Biblio

In this work an overview of basic approaches to choosing protective measures for automated process control systems is done. The aim of the research was to develop a method for choosing protection measures for information security at every APCs level using set theory within analysis of basic sets of protection measures. In the framework of the research relevant attacks on industrial infrastructure are considered, an algorithm of choosing APCs protective measures is constructed, and it is suggested that it is required to use protective measures for every system level in accordance with an individual assessment of data protection class at the corresponding level. The authors concluded that it is necessary to exclude from consideration “specification of an adapted basic set” of the algorithm for choosing APCs protection measures in case the adapted basic set of APCs protective measures provides blocking all security threats at the considered system level. The approach to choosing protection measures based on building Euler-Venn diagrams is suggested. The results of the research are recommended to be used when modeling information security threats and developing requirements for APCs information protection means.

Homomorphic encryption allows users to perform mathematical operations on open data in encrypted form by performing homomorphically appropriate operations on encrypted data without knowing the decryption function (key). Nowadays such possibilities for cryptoalgorithm are very important in many areas such as data storage, cloud computing, cryptocurrency, and mush more. In 2009 a system of fully homomorphic encryption was constructed, in the future, many works were done based on it. In this work, is performed the implementation of ideal lattices for constructing homomorphic operations over ciphertexts. The idea, presented in this work, allows to separate relations between homomorphic and security parts of a lattice-based homomorphic encryption system.

Insider threat makes great damage to the security of information system, traditional security methods are extremely difficult to work. Insider attack identification plays an important role in insider threat detection. Monitoring user's abnormal behavior is an effective method to detect impersonation, this method is applied to insider threat identification, to built user's behavior attribute information database based on weights changeable feedback tree augmented Bayes network, but data is massive, using the dimensionality reduction based on rough set, to establish the process information model of user's behavior attribute. Using the minimum risk Bayes decision can effectively identify the real identity of the user when user's behavior departs from the characteristic model.

Grassmannian frames in composite dimensions D are constructed as a collection of orthogonal bases where each is the element-wise product of a mask sequence with a generalized Hadamard matrix. The set of mask sequences is obtained by exponentiation of a q-root of unity by different quadratic forms with m variables, where q and m are the product of the unique primes and total number of primes, respectively, in the prime decomposition of D. This method is a generalization of a well-known construction of mutually unbiased bases, as well as second-order Reed-Muller Grassmannian frames for power-of-two dimension D = 2m, and allows to derive highly symmetric nested families of frames with finite alphabet. Explicit sets of symmetric matrices defining quadratic forms leading to constructions in non-prime-power dimension with good distance properties are identified.

We classify .NET files as either benign or malicious by examining directed graphs derived from the set of functions comprising the given file. Each graph is viewed probabilistically as a Markov chain where each node represents a code block of the corresponding function, and by computing the PageRank vector (Perron vector with transport), a probability measure can be defined over the nodes of the given graph. Each graph is vectorized by computing Lebesgue antiderivatives of hand-engineered functions defined on the vertex set of the given graph against the PageRank measure. Files are subsequently vectorized by aggregating the set of vectors corresponding to the set of graphs resulting from decompiling the given file. The result is a fast, intuitive, and easy-to-compute glass-box vectorization scheme, which can be leveraged for training a standalone classifier or to augment an existing feature space. We refer to this vectorization technique as PageRank Measure Integration Vectorization (PMIV). We demonstrate the efficacy of PMIV by training a vanilla random forest on 2.5 million samples of decompiled. NET, evenly split between benign and malicious, from our in-house corpus and compare this model to a baseline model which leverages a text-only feature space. The median time needed for decompilation and scoring was 24ms. 11Code available at https://github.com/gtownrocks/grafuple.

In this paper, we address the web service selection problem for linear workflows. Given a linear workflow specifying a set of ordered tasks and a set of candidate services providing different features for each task, the selection problem deals with the objective of selecting the most eligible service for each task, given the ordering specified. A number of approaches to solving the selection problem have been proposed in literature. With web services growing at an incredible pace, service selection at the Internet scale has resurfaced as a problem of recent research interest. In this work, we present our approach to the selection problem using an abstraction refinement technique to address the scalability limitations of contemporary approaches. Experiments on web service benchmarks show that our approach can add substantial performance benefits in terms of space when compared to an approach without our optimization.

We address the problem of distributed state estimation of a linear dynamical process in an attack-prone environment. A network of sensors, some of which can be compromised by adversaries, aim to estimate the state of the process. In this context, we investigate the impact of making a small subset of the nodes immune to attacks, or “trusted”. Given a set of trusted nodes, we identify separate necessary and sufficient conditions for resilient distributed state estimation. We use such conditions to illustrate how even a small trusted set can achieve a desired degree of robustness (where the robustness metric is specific to the problem under consideration) that could otherwise only be achieved via additional measurement and communication-link augmentation. We then establish that, unfortunately, the problem of selecting trusted nodes is NP-hard. Finally, we develop an attack-resilient, provably-correct distributed state estimation algorithm that appropriately leverages the presence of the trusted nodes.

