Biblio

A recent paper (Oakland 2017) discussed science and security research in the context of the government-funded Science of Security movement, and the history and prospects of security as a scientific pursuit. It drew on literature from within the security research community, and mature history and philosophy of science literature. The paper sparked debate in numerous organizations and the security community. Here we consider some of the main ideas, provide a summary list of relevant literature, and encourage discussion within the Moving Target Defense (MTD) sub-community1.

Process anomaly is used for detecting cyber-physical attacks on critical infrastructure such as plants for water treatment and electric power generation. Identification of process anomaly is possible using rules that govern the physical and chemical behavior of the process within a plant. These rules, often referred to as invariants, can be derived either directly from plant design or from the data generated in an operational. However, for operational legacy plants, one might consider a data-centric approach for the derivation of invariants. The study reported here is a comparison of design-centric and data-centric approaches to derive process invariants. The study was conducted using the design of, and the data generated from, an operational water treatment plant. The outcome of the study supports the conjecture that neither approach is adequate in itself, and hence, the two ought to be integrated.

In a world of ever-increasing systems interdependence, effective cybersecurity policy design seems to be one of the most critically understudied elements of our national security strategy. Enterprise cyber technologies are often implemented without much regard to the interactions that occur between humans and the new technology. Furthermore, the interactions that occur between individuals can often have an impact on the newly employed technology as well. Without a rigorous, evidence-based approach to ground an employment strategy and elucidate the emergent organizational needs that will come with the fielding of new cyber capabilities, one is left to speculate on the impact that novel technologies will have on the aggregate functioning of the enterprise. In this paper, we will explore a scenario in which a hypothetical government agency applies a complexity science perspective, supported by agent-based modeling, to more fully understand the impacts of strategic policy decisions. We present a model to explore the socio-technical dynamics of these systems, discuss lessons using this platform, and suggest further research and development.

The U.S. federal governments and agencies face increasingly sophisticated and persistent cyber threats and cyberattacks from black hat hackers who breach cybersecurity for malicious purposes or for personal gain. With the rise of malicious attacks that caused untold financial damage and substantial reputational damage, private-sector high-tech firms such as Google, Microsoft and Yahoo have adopted an innovative practice known as vulnerability reward program (VRP) or bug bounty program which crowdsources software bug detection from the cybersecurity community. In an alignment with the 2016 U.S. Cybersecurity National Action Plan, the Department of Defense adopted a pilot VRP in 2016. This paper examines the Pentagon's VRP and examines how it may fit with the national cybersecurity policy and the need for new and enhanced cybersecurity capability development. Our case study results show the feasibility of the government adoption and implementation of the innovative concept of VRP to enhance the government cybersecurity posture.

Today cyber security is one of the most active fields of re- search due to its wide range of impact in business, govern- ment and everyday life. In recent years machine learning methods and algorithms have been quite successful in a num- ber of security areas. In this paper, we explore an approach to classify intrusion called multi-perspective machine learn- ing (MPML). For any given cyber-attack there are multiple methods of detection. Every method of detection is built on one or more network characteristic. These characteristics are then represented by a number of network features. The main idea behind MPML is that, by grouping features that support the same characteristics into feature subsets called perspectives, this will encourage diversity among perspectives (classifiers in the ensemble) and improve the accuracy of prediction. Initial results on the NSL- KDD dataset show at least a 4% improvement over other ensemble methods such as bagging boosting rotation forest and random for- est.

Organizations develop technical and procedural measures to protect information systems. Relying only on technical based security solutions is not enough. Organizations must consider technical security solutions along with social, human, and organizational factors. The human element represents the employees (insiders) who use the information systems and other technology resources in their day-to-day operations. ISP awareness is essential to protect organizational information systems. This study adapts the Innovation Diffusion Theory to examine the antecedents of ISP awareness and its impact on the satisfaction with ISP and security practices. A sample of 236 employees in universities in the United States is collected to evaluate the research model. Results indicated that ISP quality, self-efficacy, and technology security awareness significantly impact ISP awareness. The current study presents significant contributions toward understanding the antecedents of ISP awareness and provides a starting point toward including satisfaction aspect in information security behavioral domain.

