A Probabilistic Framework for Forecasting Cryptographic Security Under Quantum and Classical Threats

Network virus propagation and security situation awareness based on Hidden Markov Model

High-order Hidden Markov Model for trend prediction in financial time series

SummaryThe rapidly growing cloud computing paradigm provides a cost‐effective platform for storing, sharing, and delivering data and computation through internet connectivity. However, one of the biggest barriers for massive cloud adoption is the growing cybersecurity threats/risks that influence its confidence and feasibility. Existing threat models for clouds may not be able to capture complex attacks. For example, an attacker may combine multiple security vulnerabilities into an intelligent, persistent, and sequence of attack behaviors that will continuously act to compromise the target on clouds. Hence, new models for detection of complex and diversified network attacks are needed. In this article, we introduce an effective threat modeling approach that has the ability to predict and detect the probability of occurrence of various security threats and attacks within the cloud environment using hidden Markov models (HMMs). The HMM is a powerful statistical analysis technique and is used to create a probability matrix based on the sensitivity of the data and possible system components that can be attacked. In addition, the HMM is used to provide supplemental information to discover a trend attack pattern from the implicit (or hidden) raw data. The proposed model is trained to identify anomalous sequences or threats so that accurate and up‐to‐date information on risk exposure of cloud‐hosted services are properly detected. The proposed model would act as an underlying framework and a guiding tool for cloud systems security experts and administrators to secure processes and services over the cloud. The performance evaluation shows the effectiveness of the proposed approach to find attack probability and the number of correctly detected attacks in the presence of multiple attack scenarios.

In the state-of-the-art literature on cryptography and control theory, there has been no systematic methodology of constructing cyber-physical systems that can achieve desired control performance while being protected against eavesdropping attacks. In this paper, we tackle this challenging problem. We first propose two novel notions referred to as sample identifying complexity and sample deciphering time in an encrypted-control framework. The former explicitly captures the relation between the dynamical characteristics of control systems and the level of identifiability of the systems while the latter shows the relation between the computation time for the identification and the key length of a cryptosystem. Based on these two tractable new notions, we propose a systematic method for designing the both of an optimal key length to prevent system identification with a given precision within a given life span of systems, and of an optimal controller to maximize both of the control performance and the difficulty of the identification. The efficiency of the proposed method in terms of security level and realtime-ness is investigated through numerical simulations. To the best of our knowledge, this paper first connect the relationship between the security of cryptography and dynamical systems from a control-theoretic perspective.

In view of all kinds of network attacks which may affect the network security of the smart grid system, an attack detection algorithm is proposed, feature extraction and training to the data such as server logs is carried out based on Hidden Markov model (HMM) to achieve the network attack detection in smart grid system. By the proposed algorithm, analysis on real data of the smart grid system in one power company is carried out, which can effectively identify common types and numbers of network attack types. Through the proposed smart grid network attack detection algorithm, network security risks in the smart grid system can be found, and the level of network security is improved.

Cryptography is one of the most important approaches to keep digital communication in lock and key and therefore guarantees the privacy, integrity and authenticity of the data by means of complex coding. Cryptographic techniques have arisen centuries ago and the old techniques have continued to evolve as today’s challenges are cloud computing, data storage and retrieval as well as authenticated users. With the widespread spread of cloud environments, trust-enabling systems with cutting-edge technologies such as Zero Knowledge Proofs and blockchain emerged to increase privacy and security. By examining the notion of trust localization, this study draws attention to a use of Gateways as access control points to sensitive information reducing the dependence on the central cloud infrastructures. Additionally, source authentication and authorization provided by Acaras and PKI is studied regarding their integration and effectiveness. The research examines the current challenges in cryptographic security and current findings of cryptographic security and investigates what the future of cryptographic security to build trust and security in modern digital systems..

<p dir="ltr"><span>PINNs (Physics-Informed Neural Networks) are neural networks designed to solve Partial Differential Equations (PDEs) by integrating physical knowledge into the learning framework. Constructing a PINN involves defining a neural network to approximate the PDE solution, with the total loss calculated as a combination of the losses associated with the PDE, boundary conditions, initial conditions, and measured data. This concept is employed in practical applications to solve various PDEs, such as the Black-Scholes and Heston equations, which are fundamental in financial option pricing. This approach enables the modelling and pricing of financial options, with the added advantage of parallelising the training process across multiple economic scenarios.</span></p><div><span><br /></span></div>

Location estimation is significant in mobile and ubiquitous computing systems. The complexity and smaller scale of the indoor environment impose a great impact on location estimation. The key of location estimation lies in the representation and fusion of uncertain information from multiple sources. The improvement of location estimation is a complicated and comprehensive issue. A lot of research has been done to address this issue. However, existing research typically focuses on certain aspects of the problem and specific methods. This paper reviews mainstream schemes on improving indoor location estimation from multiple levels and perspectives by combining existing works and our own working experiences. Initially, we analyze the error sources of common indoor localization techniques and provide a multilayered conceptual framework of improvement schemes for location estimation. This is followed by a discussion of probabilistic methods for location estimation, including Bayes filters, Kalman filters, extended Kalman filters, sigma-point Kalman filters, particle filters, and hidden Markov models. Then, we investigate the hybrid localization methods, including multimodal fingerprinting, triangulation fusing multiple measurements, combination of wireless positioning with pedestrian dead reckoning (PDR), and cooperative localization. Next, we focus on the location determination approaches that fuse spatial contexts, namely, map matching, landmark fusion, and spatial model-aided methods. Finally, we present the directions for future research.