Control algorithms, like model predictive control, can be computationally expensive and may benefit from being executed over the cloud. This is especially the case for nodes at the edge of a network since they tend to have reduced computational capabilities. However, control over the cloud requires transmission of sensitive data (e.g., system dynamics, measurements) which undermines privacy of these nodes. When choosing a method to protect the privacy of these data, efficiency must be considered to the same extent as privacy guarantees to ensure adequate control performance. In this paper, we review a transformation-based method for protecting privacy, previously introduced by the authors, and quantify the level of privacy it provides. Moreover, we also consider the case of adversaries with side knowledge and quantify how much privacy is lost as a function of the side knowledge of the adversary.

Internet of Things (IoT) is an evolving research area for the last two decades. The integration of the IoT and social networking concept results in developing an interdisciplinary research area called the Social Internet of Things (SIoT). The SIoT is dominant over the traditional IoT because of its structure, implementation, and operational manageability. In the SIoT, devices interact with each other independently to establish a social relationship for collective goals. To establish trustworthy relationships among the devices significantly improves the interaction in the SIoT and mitigates the phenomenon of risk. The problem is to choose a trustworthy node who is most suitable according to the choice parameters of the node. The best-selected node by one node is not necessarily the most suitable node for other nodes, as the trustworthiness of the node is independent for everyone. We employ some theoretical characterization of the soft-set theory to deal with this kind of decision-making problem. In this paper, we developed a weighted based trustworthiness ranking model by using soft set theory to evaluate the trustworthiness in the SIoT. The purpose of the proposed research is to reduce the risk of fraudulent transactions by identifying the most trusted nodes.

In order to solve the interference of burrs, aliasing and other noises in the background area of millimeter wave human security inspection on the objects identification, an adaptive template matching filtering method is proposed. First, the preprocessed original image is segmented by level set algorithm, then the result is used as a template to filter the background of the original image. Finally, the image after background filtered is used as the input of bilateral filtering. The contrast experiments based on the actual millimeter wave image verifies the improvement of this algorithm compared with the traditional filtering method, and proves that this algorithm can filter the background noise of the human security image, retain the image details of the human body area, and is conducive to the object recognition and location in the millimeter wave security image.

Shamir's Secret Sharing Scheme is well established and widely used. It allows a so-called Dealer to split and share a secret k among n Participants such that at least t shares are needed to reconstruct k, where 0 \textbackslashtextbackslashtextless; t ≤ n. Nothing about the secret can be learned from less than t shares. To split secret k, the Dealer generates a polynomial f, whose independent term is k and the coefficients are randomly selected using a uniform distribution. A share is a pair (x, f(x)) where x is also chosen randomly using a uniform distribution. This scheme is useful, for example, to distribute cryptographic keys among different cloud providers and to create multi-factor authentication. The security of Shamir's Secret Sharing Scheme is usually analyzed using a threat model where the Dealer is trusted to split and share secrets as described above. In this paper, we demonstrate that there exists a different threat model where a malicious Dealer can compute shares such that a subset of less than t shares is allowed to reconstruct the secret. We refer to such subsets as hidden sets. We formally define hidden sets and prove lower bounds on the number of possible hidden sets for polynomials of degree t - 1. Yet, we show how to detect hidden sets given a set of n shares and describe how to create hidden sets while sharing a secret using a modification of Shamir's scheme.

Nearest-Neighbor Search (NNS) arises as a key component of sampling-based motion planning algorithms and it is known as their asymptotic computational bottleneck. Algorithms for exact Nearest-Neighbor Search rely on explicit distance comparisons to different extents. However, in motion planning, evaluating distances is generally a computationally demanding task, since the metric is induced by the minimum cost of steering a dynamical system between states. In the presence of driftless nonholonomic constraints, we propose efficient pruning techniques for the k-d tree algorithm that drastically reduce the number of distance evaluations performed during a query. These techniques exploit computationally convenient lower and upper bounds to the geodesic distance of the corresponding sub-Riemannian geometry. Based on asymptotic properties of the reachable sets, we show that the proposed pruning techniques are optimal, modulo a constant factor, and we provide experimental results with the Reeds-Shepp vehicle model.

To discuss secure key generation from imperfect random numbers, we address the secure key generation length. There are several studies for its asymptotic expansion up to the order √n or log n. However, these expansions have errors of the order o(√n) or o(log n), which does not go to zero asymptotically. To resolve this problem, we derive the asymptotic expansion up to the constant order for upper and lower bounds of these optimal values. While the expansions of upper and lower bonds do not match, they clarify the ranges of these optimal values, whose errors go to zero asymptotically.

