MV3: A new word based stream cipher using rapid mixing and revolving buffers

MV3 is a new word based stream cipher for encrypting long streams of data. A direct adaptation of a byte based cipher such as RC4 into a 32- or 64-bit word version will obviously need vast amounts of memory. This scaling issue necessitates a look for new components and principles, as well as mathematical analysis to justify their use. Our approach, like RC4's, is based on rapidly mixing random walks on directed graphs (that is, walks which reach a random state quickly, from any starting point). We begin with some well understood walks, and then introduce nonlinearity in their steps in order to improve security and show long term statistical correlations are negligible. To minimize the short term correlations, as well as to deter attacks using equations involving successive outputs, we provide a method for sequencing the outputs derived from the walk using three revolving buffers. The cipher is fast -- it runs at a speed of less than 5 cycles per byte on a Pentium IV processor. A word based cipher needs to output more bits per step, which exposes more correlations for attacks. Moreover we seek simplicity of construction and transparent analysis. To meet these requirements, we use a larger state and claim security corresponding to only a fraction of it. Our design is for an adequately secure word-based cipher; our very preliminary estimate puts the security close to exhaustive search for keys of size < 256 bits.Comment: 27 pages, shortened version will appear in "Topics in Cryptology - CT-RSA 2007

Algorithm 959: VBF: A Library of C plus plus Classes for Vector Boolean Functions in Cryptography

VBF is a collection of C++ classes designed for analyzing vector Boolean functions (functions that map a Boolean vector to another Boolean vector) from a cryptographic perspective. This implementation uses the NTL library from Victor Shoup, adding new modules that call NTL functions and complement the existing ones, making it better suited to cryptography. The class representing a vector Boolean function can be initialized by several alternative types of data structures such as Truth Table, Trace Representation, and Algebraic Normal Form (ANF), among others. The most relevant cryptographic criteria for both block and stream ciphers as well as for hash functions can be evaluated with VBF: it obtains the nonlinearity, linearity distance, algebraic degree, linear structures, and frequency distribution of the absolute values of the Walsh Spectrum or the Autocorrelation Spectrum, among others. In addition, operations such as equality testing, composition, inversion, sum, direct sum, bricklayering (parallel application of vector Boolean functions as employed in Rijndael cipher), and adding coordinate functions of two vector Boolean functions are presented. Finally, three real applications of the library are described: the first one analyzes the KASUMI block cipher, the second one analyzes the Mini-AES cipher, and the third one finds Boolean functions with very high nonlinearity, a key property for robustness against linear attacks

Driven Dynamics of Periodic Elastic Media in Disorder

We analyze the large-scale dynamics of vortex lattices and charge density waves driven in a disordered potential. Using a perturbative coarse-graining procedure we present an explicit derivation of non-equilibrium terms in the renormalized equation of motion, in particular Kardar-Parisi-Zhang non-linearities and dynamic strain terms. We demonstrate the absence of glassy features like diverging linear friction coefficients and transverse critical currents in the drifting state. We discuss the structure of the dynamical phase diagram containing different elastic phases very small and very large drive and plastic phases at intermediate velocity.Comment: 21 pages Latex with 4 figure

A method of construction of balanced functions with optimum algebraic immunity

Because of the recent algebraic attacks, a high algebraic immunity is now an absolutely necessary (but not sufficient) property for Boolean functions used in stream ciphers. A difference of only 1 between the algebraic immunities of two functions can make a crucial difference with respect to algebraic attacks. Very few examples of (balanced) functions with high algebraic immunity have been found so far. These examples seem to be isolated and no method for obtaining such functions is known. In this paper, we introduce a general method for proving that a given function, in any number of variables, has a prescribed algebraic immunity. We deduce an algorithm for generating balanced functions in any odd number of variables, with optimum algebraic immunity. We also give an algorithm, valid for any even number of variables, for constructing (possibly) balanced functions with optimum (or, if this can be useful, with high but not optimal) algebraic immunity. We also give a new example of an infinite class of such functions. We study their Walsh transforms. To this aim, we completely characterize the Walsh transform of the majority function