There are two principal notions of security for cryptographic systems. For a few systems, they can be proven to have perfect secrecy against an opponent with unlimited computational power, in terms of information theory. However, the security of most systems, including public key cryptosystems, is based on complexity theoretic assumptions. In both cases there is an implicit notion of average-case analysis. In the case of conditional security, the underlying assumption is usually average-case, not worst case hardness. And for unconditional security, entropy itself is an average case notion of encoding length. Kolmogorov complexity (the size of the smallest program that generates a string) is a rigorous measure of the amount of information, or randomness, in an individual string x. By considering the time-bounded Kolmogorov complexity (program limited to run in time t(|x|)) we can take into account the computational difficulty of extracting information. We present a new notion of security based on Kolmogorov complexity. The first goal is to provide a formal definition of what it means for an individual instance to be secure. The second goal is to bridge the gap between information theoretic security, and computational security, by using time-bounded Kolmogorov complexity. In this paper, we lay the groundwork of the study of cryptosystems from the point of view of security of individual instances by considering three types of information-theoretically secure cryptographic systems: cipher systems (such as the one-time pad), threshold secret sharing, and authentication schemes.

Masquerade attacks are attempts by unauthorized users to gain access to confidential data or greater access privileges, while pretending to be legitimate users. Detection of masquerade attacks is of great importance and is a non-trivial task of system security. While several approaches do exist for masquerade detection, the relative effectiveness of approaches still needs considerable improvement. While in the past certain cost formulations have been used to compute the overall performance of masquerade detection methods, but these formulations appeared to be biased. Hence we present a formulation to compute the effectiveness of masquerade detection and also present a highly effective approach to masquerade detection using Hidden Markov Models (HMM). Our experimentation is on the well-known Schonalu dataset (SEA). Experimentation shows our approach to be most effective in the set of known approaches.

Maximum-entropy approach with higher moments for solving Fokker–Planck equation

Volatility modelling of asset returns is an important aspect for many financial applications, e.g., option pricing and risk management. GARCH models are usually used to model the volatility processes of financial time series. However, multivariate GARCH modelling of volatilities is still a challenge due to the complexity of parameters estimation. To solve this problem, we suggest using Independent Component Analysis (ICA) for transforming the multivariate time series into statistically independent time series. Then, we propose the ICA-GARCH model which is computationally efficient to estimate the volatilities. The experimental results show that this method is more effective to model multivariate time series than existing methods, e.g., PCA-GARCH.KeywordsFinancial EngineeringGARCHICAMultivariate Time SeriesVolatility

The paper considers financial derivatives and option pricing models which are described with the use of diffusiontype partial differential equations (e.g. Black-Scholes models). Using this approach a new filtering method for distributed parameter systems is developed, for estimating option prices variations without knowledge of initial conditions. The proposed filtering method is the so-called Derivative-free nonlinear Kalman Filter and is based on a decomposition of the nonlinear partial-differential equation of the financial system into a set of ordinary differential equations with respect to time. Next, each one of the local models associated with the ordinary differential equations is written in the linear canonical form through a transformation which is based on differential flatness theory. This transformation provides a model of the nonlinear dynamics of the option pricing model for which state estimation is possible by applying the standard Kalman Filter recursion. Based on the obtained state estimate, validation of the Black-Scholes PDE model can be performed and the existence of inconsistent parameters in the Black-Scholes PDE model can be concluded.

The theory and applications of random dynamical systems (RDS) are at the cutting edge of research in mathematics and economics, particularly in modeling the long-run evolution of economic systems subject to exogenous random shocks. Despite this interest, there are no books available that solely focus on RDS in finance and economics. Exploring this emerging area, Random Dynamical Systems in Finance shows how to model RDS in financial applications. Through numerous examples, the book explains how the theory of RDS can describe the asymptotic and qualitative behavior of systems of random and stochastic differential/difference equations in terms of stability, invariant manifolds, and attractors. The authors present many models of RDS and develop techniques for implementing RDS as approximations to financial models and option pricing formulas. For example, they approximate geometric Markov renewal processes in ergodic, merged, double-averaged, diffusion, normal deviation, and Poisson cases and apply the obtained results to option pricing formulas. With references at the end of each chapter, this book provides a variety of RDS for approximating financial models, presents numerous option pricing formulas for these models, and studies the stability and optimal control of RDS. The book is useful for researchers, academics, and graduate students in RDS and mathematical finance as well as practitioners working in the financial industry.

In this Paper, Homotopy- Perturbation method (HPM) which is one of the most recent approximate analytical solutions is implemented to solve diffusion-convection-reaction equations (DCRE). The calculations are carried out for two different types of DCRE's such as the Black-Scholes equation used in financial market option pricing and Fokker- Planck equation from plasma physics. The behavior of the approximate solutions of the distribution functions are shown graphically and are compared with those obtained by other theories (e.g. the Adomian decomposition method, ADM). It shows that the numerical results of these methods are the same; while HPM is much easier, more convenient and more ef- ficient than ADM.