This work proposes a moving target defense (MTD) strategy to detect coordinated cyber-physical attacks (CCPAs) against power grids. A CCPA consists of a physical attack, such as disconnecting a transmission line, followed by a coordinated cyber attack that injects false data into the sensor measurements to mask the effects of the physical attack. Such attacks can lead to undetectable line outages and cause significant damage to the grid. The main idea of the proposed approach is to invalidate the knowledge that the attackers use to mask the effects of the physical attack by actively perturbing the grid's transmission line reactances using distributed flexible AC transmission system (D-FACTS) devices. We identify the MTD design criteria in this context to thwart CCPAs. The proposed MTD design consists of two parts. First, we identify the subset of links for D-FACTS device deployment that enables the defender to detect CCPAs against any link in the system. Then, in order to minimize the defense cost during the system's operational time, we use a game-theoretic approach to identify the best subset of links (within the D-FACTS deployment set) to perturb which will provide adequate protection. Extensive simulations performed using the MATPOWER simulator on IEEE bus systems verify the effectiveness of our approach in detecting CCPAs and reducing the operator's defense cost.

In this article, we introduce a new method for computing fixed points of a class of iterated functions in a finite time, by exponentiating linear multivalued operators. To better illustrate this approach and show that our method can give fast and accurate results, we have chosen two well-known applications which are difficult to handle by usual techniques. First, we apply the exponentiation of linear operators to a digital filter in order to get a fine approximation of its behavior at an arbitrary time. Second, we consider a PID controller. To get a reliable estimate of its control function, we apply the exponentiation of a bundle of linear operators. Note that, our technique can be applied in a more general setting, i.e. for any multivalued linear map and that the general method is also introduced in this article.

In this paper a novel set-theoretic control framework for Cyber-Physical Systems is presented. By resorting to set-theoretic ideas, an anomaly detector module and a control remediation strategy are formally derived with the aim to contrast cyber False Data Injection (FDI) attacks affecting the communication channels. The resulting scheme ensures Uniformly Ultimate Boundedness and constraints fulfillment regardless of any admissible attack scenario.

This paper proposes a novel scheme for RFID anti-counterfeiting by applying bisectional multivariate quadratic equations (BMQE) system into an RF tag data encryption. In the key generation process, arbitrarily choose two matrix sets (denoted as A and B) and a base Rab such that [AB] = λRABT, and generate 2n BMQ polynomials (denoted as p) over finite field Fq. Therefore, (Fq, p) is taken as a public key and (A, B, λ) as a private key. In the encryption process, the EPC code is hashed into a message digest dm. Then dm is padded to d'm which is a non-zero 2n×2n matrix over Fq. With (A, B, λ) and d'm, Sm is formed as an n-vector over F2. Unlike the existing anti-counterfeit scheme, the one we proposed is based on quantum cryptography, thus it is robust enough to resist the existing attacks and has high security.

Many aspects of our daily lives now rely on computers, including communications, transportation, government, finance, medicine, and education. However, with increased dependence comes increased vulnerability. Therefore recognizing attacks quickly is critical. In this paper, we introduce a new anomaly detection algorithm based on persistent homology, a tool which computes summary statistics of a manifold. The idea is to represent a cyber network with a dynamic point cloud and compare the statistics over time. The robustness of persistent homology makes for a very strong comparison invariant.

Easy sharing files in public network that is intended only for certain people often resulting in the leaking of sharing folders or files and able to be read also by others who are not authorized. Secure data is one of the most challenging issues in data sharing systems. Here, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is a reliable asymmetric encryption mechanism which deals with secure data and used for data encryption. It is not necessary encrypted to one particular user, but recipient is only able to decrypt if and only if the attribute set of his private key match with the specified policy in the ciphertext. In this paper, we propose a secure data exchange using CP-ABE with authentication feature. The data is attribute-based encrypted to satisfy confidentiality feature and authenticated to satisfy data authentication simultaneously.

Formal methods, models and tools for social big data analytics are largely limited to graph theoretical approaches such as social network analysis (SNA) informed by relational sociology. There are no other unified modeling approaches to social big data that integrate the conceptual, formal and software realms. In this paper, we first present and discuss a theory and conceptual model of social data. Second, we outline a formal model based on set theory and discuss the semantics of the formal model with a real-world social data example from Facebook. Third, we briefly present and discuss the Social Data Analytics Tool (SODATO) that realizes the conceptual model in software and provisions social data analysis based on the conceptual and formal models. Fourth and last, based on the formal model and sentiment analysis of text, we present a method for profiling of artifacts and actors and apply this technique to the data analysis of big social data collected from Facebook page of the fast fashion company, H&M.
 

Mutation analysis generates tests that distinguish variations, or mutants, of an artifact from the original. Mutation analysis is widely considered to be a powerful approach to testing, and hence is often used to evaluate other test criteria in terms of mutation score, which is the fraction of mutants that are killed by a test set. But mutation analysis is also known to provide large numbers of redundant mutants, and these mutants can inflate the mutation score. While mutation approaches broadly characterized as reduced mutation try to eliminate redundant mutants, the literature lacks a theoretical result that articulates just how many mutants are needed in any given situation. Hence, there is, at present, no way to characterize the contribution of, for example, a particular approach to reduced mutation with respect to any theoretical minimal set of mutants. This paper's contribution is to provide such a theoretical foundation for mutant set minimization. The central theoretical result of the paper shows how to minimize efficiently mutant sets with respect to a set of test cases. We evaluate our method with a widely-used benchmark.