Biblio

Measuring the information leakage is critical for evaluating the practical security of cryptographic devices against side-channel analysis. Information-theoretic measures can be used (along with Fano’s inequality) to derive upper bounds on the success rate of any possible attack in terms of the number of side-channel measurements. Equivalently, this gives lower bounds on the number of queries for a given success probability of attack. In this paper, we consider cryptographic implementations protected by (first-order) masking schemes, and derive several information-theoretic bounds on the efficiency of any (second-order) attack. The obtained bounds are generic in that they do not depend on a specific attack but only on the leakage and masking models, through the mutual information between side-channel measurements and the secret key. Numerical evaluations confirm that our bounds reflect the practical performance of optimal maximum likelihood attacks.

Recently, it has been proposed to deal with fusion of multi-object densities exploiting the minimum information loss (MIL) rule, which has shown its superiority over generalized covariance intersection (GCI) fusion whenever sensor nodes have low detection probability. On the contrary, GCI shows better performance than MIL when dense clutter is involved in the measurements. In this paper, we are going to study the behavior of multi-object fusion with MIL and, respectively, GCI rules in the situation wherein the sensor network is exposed to cyber-attacks. Both theoretical and numerical analyses demonstrate that MIL is more robust than GCI fusion when the multi-sensor system is subject to a packet substitution attack.

This paper presents a study on the "Dynamic Load Altering Attacks" (D-LAAs), their effects on the dynamics of a transmission network, and provides a robust control protection scheme, based on polytopic uncertainties, invariance theory, Lyapunov arguments and graph theory. The proposed algorithm returns an optimal Energy Storage Systems (ESSs) placement, that minimizes the number of ESSs placed in the network, together with the associated control law that can robustly stabilize against D-LAAs. The paper provides a contextualization of the problem and a modelling approach for power networks subject to D-LAAs, suitable for the designed robust control protection scheme. The paper also proposes a reference scenario for the study of the dynamics of the control actions and their effects in different cases. The approach is evaluated by numerical simulations on large networks.

In this paper, we study an underlay Cognitive Radio (CR) system with energy harvesting over Nakagami-m fading channel. This system consists of a secondary source, a secondary receiver, a primary receiver and a single eavesdropper. The source in the secondary network has one antenna and transmits information to the secondary receiver equipped with two separated antennas to operate in a Full-Duplex (FD) mode. The upper and lower bounds for the Strictly Positive Secrecy Capacity (SPSC) are derived and the numerical results demonstrate that the performance of the proposed system can be improved by increasing the average channel power gain between the source and the destination. Here, the lower and upper bounds are merged to form the exact SPSC when the total interference is below a predefined limit.

A considerable portion of computing power is always required to perform symbolic calculations. The reliability and effectiveness of algorithms are two of the most significant challenges observed in the field of scientific computing. The terms “feasible calculations” and “feasible computations” refer to the same idea: the algorithms that are reliable and effective despite practical constraints. This research study intends to investigate different types of computing and modelling challenges, as well as the development of efficient integration methods by considering the challenges before generating the accurate results. Further, this study investigates various forms of errors that occur in the process of data integration. The proposed framework is based on automata, which provides the ability to investigate a wide-variety of distinct distance-bounding protocols. The proposed framework is not only possible to produce computational (in)security proofs, but also includes an extensive investigation on different issues such as optimal space complexity trade-offs. The proposed framework in embedded with the already established symbolic framework in order to get a deeper understanding of distance-bound security. It is now possible to guarantee a certain level of physical proximity without having to continually mimic either time or distance.

For the huge charging demands of numerous electric vehicles (EVs), coordinated charging is increasing in power grid. However, since connected with public networks, the coordinated charging control system is in a low-level cyber security and greatly vulnerable to malicious attacks. This paper investigates the malicious mode attack (MMA), which is a new cyber-attack pattern that simultaneously attacks massive EV charging piles to generate continuous sinusoidal power disturbance with the same frequency as the poorly-damped wide-area electromechanical mode. Thereby, high amplitude forced oscillations are stimulated by MMA, which seriously threats the stability of power systems and the power supply of charging stations. The potential threat of MMA is clarified by investigating the vulnerability of the IoT-based coordinated charging load control system, and an MMA process like Mirai is pointed out as an example. An MMA model is established for impact analysis. A hardware test platform is built for the verification of the MMA model. Test result verified the existence of MMA and the accuracy of the MMA model.

