Biblio

Learning-enabled components (LECs) are widely used in cyber-physical systems (CPS) since they can handle the uncertainty and variability of the environment and increase the level of autonomy. However, it has been shown that LECs such as deep neural networks (DNN) are not robust and adversarial examples can cause the model to make a false prediction. The paper considers the problem of efficiently detecting adversarial examples in LECs used for regression in CPS. The proposed approach is based on inductive conformal prediction and uses a regression model based on variational autoencoder. The architecture allows to take into consideration both the input and the neural network prediction for detecting adversarial, and more generally, out-of-distribution examples. We demonstrate the method using an advanced emergency braking system implemented in an open source simulator for self-driving cars where a DNN is used to estimate the distance to an obstacle. The simulation results show that the method can effectively detect adversarial examples with a short detection delay.

Transactive Energy (TE) is an emerging discipline that utilizes economic and control techniques for operating and managing the power grid effectively. Distributed Energy Resources (DERs) represent a fundamental shift away from traditionally centrally managed energy generation and storage to one that is rather distributed. However, integrating and managing DERs into the power grid is highly challenging owing to the TE implementation issues such as privacy, equity, efficiency, reliability, and security. The TE market structures allow utilities to transact (i.e., buy and sell) power services (production, distribution, and storage) from/to DER providers integrated as part of the grid. Flexible power pricing in TE enables power services transactions to dynamically adjust power generation and storage in a way that continuously balances power supply and demand as well as minimize cost of grid operations. Therefore, it has become important to analyze various market models utilized in different TE applications for their impact on above implementation issues.In this demo, we show-case the Transactive Energy Simulation and Analysis Toolsuite (TE-SAT) with its three publicly available design studios for experimenting with TE markets. All three design studios are built using metamodeling tool called the Web-based Graphical Modeling Environment (WebGME). Using a Git-like storage and tracking backend server, WebGME enables multi-user editing on models and experiments using simply a web-browser. This directly facilitates collaboration among different TE stakeholders for developing and analyzing grid operations and market models. Additionally, these design studios provide an integrated and scalable cloud backend for running corresponding simulation experiments.

Anomaly detection classification technology based on deep learning is one of the crucial technologies supporting network security. However, as the data increasing, this traditional model cannot guarantee that the false alarm rate is minimized while meeting the high detection rate. Additionally, distribution of imbalanced abnormal samples will lead to an increase in the error rate of the classification results. In this work, since CNN is effective in network intrusion classification, we embed a compressed feature layer in CNN (Convolutional Neural Networks). The purpose is to improve the efficiency of network intrusion detection. After our model was trained for 55 epochs and we set the learning rate of the model to 0.01, the detection rate reaches over 98%.

In recent years, neural networks have been implemented while solving various tasks. Deep learning algorithms provide state of the art performance in computer vision, NLP, speech recognition, speaker recognition and many other fields. In spite of the good performance, neural networks have significant drawback- they have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. While being imperceptible to a human eye, such perturbations lead to significant drop in classification accuracy. It is demonstrated by many studies related to neural network security. Considering the pros and cons of neural networks, as well as a variety of their applications, developing of the methods to improve the robustness of neural networks against adversarial attacks becomes an urgent task. In the article authors propose the “minimalistic” attacker model of the decision support system identification unit, adaptive recommendations on security enhancing, and a set of protective methods. Suggested methods allow for significant increase in classification accuracy under adversarial attacks, as it is demonstrated by an experiment outlined in this article.

The object of research is information security. The tools used for research are artificial neural networks. The goal is to increase the cryptography security. The problems are: the big volume of information, the expenses for neural networks design and training. It is offered to use the neural network for the cryptographic transformation of information.

Software-Defined Networking (SDN) simplifies network management by separating the control plane from the data forwarding plane. However, the plane separation technology introduces many new loopholes in the SDN data plane. In order to facilitate taking proactive measures to reduce the damage degree of network security events, this paper proposes a security situation prediction method based on particle swarm optimization algorithm and long-short-term memory neural network for network security events on the SDN data plane. According to the statistical information of the security incident, the analytic hierarchy process is used to calculate the SDN data plane security situation risk value. Then use the historical data of the security situation risk value to build an artificial neural network prediction model. Finally, a prediction model is used to predict the future security situation risk value. Experiments show that this method has good prediction accuracy and stability.