Swarm Behavior to Mitigate Rebound in Air Conditioning Demand Response Events

Thermostatically Controlled Loads (TCLs) have shown great potential for Demand Response (DR) events. However, it has been commonly seen that DR events using TCLs may cause demand rebound, especially in homogeneous populations. To further explore the potential for DR events, as well as the negative effects, a stability and resilience analysis were performed on multiple populations and verified with agent based modeling simulations. At the core of this study is an added thermostat criterion created from the combination of a proportional gain and the average compressor operating state of neighboring TCLs. Where DR events in TCLs are commonly controlled by set point manipulation, the modified thermostat behavior proposed in this study alters the effective dead band of each individual TCL. Previous work has shown the effectiveness of the proposed behavior to mitigating the demand rebound. By adding the average operating state of neighboring TCLs and a proportional gain, the systems feedback is changed, opening the possibilities to creating an unstable response. Stability limit are found from linearized systems, differing in delay schemes and connection architecture. The stability analysis was verified through agent-based modeling simulations on MATLAB. The linearization assumption was tested by simulating the systems while altering the parameters of population size and thermostat dead band. Resilience of several systems, differing in connection architecture, is computed and compared to results of a simulated denial of service attack on the system. Resilience for each architecture was calculated using the algebraic connectivity of the graph. The simulated attack is completed by removing the TCLs ability to communicate with in the agent based model. The stability analysis showed the effect of the gain value on the performance of the system and that the stability limit was directly affected by the effective deadband. As the deadband size was increased, the predicted results found from the analysis aligned with simulations of the system. Contrarily the resilience analysis was validated by simulations with smaller deadband sizes. Simulations of cyber-attacks also showed optimal attacks based on operating state of thermostats, as well as locations within the population

PremiUm-CNN: Propagating Uncertainty Towards Robust Convolutional Neural Networks

Deep neural networks (DNNs) have surpassed human-level accuracy in various learning tasks. However, unlike humans who have a natural cognitive intuition for probabilities, DNNs cannot express their uncertainty in the output decisions. This limits the deployment of DNNs in mission-critical domains, such as warfighter decision-making or medical diagnosis. Bayesian inference provides a principled approach to reason about model\u27s uncertainty by estimating the posterior distribution of the unknown parameters. The challenge in DNNs remains the multi-layer stages of non-linearities, which make the propagation of high-dimensional distributions mathematically intractable. This paper establishes the theoretical and algorithmic foundations of uncertainty or belief propagation by developing new deep learning models named PremiUm-CNNs (Propagating Uncertainty in Convolutional Neural Networks). We introduce a tensor normal distribution as a prior over convolutional kernels and estimate the variational posterior by maximizing the evidence lower bound (ELBO). We start by deriving the first-order mean-covariance propagation framework. Later, we develop a framework based on the unscented transformation (correct at least up to the second-order) that propagates sigma points of the variational distribution through layers of a CNN. The propagated covariance of the predictive distribution captures uncertainty in the output decision. Comprehensive experiments conducted on diverse benchmark datasets demonstrate: 1) superior robustness against noise and adversarial attacks, 2) self-assessment through predictive uncertainty that increases quickly with increasing levels of noise or attacks, and 3) an ability to detect a targeted attack from ambient noise

International Conference on Computer Science

UBT Annual International Conference is the 11th international interdisciplinary peer reviewed conference which publishes works of the scientists as well as practitioners in the area where UBT is active in Education, Research and Development. The UBT aims to implement an integrated strategy to establish itself as an internationally competitive, research-intensive university, committed to the transfer of knowledge and the provision of a world-class education to the most talented students from all background. The main perspective of the conference is to connect the scientists and practitioners from different disciplines in the same place and make them be aware of the recent advancements in different research fields, and provide them with a unique forum to share their experiences. It is also the place to support the new academic staff for doing research and publish their work in international standard level. This conference consists of sub conferences in different fields like: Art and Digital Media Agriculture, Food Science and Technology Architecture and Spatial Planning Civil Engineering, Infrastructure and Environment Computer Science and Communication Engineering Dental Sciences Education and Development Energy Efficiency Engineering Integrated Design Information Systems and Security Journalism, Media and Communication Law Language and Culture Management, Business and Economics Modern Music, Digital Production and Management Medicine and Nursing Mechatronics, System Engineering and Robotics Pharmaceutical and Natural Sciences Political Science Psychology Sport, Health and Society Security Studies This conference is the major scientific event of the UBT. It is organizing annually and always in cooperation with the partner universities from the region and Europe. We have to thank all Authors, partners, sponsors and also the conference organizing team making this event a real international scientific event. Edmond Hajrizi, President of UBT UBT – Higher Education Institutio