Common Vulnerability Scoring System (CVSS) is intended to capture the key characteristics of a vulnerability and correspondingly produce a numerical score to indicate the severity. Important efforts are conducted for building a CVSS stochastic model in order to provide a high-level risk assessment to better support cybersecurity decision-making. However, these efforts consider nothing regarding HPC (High-Performance Computing) networks using a Science Demilitary Zone (DMZ) architecture that has special design principles to facilitate data transition, analysis, and store through in a broadband backbone. In this paper, an HPCvul (CVSS-based vulnerability and risk assessment) approach is proposed for HPC networks in order to provide an understanding of the ongoing awareness of the HPC security situation under a dynamic cybersecurity environment. For such a purpose, HPCvul advocates the standardization of the collected security-related data from the network to achieve data portability. HPCvul adopts an attack graph to model the likelihood of successful exploitation of a vulnerability. It is able to merge multiple attack graphs from different HPC subnets to yield a full picture of a large HPC network. Substantial results are presented in this work to demonstrate HPCvul design and its performance.

An accurate state estimation (SE) considering increased uncertainty by the high penetration of renewable energy systems (RESs) is more and more important to enhance situational awareness, and the optimal and resilient operation of the renewable-rich power grids. However, it is anticipated that adversaries who plan to manipulate the target power grid will generate attacks that inject inaccurate data to the SE using the vulnerabilities of the devices and networks. Among potential attack types, false data injection attack (FDIA) is gaining popularity since this can bypass bad data detection (BDD) methods implemented in the SE systems. Although numerous FDIA detection methods have been recently proposed, the uncertainty of system configuration that arises by the continuously increasing penetration of RESs has been been given less consideration in the FDIA algorithms. To address this issue, this paper proposes a new FDIA detection scheme that is applicable to renewable energy-rich power grids. A deep learning framework is developed in particular by synergistically constructing a Bidirectional Long Short-Term Memory (Bi-LSTM) with modern smart grid characteristics. The developed framework is evaluated on the IEEE 14-bus system integrating several RESs by using several attack scenarios. A comparison of the numerical results shows that the proposed FDIA detection mechanism outperforms the existing deep learning-based approaches in a renewable energy-rich grid environment.

To achieve secure uplink communication from smartphones’ screen to a telephoto camera at a long distance of 3.5 meters, we demonstrate that low-luminance space division multiplexing screen is effective in enhancement of the physical layer security. First, a numerical model shows that the spatial inter-symbol interference caused by space division multiplexing prevents eavesdropping from a wide angle by the camera. Second, wide-angle characteristics of the symbol error rate and the pixel value distribution are measured to verify the numerical analysis. We experimentally evaluate the difference in the performances from a wide angle depending on the screen luminance and color. We also evaluate the performances at a long distance in front of the screen and a short distance from a wider angle.

SUMMARY & CONCLUSIONSA Multi-access Edge Computing (MEC) micro data center (MEDC) consists of multiple MEC hosts close to endpoint devices. MEC service is delivered by instantiating a virtualization system (e.g., Virtual Machines or Containers) on a MEC host. MEDC faces more new security risks due to various device connections in an open environment. When more and more IoT/CPS systems are connected to MEDC, it is necessary for MEC service providers to quantitatively analyze any security loss and then make defense-related decision. This paper develops a CTMC model for quantitatively analyzing the security and dependability of a vulnerable MEDC system under lateral movement attacks, from the adversary’s initial successful access until the MEDC becomes resistant to the attack. The proposed model captures the behavior of the system in a scenario where (i) the rate of vulnerable MEC servers being infected increases with the increasing number of infected MEC servers, (ii) each infected MEC server can perform its compromising activity independently and randomly, and (iii) any infected MEC may fail and then cannot provide service. We also introduce the formulas for computing metrics. The proposed model and formula are verified to be approximately accurate by comparing numerical results and simulation results.