The proliferation of artificial intelligence based systems in all walks of life raises concerns about their safety and robustness, especially for cyber-physical systems including multiple machine learning components. In this paper, we introduce robustness contracts as a framework for compositional specification and reasoning about the robustness of cyber-physical systems based on neural network (NN) components. Robustness contracts can encompass and generalize a variety of notions of robustness which were previously proposed in the literature. They can seamlessly apply to NN-based perception as well as deep reinforcement learning (RL)-enabled control applications. We present a sound and complete algorithm that can efficiently verify the satisfaction of a class of robustness contracts on NNs by leveraging notions from Lagrangian duality to identify system configurations that violate the contracts. We illustrate the effectiveness of our approach on the verification of NN-based perception systems and deep RL-based control systems.

The usage of connected devices and their role within our daily- and business life gains more and more impact. In addition, various derivations of Cyber-Physical Systems (CPS) reach new business fields, like smart healthcare or Industry 4.0. Although these systems do bring many advantages for users by extending the overall functionality of existing systems, they come with several challenges, especially for system engineers and architects. One key challenge consists in achieving a sufficiently high level of security within the CPS environment, as sensitive data or safety-critical functions are often integral parts of CPS. Being system of systems (SoS), CPS complexity, unpredictability and heterogeneity complicate analyzing the overall level of security, as well as providing a way to detect ongoing attacks. Usually, security metrics and frameworks provide an effective tool to measure the level of security of a given component or system. Although several comprehensive surveys exist, an assessment of the effectiveness of the existing solutions for CPS environments is insufficiently investigated in literature. In this work, we address this gap by benchmarking a carefully selected variety of existing security metrics in terms of their usability for CPS. Accordingly, we pinpoint critical CPS challenges and qualitatively assess the effectiveness of the existing metrics for CPS systems.

Compressive sensing (CS) is beneficial for unmanned reconnaissance systems to obtain high-quality images with limited resources. The existing prediction methods for block-based compressive sensing of images can be regarded as the particular coefficients of weighted predictive coding. To find better prediction coefficients for BCS, this paper proposes two weighted prediction methods. The first method converts the prediction model of measurements into a prediction model of image blocks. The prediction weights are obtained by training the prediction model of image blocks offline, which avoiding the influence of the sampling rates on the prediction model of measurements. Another method is to calculate the prediction coefficients adaptively based on the average energy of measurements, which can adjust the weights based on the measurements. Compared with existing methods, the proposed prediction methods for BCS of images can further improve the reconstruction image quality.

This paper examines the influence of quantization on the compressive sensing theory applied to the nonuniformly sampled nonsparse signals with reduced set of randomly positioned measurements. The error of the reconstruction will be generalized to exact expected squared error expression. The aim is to connect the generalized random sampling strategy with the quantization effect, finding the resulting error of the reconstruction. Small sampling deviations correspond to the imprecisions of the sampling strategy, while completely random sampling schemes causes large sampling deviations. Numerical examples provide an agreement between the statistical results and theoretical values.

This paper proposes a Time Difference of Arrival (TDoA) based method for the localization of Radio Frequency (RF) emitters working at different carriers, by using wideband spectrum sensors exploiting compressive sampling. The proposed measurement method is based on four or more RF receivers, with known Cartesian positions, performing non uniform sampling on the received signal. By means of simulations, the method has been compared against a localization method adopting RF receivers performing uniform sampling at Nyquist rate. The obtained preliminary results demonstrate that the method is capable of localizing two RF emitters achieving the same results obtained with uniform sampling, with a compression ratio up to CR = 20.

Compressive Sensing (CS) is a promising technology for the acquisition of signals. The number of measurements is reduced by using CS which is needed to obtain the signals in some basis that are compressible or sparse. The compressible or sparse nature of the signals can be obtained by transforming the signals in some domain. Depending on the signals sparsity signals are sampled below the Nyquist sampling criteria by using CS. An optimization problem needs to be solved for the recovery of the original signal. Very few studies have been reported about the reconstruction of the signals. Therefore, in this paper, the reconstruction algorithms are elaborated systematically for sparse signal recovery in CS. The discussion of various reconstruction algorithms in made in this paper will help the readers in order to understand these algorithms efficiently.

We propose a photonic compressive sampling scheme based on multimode fiber for radio spectrum sensing, which shows high accuracy and stability, and low complexity and cost. Pulse overlapping is utilized for a fast detection. © 2020 The Author(s).

The deep learning-based compressive Sensing (CS) has shown substantial improved performance and in run-time reduction with signal sampling and reconstruction. In most cases, moreover, these techniques suffer from disrupting artefacts or high-frequency contents at low sampling ratios. Similarly, this occurs in the multi-resolution sampling method, which further collects more components with lower frequencies. A promising innovation combining CS with convolutionary neural network has eliminated the sparsity constraint yet recovery persists slow. We propose a Deep wavelet based compressive sensing with multi-resolution framework provides better improvement in reconstruction as well as run time. The proposed model demonstrates outstanding quality on test functions over previous approaches.

