industries and occupations. This research, which consists of interviews with union organisers and union officials, draws on perspectives from the labour geography literature to explore how union personnel located in various places, spaces and scales construct the issue of declining youth union membership. It explores the scale of connections within the labour movement and the extent to which these connections are leveraged to address the problem of youth union membership decline. To offer the reader a sense of context and perspective, the thesis firstly outlines the historical development of the union movement. It also reviews the literature on youth membership decline. Labour geography offers a rich and apposite analytical tool for investigation of this area. The notion of ‘scale’ as a dynamic, interactive, constructed and reconstructed entity (Ellem, 2006) is an appropriate lens for viewing youth-union membership issues. In this non-linear view, scale is a relational element which interplays with space, place and the environment (Howett, in Marston, 2000) rather than being ‘sequential’ and hierarchical. Importantly, the thesis investigates the notion of unions as ‘spaces of dependence’ (Cox, 1998a, p.2), organisations whose space is centred upon realising essential interests. It also considers the quality of unions’ interactions with others – their ‘spaces of engagement‘(Cox, 1998a, p.2), and the impact that this has upon their ability to recruit youth. The findings reveal that most respondents across the spectrum of the union movement attribute the decline in youth membership levels to factors external to the movement itself, such as changes to industrial relations legislation and the impact of globalisation on employment markets. However, participants also attribute responsibility for declining membership levels to the union movement itself, citing factors such as a lack of resourcing and a need to change unions’ perceived identity and methods of operation. The research further determined that networks of connections across the union movement are tenuous and, to date, are not being fully utilised to assist unions to overcome the youth recruitment dilemma. The study concludes that potential connections between unions are hampered by poor resourcing, workload issues and some deeply entrenched attitudes related to unions ‘defending (and maintaining) their patch’.

Crossing boundaries to explore sustainable architecture by open system design

This paper argues a model of open system design for sustainable architecture, based on a thermodynamics framework of entropy as an evolutionary paradigm. The framework can be simplified to stating that an open system evolves in a non-linear pattern from a far-from-equilibrium state towards a non-equilibrium state of entropy balance, which is a highly ordered organization of the system when order comes out of chaos. This paper is work in progress on a PhD research project which aims to propose building information modelling for optimization and adaptation of buildings environmental performance as an alternative sustainable design program in architecture. It will be used for efficient distribution and consumption of energy and material resource in life-cycle buildings, with the active involvement of the end-users and the physical constraints of the natural environment.

A statistical framework for natural feature representation

This paper presents a robust stochastic framework for the incorporation of visual observations into conventional estimation, data fusion, navigation and control algorithms. The representation combines Isomap, a non-linear dimensionality reduction algorithm, with expectation maximization, a statistical learning scheme. The joint probability distribution of this representation is computed offline based on existing training data. The training phase of the algorithm results in a nonlinear and non-Gaussian likelihood model of natural features conditioned on the underlying visual states. This generative model can be used online to instantiate likelihoods corresponding to observed visual features in real-time. The instantiated likelihoods are expressed as a Gaussian mixture model and are conveniently integrated within existing non-linear filtering algorithms. Example applications based on real visual data from heterogenous, unstructured environments demonstrate the versatility of the generative models.

A stochastic model for natural feature representation

This paper presents a robust stochastic model for the incorporation of natural features within data fusion algorithms. The representation combines Isomap, a non-linear manifold learning algorithm, with Expectation Maximization, a statistical learning scheme. The representation is computed offline and results in a non-linear, non-Gaussian likelihood model relating visual observations such as color and texture to the underlying visual states. The likelihood model can be used online to instantiate likelihoods corresponding to observed visual features in real-time. The likelihoods are expressed as a Gaussian Mixture Model so as to permit convenient integration within existing nonlinear filtering algorithms. The resulting compactness of the representation is especially suitable to decentralized sensor networks. Real visual data consisting of natural imagery acquired from an Unmanned Aerial Vehicle is used to demonstrate the versatility of the feature representation.

Learning articulated motion structures with Bayesian Networks

This paper presents a general methodology for learning articulated motions that, despite having non-linear correlations, are cyclical and have a defined pattern of behavior Using conventional algorithms to extract features from images, a Bayesian classifier is applied to cluster and classify features of the moving object. Clusters are then associated in different frames and structure learning algorithms for Bayesian networks are used to recover the structure of the motion. This framework is applied to the human gait analysis and tracking but applications include any coordinated movement such as multi-robots behavior analysis.