Biblio

We investigate the potential of an information-spectrum approach to information-theoretic security. We show how this approach provides conceptually simple yet powerful results that can be used to investigate complex communication scenarios. In particular, we illustrate the usefulness of information-spectrum methods by analyzing the effect of channel state information (CSI) on the secure rates achievable over wiretap channels. We establish a formula for secrecy capacity, which we then specialize to compute achievable rates for ergodic fading channels in the presence of imperfect CSI. Our results confirm the importance of having some knowledge about the eavesdropper's channel, but also show that imperfect CSI does not necessarily preclude security.

A time-delay switch (TDS) cyber attack is a deliberate attempt by malicious adversaries aiming at destabilizing a power system by impeding the communication signals to/from the centralized controller from/to the network sensors and generating stations that participate in the load frequency control (LFC). A TDS cyber attack can be targeting the sensing loops (transmitting network measurements to the centralized controller) or the control signals dispatched from the centralized controller to the governor valves of the generating stations. A resilient TDS control strategy is proposed and developed in this work that thwarts network instabilities that are caused by delays in the sensing loops, and control commands, and guarantees normal operation of the LFC mechanism. This will be achieved by augmenting the telemetered control commands with locally generated feedback control laws (i.e., “decentralized” control commands) taking measurements that are available and accessible at the power generating stations (locally) independent from all the telemetered signals to/from the centralized controller. Our objective is to devise a controller that is capable of circumventing all types of TDS and DoS (Denial of Service) cyber attacks as well as a broad class of False Data Injection (FDI) cyber attacks.

This paper describes a method for classifying onion sites. According to the results of the research, the most spread model of site in onion space is built. To create such a model, a specially trained neural network is used. The classification of neural network is based on five different categories such as using authentication system, corporate email, readable URL, feedback and type of onion-site. The statistics of the most spread types of websites in Dark Net are given.

Continuous monitoring and risk assessment of privacy violations on cloud systems are needed by anyone who has business needs subject to privacy regulations. Compliance to such regulations in dynamic systems demands appropriate techniques, tools and instruments. As a Service concepts can be a good option to support this task. Previous work presented PRIVAaaS, a software toolkit that allows controlling and reducing data leakages, thus preserving privacy, by providing anonymization capabilities to query-based systems. This short paper discusses the implementation details and deployment environment of an evolution of PRIVAaaS as a MAPE-K control loop within the ATMOSPHERE Platform. ATMOSPHERE is both a framework and a platform enabling the implementation of trustworthy cloud services. By enabling PRIVAaaS within ATMOSPHERE, privacy is made one of several trustworthiness properties continuously monitored and assessed by the platform with a software-based, feedback control loop known as MAPE-K.

In this paper, we propose a robust Nash strategy for a class of uncertain Markov jump delay stochastic systems (UMJDSSs) via static output feedback (SOF). After establishing the extended bounded real lemma for UMJDSS, the conditions for the existence of a robust Nash strategy set are determined by means of cross coupled stochastic matrix inequalities (CCSMIs). In order to solve the SOF problem, an heuristic algorithm is developed based on the algebraic equations and the linear matrix inequalities (LMIs). In particular, it is shown that robust convergence is guaranteed under a new convergence condition. Finally, a practical numerical example based on the congestion control for active queue management is provided to demonstrate the reliability and usefulness of the proposed design scheme.

With the recent advances in computing, artificial intelligence (AI) is quickly becoming a key component in the future of advanced applications. In one application in particular, AI has played a major role - that of revolutionizing traditional healthcare assistance. Using embodied interactive agents, or interactive robots, in healthcare scenarios has emerged as an innovative way to interact with patients. As an essential factor for interpersonal interaction, trust plays a crucial role in establishing and maintaining a patient-agent relationship. In this paper, we discuss a study related to healthcare in which we examine aspects of trust between humans and interactive robots during a therapy intervention in which the agent provides corrective feedback. A total of twenty participants were randomly assigned to receive corrective feedback from either a robotic agent or a human agent. Survey results indicate trust in a therapy intervention coupled with a robotic agent is comparable to that of trust in an intervention coupled with a human agent. Results also show a trend that the agent condition has a medium-sized effect on trust. In addition, we found that participants in the robot therapist condition are 3.5 times likely to have trust involved in their decision than the participants in the human therapist condition. These results indicate that the deployment of interactive robot agents in healthcare scenarios has the potential to maintain quality of health for future generations.