Data sampling is critical process for energy constrained Wireless Sensor Networks. In this article, we proposed a Predictive Data Recovery Compressive Sensing (PDR-CS) procedure for data sampling. PDR-CS samples data measurements from the monitoring field on the basis of spatial and temporal correlation and sparse measurements recovered at the Sink. Our proposed algorithm, PDR-CS extends the iterative re-weighted -ℓ1(IRW - ℓ1) minimization and regularization on the top of Spatio-temporal compressibility for enhancing accuracy of signal recovery and reducing the energy consumption. The simulation study shows that from the less number of samples are enough to recover the signal. And also compared with the other compressive sensing procedures, PDR-CS works with less time.

The work we present is a comparative study based on an experimental approach to the mechanical and thermal properties of different local clay-based building materials with the incorporation of agricultural waste in Chad. These local building materials have been used since ancient times by the low-income population. They were the subject of a detailed characterization of their mechanical and thermal parameters. The objective is to obtain lightweight materials with good thermomechanical performance and which can contribute to improving thermal comfort, energy-saving, and security in social housing in Chad while reducing the cost of investment. Several clay-based samples with increasing incorporation of 0 to 8% of agricultural waste (cow dung or millet pod) were made. We used appropriate experimental methods for porous materials (the hydraulic press for mechanical tests and the box method for thermal tests). In this article, we have highlighted the values and variations of the mechanical compressive resistances, thermal conductivities, and thermal resistances of test pieces made with these materials. Knowing the mechanical and thermal characteristics, we also carried out a thermomechanical study. The thermal data made it possible to make Dynamic Thermal Simulations (STD) of the buildings thanks to the Pléiades + COMFIE software. The results obtained show that the use of these materials in a building presents good mechanical and thermal performance with low consumption of electrical energy for better thermal comfort of the occupants. Thus agricultural waste can be recovered thanks to its integration into building materials based on clay.

Compressive sensing (CS) is regarded as one of the promising solutions for IoT data encryption as it achieves simultaneous sampling, compression, and encryption. Theoretical work in the literature has proved that CS provides computational secrecy. It also provides asymptotic perfect secrecy for Gaussian sensing matrix with constraints on input signal. In this paper, we design an attack decoding algorithm based on block compressed sensing decoding algorithm to perform ciphertext-only attack on real-life time series IoT data. It shows that it is possible to retrieve vital information in the plaintext under some conditions. Furthermore, it is also applied to a State-of-the Art CS-based encryption scheme for smart grid, and the power profile is reconstructed using ciphertext-only attack. Additionally, the statistical analysis of Gaussian and Binomial measurements is conducted to investigate the randomness provided by them.

The paper proposes a new measurement method for impairments identification in wireline channels (i.e. wire cables) by exploiting a Compressive Sampling (CS)-based technique. The method consists of two-phases: (i) acquisition and reconstruction of the channel impulse response in the nominal working condition and (ii) analysis of the channel state to detect any physical anomaly/discontinuity like deterioration (e.g. aging due to harsh environment) or unauthorized side channel attacks (e.g. taps). The first results demonstrate that the proposed method is capable of estimating the channel impairments with an accuracy that could allow the classification of the main channel impairments. The proposed method could be used to develop low-cost instrumentation for continuous monitoring of the physical layer of data networks and to improve their hardware security.

Sybils are users created for carrying out nefarious actions in online social networks (OSNs) and threaten the security of OSNs. Therefore, Sybil detection is an urgent security task, and various detection methods have been proposed. Existing Sybil detection methods are based on the relationship (i.e., graph structure) of users in OSNs. Structure-based methods can be classified into two categories: Random Walk (RW)-based and Belief Propagation (BP)-based. However, although almost all methods have been experimentally evaluated in terms of their performance and robustness to noise, the theoretical understanding of them is insufficient. In this paper, we interpret the Sybil detection problem from the viewpoint of graph signal processing and provide a framework to formulate RW- and BPbased methods as low-pass filtering. This framework enables us to theoretically compare RW- and BP-based methods and explain why BP-based methods perform well for scale-free graphs, unlike RW-based methods. Furthermore, by this framework, we relate RW- and BP-based methods and Graph Neural Networks (GNNs) and discuss the difference among these methods. Finally, we evaluate the validity of this framework through numerical experiments.