In the Bitcoin mining network, miners contribute computation power to solve crypto-puzzles in exchange for financial rewards. Due to the randomness and the competitiveness of mining, individual miners tend to join mining pools for low risks and steady incomes. Usually, a pool is managed by its central operator, who charges fees for providing risk-sharing services. This paper presents a hierarchical distributed computation paradigm where miners can distribute their power among multiple pools. By adding virtual pools, we separate miners’ dual roles of being the operator as well as being the member when solo mining. We formulate a multi-leader multi-follower Stackelberg game to study the joint utility maximization of pool operators and miners, thereby addressing a computation power allocation problem. We investigate two practical pool operation modes, a uniform-share-difficulty mode and a nonuniform-share-difficulty mode. We derive analytical results for the Stackelberg equilibrium of the game under both modes, based on which optimal strategies are designed for all operators and miners. Numerical evaluations are presented to verify the proposed model.

Differential evolution algorithm is an efficient computational method that uses population crossover and variation to achieve high-quality solutions. The algorithm is simple in principle and fast in solving global solutions, so it has been widely used in complex optimization problems. In this paper, we applied the differential evolution algorithm to a time-delay dynamic system for microbial fermentation of 1,3-propanediol and obtained an average error of 22.67% comparing to baseline error of 48.53%.

This paper is devoted to boundary control design for urban traffic described on a macroscopic scale. The state corresponds to vehicle density that evolves on a continuum two-dimensional domain that represents a continuous approximation of a urban network. Its parameters are interpolated as a function of distance to physical roads. The dynamics are governed by a new macroscopic multi-directional traffic model that encompasses a system of four coupled partial differential equations (PDE) each describing density evolution in one direction layer: North, East, West and South (NEWS). We analyse the class of desired states that the density governed by NEWS model can achieve. Then a boundary control is designed to drive congested traffic to an equilibrium with the minimal congestion level. The result is validated numerically using the real structure of Grenoble downtown (a city in France).

This paper considers a multivariate quickest detection problem with false data injection (FDI) attacks in internet of things (IoT) systems. We derive a sequential generalized likelihood ratio test (GLRT) for zero-mean Gaussian FDI attacks. Exploiting the fact that covariance matrices are positive, we propose strategies to detect positive semi-definite matrix additions rather than arbitrary changes in the covariance matrix. The distribution of the GLRT is only known asymptotically whereas quickest detectors deal with short sequences, thereby leading to loss of performance. Therefore, we use a finite-sample correction to reduce the false alarm rate. Further, we provide a numerical approach to estimate the threshold sequences, which are analytically intractable to compute. We also compare the average detection delay of the proposed detector for constant and varying threshold sequences. Simulations showed that the proposed detector outperforms the standard sequential GLRT detector.

Multimedia Internet of Things (IoT) devices, especially, the smartphones are embedded with sensors including Global Positioning System (GPS), barometer, microphone, accelerometer, etc. These sensors working together, present a fairly complete picture of the citizens' daily activities, with implications for their privacy. With the internet, Citizens in Smart Cities are able to perform their daily life activities online with their connected electronic devices. But, unfortunately, computer hackers tend to write automated malicious applications to attack websites on which these citizens perform their activities. These security threats sometime put their private information at risk. In order to prevent these security threats on websites, Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHAs) are generated, as a form of security mechanism to protect the citizens' private information. But with the advancement of deep learning, text-based CAPTCHAs can sometimes be vulnerable. As a result, it is essential to conduct performance evaluation on the CAPTCHAs that are generated before they are deployed on multimedia web applications. Therefore, this work proposed an optimal and light-weight Convolutional Neural Network (CNN) to solve both numerical and alpha-numerical complex text-based CAPTCHAs simultaneously. The accuracy of the proposed CNN model has been accelerated based on Cyclical Learning Rates (CLRs) policy. The proposed CLR-CNN model achieved a high accuracy to solve both numerical and alpha-numerical text-based CAPTCHAs of 99.87% and 99.66%, respectively. In real-time, we observed that the speed of the model has increased, the model is lightweight, stable, and flexible as compared to other CAPTCHA solving techniques. The result of this current work will increase awareness and will assist multimedia security Researchers to continue and develop more robust text-based CAPTCHAs with their security mechanisms capable of protecting the private information of citizens in Smart Cities.