This paper suggests a conceptual mechanism for increasing the security level of the global information community, national information technology infrastructures (e-governments) and private cloud structures, which uses the logical characteristic of IPv6-protocol. The mechanism is based on the properties of the IPv6-header and, in particular, rules of coding IPv6-addresses.

This study seeks to investigate how the development of e-government services impacts on cybersecurity. The study uses the methods of correlation and multiple regression to analyse two sets of global data, the e-government development index of the 2015 United Nations e-government survey and the 2015 International Telecommunication Union global cybersecurity development index (GCI 2015). After analysing the various contextual factors affecting e-government development, the study found that, various composite measures of e-government development are significantly correlated with cybersecurity development. The therefore study contributes to the understanding of the relationship between e-government and cybersecurity development. The authors developed a model to highlight this relationship and have validated the model using empirical data. This is expected to provide guidance on specific dimensions of e-government services that will stimulate the development of cybersecurity. The study provided the basis for understanding the patterns in cybersecurity development and has implication for policy makers in developing trust and confidence for the adoption e-government services.

There were many researches about the parameter estimation of canonical dynamic systems recently. Extended Kalman filter (EKF) is a popular parameter estimation method in virtue of its easy applications. This paper focuses on parameter estimation for a class of canonical dynamic systems by EKF. By constructing associated differential equation, the convergence of EKF parameter estimation for the canonical dynamic systems is analyzed. And the simulation demonstrates the good performance.

A general approach to the synthesis of the conditionally unstable fuzzy controller is introduced in this paper. This approach allows tuning the output signal of the system for both fast and smooth transient. Fuzzy logic allows combining the properties of several strategies of system tuning dependent on the state of the system. The utilization of instability allows achieving faster transient when the error of the system output is beyond the predefined value. Later the system roots are smoothly moved to the left-hand side of the complex s-plane due to the change of the membership function values. The results of the proposed approaches are compared with the results obtained using traditional methods of controller synthesis.

In this paper, we investigate the Bayesian filtering problem for discrete nonlinear dynamical systems which contain random parameters. An augmented cubature Kalman filter (CKF) is developed to deal with the random parameters, where the state vector is enlarged by incorporating the random parameters. The corresponding number of cubature points is increased, so the augmented CKF method requires more computational complexity. However, the estimation accuracy is improved in comparison with that of the classical CKF method which uses the nominal values of the random parameters. An application to the mobile source localization with time difference of arrival (TDOA) measurements and random sensor positions is provided where the simulation results illustrate that the augmented CKF method leads to a superior performance in comparison with the classical CKF method.

The problem of analytical synthesis of the reduced order state observer for the bilinear dynamic system with scalar input and vector output has been considered. Formulas for calculation of the matrix coefficients of the nonlinear observer with estimation error asymptotically approaching zero have been obtained. Two modifications of observer dynamic equation have been proposed: the first one requires differentiation of an output signal and the second one does not. Based on the matrix canonization technology, the solvability conditions for the synthesis problem and analytical expressions for an acceptable set of solutions have been received. A precise step-by-step algorithm for calculating the observer coefficients has been offered. An example of the practical use of the developed algorithm has been given.

In this paper, based on the Hamiltonian, an alternative interpretation about the iterative adaptive dynamic programming (ADP) approach from the perspective of optimization is developed for discrete time nonlinear dynamic systems. The role of the Hamiltonian in iterative ADP is explained. The resulting Hamiltonian driven ADP is able to evaluate the performance with respect to arbitrary admissible policies, compare two different admissible policies and further improve the given admissible policy. The convergence of the Hamiltonian ADP to the optimal policy is proven. Implementation of the Hamiltonian-driven ADP by neural networks is discussed based on the assumption that each iterative policy and value function can be updated exactly. Finally, a simulation is conducted to verify the effectiveness of the presented Hamiltonian-driven ADP.

The new criterion for selecting the frequencies of the test polyharmonic signals is developed. It allows uniquely filtering the values of multidimensional transfer functions - Fourier-images of Volterra kernel from the partial component of the response of a nonlinear system. It is shown that this criterion significantly weakens the known limitations on the choice of frequencies and, as a result, reduces the number of interpolations during the restoration of the transfer function, and, the more significant, the higher the order of estimated transfer function.