False data injection attack detection in smart grid

Smart grid is a distributed and autonomous energy delivery infrastructure that constantly monitors the operational state of its overall network using smart techniques and state estimation. State estimation is a powerful technique that is used to determine the overall operational state of the system based on a limited set of measurements collected through metering systems. Cyber-attacks pose serious risks to a smart grid state estimation that can cause disruptions and power outages resulting in huge economical losses and are therefore a big concern to a reliable national grid operation. False data injection attacks (FDIAs), engineered on the basis of the knowledge of the network configuration, are difficult to detect using the traditional data detection mechanisms. These detection schemes have been found vulnerable and failed to detect these FDIAs. FDIAs specifically target the state data and can manipulate the state measurements in such a way that these false measurements appear real to the main control systems. This research work explores the possibility of FDIA detection using state estimation in a distributed and partitioned smart grid. In order to detect FDIAs we use measurements for residual-based testing which creates an objective function; and the probability of erroneous data is determined from this residual test. In this test, a preset threshold is determined based on the prior history of the state data. FDIA cases are simulated within a smart grid considering that the Chi-square detection state estimator fails in identifying such attacks. We compute the objective function using the standard weighted least problem and then test the objective function against the value in the Chi-square table. The gain matrix and the Jacobian matrix are computed. The state variables are computed in the form of a voltage magnitude. The state variables are computed after the inception of an attack to assess these state magnitude results. Different sizes of partitioning are used to improve the overall sensitivity of the Chi-square results. Our additional estimator is based on a Kalman estimation that consists of the state prediction and state correction steps. In the first step, it obtains the state and matrix covariance prediction, and in the second step, it calculates the Kalman gain and the state and matrix covariance update steps. The set of points is created for the state vector x at a time instant t. The initial vector and covariance matrix are based on a priori knowledge of the historical estimates. A set of sigma points is estimated by the state update function. Sigma points refer to the minimal set of sampling points that are selected and transformed using nonlinear function, and the new mean and the covariance are formed out of these transformed points. The idea behind this is that it is easier to compute a Gaussian distribution than an arbitrary nonlinear function. The filter gain, the mean and the covariance are used to estimate the next state. Our simulation results show that the combination of Kalman estimation and distributed state estimation improves the overall stability index and vulnerability assessment score of the smart grid. We built a stability index table for a smart grid based on the state estimates value after the inception of an FDIA. The vulnerability assessment score of the smart grid is based on common vulnerability scoring system (CVSS) and state estimates under the influence of an FDIA. The simulations are conducted in the MATPOWER program and different electrical bus systems such as IEEE 14, 30, 39, 118 and 300 are tested. All the contributions have been published in reputable journals and conferences.Doctor of Philosoph

Missile Longitudinal Dynamics Control Design Using Pole Placement and LQR Methods

In high-maneuvering missile systems, with severe restrictions on actuator energy requirements, it is desirable to achieve the required performance with least actuation effort. Linear Quadratic Regulator (LQR) has been in literature for long and has proven it’s mettle as an optimal controller in many benign aerospace applications and industrial applications where the response times of the plant, in most cases, are seen to be greater than 10 seconds. It can be observed in the literature that LQR control methodology has not been explored enough in the tactical missile applications where requirement of very fast airframe response times are desired, typically of the order of milliseconds. In the present research, the applicability of LQR method for one such agile missile control has been critically explored. In the present research work, longitudinal dynamic model of an agile missile flying at high angle of attack regime has been established and an optimal LQR control solution has been proposed to bring out the required performance demanding least control actuator energy. A novel scheme has been presented to further optimise the control effort, which is essential in this class of missile systems with space and energy constraints, by iteratively computing optimal magnitude state weighing matrix Q and control cost matrix R. Pole placement design techniques, though extensively used in aerospace industry because of ease of implementation and proven results, do not address optimality of the system performance. Hence, a comparative study has been carried out to verify the results of LQR against pole placement technique based controller. The efficacy of LQR based controller over pole placement design techniques is successfully established with minimum control energy requirement in this paper. Futuristic high maneuvering, agile missile control design with severe space and energy constraints stand to benefit incorporating the controller design scheme proposed in this paper.&nbsp

Vortex: A New Family of One Way Hash Functions based on Rijndael Rounds and Carry-less Multiplication

We present Vortex a new family of one way hash functions that can produce message digests of 224, 256, 384 and 512 bits. The main idea behind the design of these hash functions is that we use well known algorithms that can support very fast diffusion in a small number of steps. We also balance the cryptographic strength that comes from iterating block cipher rounds with SBox substitution and diffusion (like Whirlpool) against the need to have a lightweight implementation with as small number of rounds as possible. We use a variable number of Rijndael rounds with a stronger key schedule. Our goal is not to protect a secret symmetric key but to support perfect mixing of the bits of the input into the hash value. Rijndael rounds are followed by our variant of Galois Field multiplication. This achieves cross-mixing between 128-bit or 256-bit sets. Our hash function uses the Enveloped Merkle-Damgard construction to support properties such as collision resistance, first and second pre-image resistance, pseudorandom oracle preservation and pseudorandom function preservation. We provide analytical results that demonstrate that the number of queries required for finding a collision with probability greater or equal to 0.5 in an ideal block cipher approximation of Vortex 256 is at least 1.18x2^122.55 if the attacker uses randomly selected message words. We also provide experimental results that indicate that the compression function of Vortex is not inferior to that of the SHA family regarding its capability to preserve the pseudorandom oracle property. We list a number of well known attacks and discuss how the Vortex design addresses them. The main strength of the Vortex design is that this hash function can demonstrate an expected performance of 2.2-2.5 cycles per byte in future processors with instruction set support for Rijndael rounds and carry-less multiplication. We provide arguments why we believe this is a trend in the industry. We also discuss how optimized assembly code can be written that demonstrates such performance