For hours-ahead assessment of power systems with a high penetration level of renewable generation, a large number of uncertain scenarios should be checked to ensure the frequency security of the system after the severe power disturbance following HVDC blocking. In this situation, the full time-domain simulation is unsuitable as a result of the heavy calculation burden. To fulfill the quick assessment of the frequency security, the online frequency security assessment framework based on deep learning is proposed in this paper. The Deep Belief Network (DBN) method is used to establish the framework. The sample generation method is researched to generate representative samples for the purposed of higher assessment accuracy. A large-scale AC-DC interconnected power grid is adopted to verify the validity of the proposed assessment method.

Attack graphs are commonly used to analyse the security of medium-sized to large networks. Based on a scan of the network and likelihood information of vulnerabilities, attack graphs can be transformed into Bayesian Attack Graphs (BAGs). These BAGs are used to evaluate how security controls affect a network and how changes in topology affect security. A challenge with these automatically generated BAGs is that cycles arise naturally, which make it impossible to use Bayesian network theory to calculate state probabilities. In this paper we provide a systematic approach to analyse and perform computations over cyclic Bayesian attack graphs. We present an interpretation of Bayesian attack graphs based on combinational logic circuits, which facilitates an intuitively attractive systematic treatment of cycles. We prove properties of the associated logic circuit and present an algorithm that computes state probabilities without altering the attack graphs (e.g., remove an arc to remove a cycle). Moreover, our algorithm deals seamlessly with any cycle without the need to identify their type. A set of experiments demonstrates the scalability of the algorithm on computer networks with hundreds of machines, each with multiple vulnerabilities.

In this paper, we propose a decision support process that is designed to help network and security operators in understanding the complexity of a current security situation and decision making concerning ongoing cyber-attacks and threats. The process focuses on enterprise missions and uses a graph-based mission decomposition model that captures the missions, underlying hosts and services in the network, and functional and security requirements between them. Knowing the vulnerabilities and attacker's position in the network, the process employs logical attack graphs and Bayesian network to infer the probability of the disruption of the confidentiality, integrity, and availability of the missions. Based on the probabilities of disruptions, the process suggests the most resilient mission configuration that would withstand the current security situation.

Well-known approaches to risk analysis suggest considering the level of an information system risk as one frame in a film. This means that we only can perform a risk assessment for the current point in time. This article explores the idea of risk assessment in a future period, as a prediction of what we will see in the film later. In other words, the article presents an approach to predicting a potential future risk and suggests the idea of relying on forecasting the likelihood of an attack on information system assets. To establish the risk level at a selected time interval in the future, one has to perform a mathematical decomposition. To do this, we need to select the required information system parameters for the predictions and their statistical data for risk assessment. This method can be used to ensure more detailed budget planning when ensuring the protection of the information system. It can be also applied in case of a change of the information protection configuration to satisfy the accepted level of risk associated with projected threats and vulnerabilities.

Spiking Neural Networks (SNNs) claim to present many advantages in terms of biological plausibility and energy efficiency compared to standard Deep Neural Networks (DNNs). Recent works have shown that DNNs are vulnerable to adversarial attacks, i.e., small perturbations added to the input data can lead to targeted or random misclassifications. In this paper, we aim at investigating the key research question: "Are SNNs secure?" Towards this, we perform a comparative study of the security vulnerabilities in SNNs and DNNs w.r.t. the adversarial noise. Afterwards, we propose a novel black-box attack methodology, i.e., without the knowledge of the internal structure of the SNN, which employs a greedy heuristic to automatically generate imperceptible and robust adversarial examples (i.e., attack images) for the given SNN. We perform an in-depth evaluation for a Spiking Deep Belief Network (SDBN) and a DNN having the same number of layers and neurons (to obtain a fair comparison), in order to study the efficiency of our methodology and to understand the differences between SNNs and DNNs w.r.t. the adversarial examples. Our work opens new avenues of research towards the robustness of the SNNs, considering their similarities to the human brain's functionality.

There are a lot of emerging security equipment required to be tested on detection rate (DR) and false alarm rate (FAR) for prohibited items. This article imports Bayesian approach to accept or reject DR and FAR. The detailed quantitative predictions can be made through the posterior distribution obtained by Markov chain Monte Carlo method. Based on this, HDI + ROPE decision rule is established. For the tests that need to make early decision, HDI + ROPE stopping rule is presented with biased estimate value, and criterial precision rule is presented with unbiased estimate value. Choosing the stopping rule according to the test purpose can achieve the balance of efficiency and accuracy.