Multi-robot transfer learning allows a robot to use data generated by a second, similar robot to improve its own behavior. The potential advantages are reducing the time of training and the unavoidable risks that exist during the training phase. Transfer learning algorithms aim to find an optimal transfer map between different robots. In this paper, we investigate, through a theoretical study of single-input single-output (SISO) systems, the properties of such optimal transfer maps. We first show that the optimal transfer learning map is, in general, a dynamic system. The main contribution of the paper is to provide an algorithm for determining the properties of this optimal dynamic map including its order and regressors (i.e., the variables it depends on). The proposed algorithm does not require detailed knowledge of the robots' dynamics, but relies on basic system properties easily obtainable through simple experimental tests. We validate the proposed algorithm experimentally through an example of transfer learning between two different quadrotor platforms. Experimental results show that an optimal dynamic map, with correct properties obtained from our proposed algorithm, achieves 60-70% reduction of transfer learning error compared to the cases when the data is directly transferred or transferred using an optimal static map.

In this paper, we describe an abstraction-based method for synthesizing a state-based switching control for stabilizing a family of dynamical systems. Given a set of dynamical systems and a set of polyhedral switching surfaces, the algorithm synthesizes a strategy that assigns to every surface the linear dynamics to switch to at the surface. Our algorithm constructs a finite game graph that consists of the switching surfaces as the existential nodes and the choices of the dynamics as the universal nodes. In addition, the edges capture quantitative information about the evolution of the distance of the state from the equilibrium point along the executions. A switching strategy for the family of dynamical systems is extracted by finding a strategy on the game graph which results in plays having a bounded weight. Such a strategy is obtained by reducing the problem to the strategy synthesis for an energy game, which is a well-studied problem in the literature. We have implemented our algorithm for polyhedral inclusion dynamics and linear dynamics. We illustrate our algorithm on examples from these two classes of systems.

Modelling complex dynamical systems plays a crucial role to understand several phenomena in different domains such as physics, engineering, biology and social sciences. In this paper, a genetic programming ensemble method is proposed to learn complex dynamical systems' underlying mathematical models, represented as differential equations, from systems' time series observations. The proposed method relies on decomposing the modelling space based on given variable dependencies. An ensemble of learners is then applied in this decomposed space and their output is combined to generate the final model. Two examples of complex dynamical systems are used to test the performance of the proposed methodology where the standard genetic programming method has struggled to find matching model equations. The empirical results show the effectiveness of the proposed methodology in learning closely matching structure of almost all system equations.

The fast and energy-efficient simulation of dynamical systems defined by coupled ordinary/partial differential equations has emerged as an important problem. The accelerated simulation of coupled ODE/PDE is critical for analysis of physical systems as well as computing with dynamical systems. This paper presents a fast and programmable accelerator for simulating dynamical systems. The computing model of the proposed platform is based on multilayer cellular nonlinear network (CeNN) augmented with nonlinear function evaluation engines. The platform can be programmed to accelerate wide classes of ODEs/PDEs by modulating the connectivity within the multilayer CeNN engine. An innovative hardware architecture including data reuse, memory hierarchy, and near-memory processing is designed to accelerate the augmented multilayer CeNN. A dataflow model is presented which is supported by optimized memory hierarchy for efficient function evaluation. The proposed solver is designed and synthesized in 15nm technology for the hardware analysis. The performance is evaluated and compared to GPU nodes when solving wide classes of differential equations and the power consumption is analyzed to show orders of magnitude improvement in energy efficiency.

The Polytope Escape Problem for continuous linear dynamical systems consists of deciding, given an affine function f:Rd -\textbackslashtextgreater Rd and a convex polytope P⊆ Rd, both with rational descriptions, whether there exists an initial point x0 in P such that the trajectory of the unique solution to the differential equation: ·x(t)=f(x(t)) x 0= x0 is entirely contained in P. We show that this problem is reducible in polynomial time to the decision version of linear programming with real algebraic coefficients. The latter is a special case of the decision problem for the existential theory of real closed fields, which is known to lie between NP and PSPACE. Our algorithm makes use of spectral techniques and relies, among others, on tools from Diophantine approximation.