Cyber deception plays an important role in both proactive and reactive defense systems. Internet of Battlefield things connecting smart devices of any military tactical network is of great importance. The goal of cyber deception is to provide false information regarding the network state, and topology to protect the IoBT's network devices. In this paper, we propose a novel deceptive approach based on game theory that takes into account the topological aspects of the network and the criticality of each device. To find the optimal deceptive strategy, we formulate a two-player game to study the interactions between the network defender and the adversary. The Nash equilibrium of the game model is characterized. Moreover, we propose a scalable game-solving algorithm to overcome the curse of dimensionality. This approach is based on solving a smaller in-size subgame per node. Our numerical results show that the proposed deception approach effectively reduced the impact and the reward of the attacker

Wireless communications networks are an integral part of intelligent systems that enhance the automation of various activities and operations embarked by humans. For example, the development of intelligent devices imbued with sensors leverages emerging technologies such as machine learning (ML) and artificial intelligence (AI), which have proven to enhance military operations through communication, control, intelligence gathering, and situational awareness. However, growing concerns in cybersecurity imply that attackers are always seeking to take advantage of the widened attack surface to launch adversarial attacks which compromise the activities of legitimate users. To address this challenge, we leverage on deep learning (DL) and the principle of cyber-deception to propose a method for defending wireless networks from the activities of jammers. Specifically, we use DL to regulate the power allocated to users and the channel they use to communicate, thereby luring jammers into attacking designated channels that are considered to guarantee maximum damage when attacked. Furthermore, by directing its energy towards the attack on a specific channel, other channels are freed up for actual transmission, ensuring secure communication. Through simulations and experiments carried out, we conclude that this approach enhances security in wireless communication systems.

Various algorithms and models have been proposed to address text classification tasks; however, they rarely consider incorporating the additional knowledge hidden in class labels. We argue that hidden information in class labels leads to better classification accuracy. In this study, instead of encoding the labels into numerical values, we incorporated the knowledge in the labels into the original model without changing the model architecture. We combined the output of an original classification model with the relatedness calculated based on the embeddings of a sequence and a keyword set. A keyword set is a word set to represent knowledge in the labels. Usually, it is generated from the classes while it could also be customized by the users. The experimental results show that our proposed method achieved statistically significant improvements in text classification tasks. The source code and experimental details of this study can be found on Github11https://github.com/HeroadZ/KiL.

Accidents in Nuclear Power Plants (NPPs) can occur for a variety of causes. However, among these, the scale of accidents due to human error can be greater than expected. Accordingly, researches are being actively conducted using artificial intelligence to reduce human error. Most of the research shows high performance based on the numerical data on NPPs, but the expandability of researches using only numerical data is limited. Therefore, in this study, abnormal diagnosis was performed using artificial intelligence based on image data. The methods applied to abnormal diagnosis are the deep neural network, convolution neural network, and convolution recurrent neural network. Consequently, in nuclear power plants, it is expected that the application of more methodologies can be expanded not only in numerical data but also in image-based data.

This paper presents a secure reinforcement learning (RL) based control method for unknown linear time-invariant cyber-physical systems (CPSs) that are subjected to compositional attacks such as eavesdropping and covert attack. We consider the attack scenario where the attacker learns about the dynamic model during the exploration phase of the learning conducted by the designer to learn a linear quadratic regulator (LQR), and thereafter, use such information to conduct a covert attack on the dynamic system, which we refer to as doubly learning-based control and attack (DLCA) framework. We propose a dynamic camouflaging based attack-resilient reinforcement learning (ARRL) algorithm which can learn the desired optimal controller for the dynamic system, and at the same time, can inject sufficient misinformation in the estimation of system dynamics by the attacker. The algorithm is accompanied by theoretical guarantees and extensive numerical experiments on a consensus multi-agent system and on a benchmark power grid model.