Nowadays, data has become more important as the core resource for the information society. However, with the development of data analysis techniques, the privacy violation such as leakage of sensitive data and personal identification exposure are also increasing. Differential privacy is the technique to satisfy the requirement that any additional information should not be disclosed except information from the database itself. It is well known for protecting the privacy from arbitrary attack. However, recent research argues that there is a several ways to infer sensitive information from data although the differential privacy is applied. One of this inference method is to use the correlation between the data. In this paper, we investigate the new privacy threats using attribute correlation which are not covered by traditional studies and propose a privacy preserving technique that configures the differential privacy's noise parameter to solve this new threat. In the experiment, we show the weaknesses of traditional differential privacy method and validate that the proposed noise parameter configuration method provide a sufficient privacy protection and maintain an accuracy of data utility.

As more and more technologies to store and analyze massive amount of data become available, it is extremely important to make privacy-sensitive data de-identified so that further analysis can be conducted by different parties. For example, data needs to go through data de-identification process before being transferred to institutes for further value added analysis. As such, privacy protection issues associated with the release of data and data mining have become a popular field of study in the domain of big data. As a strict and verifiable definition of privacy, differential privacy has attracted noteworthy attention and widespread research in recent years. Nevertheless, differential privacy is not practical for most applications due to its performance of synthetic dataset generation for data query. Moreover, the definition of data protection by randomized noise in native differential privacy is abstract to users. Therefore, we design a pragmatic DP-based data de-identification protection and risk of data disclosure estimation system, in which a DP-based noise addition mechanism is applied to generate synthetic datasets. Furthermore, the risk of data disclosure to these synthetic datasets can be evaluated before releasing to buyers/consumers.

Differential privacy is an approach that preserves patient privacy while permitting researchers access to medical data. This paper presents mechanisms proposed to satisfy differential privacy while answering a given workload of range queries. Representing input data as a vector of counts, these methods partition the vector according to relationships between the data and the ranges of the given queries. After partitioning the vector into buckets, the counts of each bucket are estimated privately and split among the bucket's positions to answer the given query set. The performance of the proposed method was evaluated using different workloads over several attributes. The results show that partitioning the vector based on the data can produce more accurate answers, while partitioning the vector based on the given workload improves privacy. This paper's two main contributions are: (1) improving earlier work on partitioning mechanisms by building a greedy algorithm to partition the counts' vector efficiently, and (2) its adaptive algorithm considers the sensitivity of the given queries before providing results.

It is a challenging problem to preserve the friendly-correlations between individuals when publishing social-network data. To alleviate this problem, uncertain graph has been presented recently. The main idea of uncertain graph is converting an original graph into an uncertain form, where the correlations between individuals is an associated probability. However, the existing methods of uncertain graph lack rigorous guarantees of privacy and rely on the assumption of adversary's knowledge. In this paper we first introduced a general model for constructing uncertain graphs. Then, we proposed an algorithm under the model which is based on differential privacy and made an analysis of algorithm's privacy. Our algorithm provides rigorous guarantees of privacy and against the background knowledge attack. Finally, the algorithm we proposed satisfied differential privacy and showed feasibility in the experiments. And then, we compare our algorithm with (k, ε)-obfuscation algorithm in terms of data utility, the importance of nodes for network in our algorithm is similar to (k, ε)-obfuscation algorithm.

To enhance privacy protection and improve data availability, a differential privacy data protection method ICMD-DP is proposed. Based on insensitive clustering algorithm, ICMD-DP performs differential privacy on the results of ICMD (insensitive clustering method for mixed data). The combination of clustering and differential privacy realizes the differentiation of query sensitivity from single record to group record. At the meanwhile, it reduces the risk of information loss and information disclosure. In addition, to satisfy the requirement of maintaining differential privacy for mixed data, ICMD-DP uses different methods to calculate the distance and centroid of categorical and numerical attributes. Finally, experiments are given to illustrate the availability of the method.

Comparing with the traditional grid, energy internet will collect data widely and connect more broader. The analysis of electrical data use of Non-intrusive Load Monitoring (NILM) can infer user behavior privacy. Consideration both data security and availability is a problem must be addressed. Due to its rigid and provable privacy guarantee, Differential Privacy has proverbially reached and applied to privacy preserving data release and data mining. Because of its high sensitivity, increases the noise directly will led to data unavailable. In this paper, we propose a differentially private mechanism to protect energy internet privacy. Our focus is the aggregated data be released by data owner after added noise in disaggregated data. The theoretically proves and experiments show that our scheme can achieve the purpose of privacy-preserving and data availability.