Swarm intelligence, a nature-inspired concept that includes multiplicity, stochasticity, randomness, and messiness is emergent in most real-life problem-solving. The concept of swarming can be integrated with herding predators in an ecological system. This paper presents the development of stabilizing velocity-based controllers for a Lagrangian swarm of \$nın \textbackslashtextbackslashmathbbN\$ individuals, which are supposed to capture a moving target (intruder). The controllers are developed from a Lyapunov function, total potentials, designed via Lyapunov-based control scheme (LbCS) falling under the classical approach of artificial potential fields method. The interplay of the three central pillars of LbCS, which are safety, shortness, and smoothest course for motion planning, results in cost and time effectiveness and efficiency of velocity controllers. Computer simulations illustrate the effectiveness of control laws.

Securing communication between any two wireless devices or users is challenging without compromising sensitive/personal data. To address this problem, we have developed an artificial intelligence (AI) algorithm to secure communication on virtualized wireless networks. To detect cyberattacks in a virtualized environment is challenging compared to traditional wireless networks setting. However, we successfully investigate an efficient cyberattack detection algorithm using an AI algorithm in a Bayesian learning model for detecting cyberattacks on the fly. We have studied the results of Random Forest and deep neural network (DNN) models to detect the cyberattacks on a virtualized wireless network, having considered the required transmission power as a threshold value to classify suspicious activities in our model. We present both formal mathematical analysis and numerical results to support our claims. The numerical results show our accuracy in detecting cyberattacks in the proposed Bayesian model is better than Random Forest and DNN models. We have also compared both models in terms of detection errors. The performance comparison results show our proposed approach outperforms existing approaches in detection accuracy, precision, and recall.

The objective of this work is to study, using an analytical approach and a numerical simulation, the dynamic behavior of an oscillating liquid inside a fixed U-tube with open ends used as wave energy converter. By establishing a detailed liquid's motion equation and developing a numerical simulation, based on volume of fluid formulation, we quantified the available power that could be extracted for our configuration. A parametrical study using the analytical model showed the effect of each significant parameter on first peak power and subsequent dampening of this peak power, which constitutes a tool for choosing optimal designs. The numerical simulation gave a more realistic model, the obtained results are in good agreements with those of the analytical approach that underestimates the dampening of oscillations. We focused after on influence of the numerical model formulation, mesh type and mesh size on simulation results: no noticeable effect was observed.

Tor is a famous routing overlay network based on the Onion multi-layered encryption to support communication anonymity in a threat model in which some network nodes are malicious. However, Tor does not provide any protection against the global passive adversary. In this threat model, an idea to obtain recipient anonymity, which is enough to have relationship anonymity, is to hide the recipient among a sufficiently large anonymity set. However, this would lead to high latency both in the set-up phase (which has a quadratic cost in the number of involved nodes) and in the successive communication. In this paper, we propose a way to arrange a Tor circuit with a tree-like topology, in which the anonymity set consists of all its nodes, whereas set-up and communication latency depends on the number of the sole branch nodes (which is a small fraction of all the nodes). Basically, the cost goes down from quadratic to linear. Anonymity is obtained by applying a broadcast-based technique for the forward message, and cover traffic (generated by the terminal-chain nodes) plus mixing over branch nodes, for the response.

In integrated gas and electricity energy systems, a catastrophic outage in one system could propagate to other, resulting in severe service interruption like what happened in 2021 Texas Blackout. To alleviate detrimental effects of these events, a coordinated effort must be adopted between integrated energy systems. In this paper, a central situational awareness system (CSAS) is developed to improve the coordination of operational resiliency measures by facilitating information sharing between power distribution systems (PDSs) and natural gas networks (NGNs) during emergency conditions. The CSAS collects operational data of the PDS and the NGN as well as data of upcoming weather condition, extracts the most vulnerable lines and pipelines, and accordingly obtains emergency actions. The emergency actions, i.e., optimal multi-microgrid formation, scheduling of distribution energy resources (DERs), and optimal electrical and gas load shedding plan, are optimized through a coupled graph-based approach with stochastic mixed integer linear programming (MILP) model. In the proposed model, uncertainties of renewable energy resources (RESs) is also considered. Numerical results on an integrated IEEE 33-bus and 30-node NGNs demonstrate the effectiveness of proposed CSAS.