Data dropouts in communication network can have a significant impact on wide-area oscillation damping control of a smart power grid with large-scale deployment of distributed and networked phasor measurement units and wind energy resources. Remote feedback signals sent through communication channels encounter data dropout, which is represented by the Gilbert-Elliott model. An observer-driven reduced copy (ORC) approach is presented, which uses the knowledge of the nominal system dynamics during data dropouts to improve the damping performance where conventional feedback would suffer. An expression for the expectation of the bound on the error norm between the actual and the estimated states relating uncertainties in the cyber system due to data dropout and physical system due to change in operating conditions is also derived. The key contribution comes from the analytical derivation of the impact of coupling between the cyber and the physical layer on ORC performance. Monte Carlo simulation is performed to calculate the dispersion of the error bound. Nonlinear time-domain simulations demonstrate that the ORC produces significantly better performance compared to conventional feedback under higher data drop situations.

We consider a compositional construction of approximate abstractions of interconnected control systems. In our framework, an abstraction acts as a substitute in the controller design process and is itself a continuous control system. The abstraction is related to the concrete control system via a so-called simulation function: a Lyapunov-like function, which is used to establish a quantitative bound between the behavior of the approximate abstraction and the concrete system. In the first part of the paper, we provide a small gain type condition that facilitates the compositional construction of an abstraction of an interconnected control system together with a simulation function from the abstractions and simulation functions of the individual subsystems. In the second part of the paper, we restrict our attention to linear control system and characterize simulation functions in terms of controlled invariant, externally stabilizable subspaces. Based on those characterizations, we propose a particular scheme to construct abstractions for linear control systems. We illustrate the compositional construction of an abstraction on an interconnected system consisting of four linear subsystems. We use the abstraction as a substitute to synthesize a controller to enforce a certain linear temporal logic specification.

Information communicated within cyber-physical systems (CPSs) is often used in determining the physical states of such systems, and malicious adversaries may intercept these communications in order to infer future states of a CPS or its components. Accordingly, there arises a need to protect the state values of a system. Recently, the notion of differential privacy has been used to protect state trajectories in dynamical systems, and it is this notion of privacy that we use here to protect the state trajectories of CPSs. We incorporate a cloud computer to coordinate the agents comprising the CPSs of interest, and the cloud offers the ability to remotely coordinate many agents, rapidly perform computations, and broadcast the results, making it a natural fit for systems with many interacting agents or components. Striving for broad applicability, we solve infinite-horizon linear-quadratic-regulator (LQR) problems, and each agent protects its own state trajectory by adding noise to its states before they are sent to the cloud. The cloud then uses these state values to generate optimal inputs for the agents. As a result, private data are fed into feedback loops at each iteration, and each noisy term affects every future state of every agent. In this paper, we show that the differentially private LQR problem can be related to the well-studied linear-quadratic-Gaussian (LQG) problem, and we provide bounds on how agents' privacy requirements affect the cloud's ability to generate optimal feedback control values for the agents. These results are illustrated in numerical simulations.

Bluetooth enables excellent mobility in Brain Computer Interface (BCI) research and other use cases including ambulatory care, telemedicine, fitness tracking and mindfulness training. Although significant research exists for an all-encompassing BCI performance rating, almost all the literature addresses performance in terms of brain state or brain function classification accuracy. For the few published experiments that address BCI hardware performance, they too, focused on improving classification accuracy. This paper explores some of the more recent studies and proposes a trusted performance rating for BCI applications based on the enhanced privacy, yet reduced bandwidth needs of mobile EEG-based BCI applications. This paper proposes a set of Bluetooth operating parameters required to meet the performance, usability and privacy requirements of reliable and secure mobile neuro-feedback applications. It presents a rating model, "Trusted Mobile BCI", based on those operating parameters, and validated the model with studies that leveraged mobile BCI technology.

Cyber-Physical Systems (CPS) are playing important roles in the critical infrastructure now. A prominent family of CPSs are networked control systems in which the control and feedback signals are carried over computer networks like the Internet. Communication over insecure networks make system vulnerable to cyber attacks. In this article, we design an intrusion detection and compensation framework based on system/plant identification to fight covert attacks. We collect error statistics of the output estimation during the learning phase of system operation and after that, monitor the system behavior to see if it significantly deviates from the expected outputs. A compensating controller is further designed to intervene and replace the classic controller once the attack is detected. The proposed model is tested on a DC motor as the plant and is put against a deception signal amplification attack over the forward link. Simulation results show that the detection algorithm well detects the intrusion and the compensator is also successful in alleviating the attack effects.

In the article the problem of survivability evaluation of control systems is considered. Control system is presented as a graph with edges that formalize minimal control systems consist of receiver, transmitter and a communication line connecting them. Based on the assumption that the survivability of minimal control systems is known, the mathematical model of survivability evaluation of not minimal control systems based on fuzzy logic is offered.

In a diagnosability verifier with polynomial complexity, a non-diagnosable system generates uncertain loops. Such forbidden loops are in this paper transformed to forbidden states by simple detector automata. The forbidden state problem is trivially transformed to a nonblocking problem by considering all states except the forbidden ones as marked states. This transformation is combined with one of the most efficient abstractions for modular systems called conflict equivalence, where nonblocking properties are preserved. In the resulting abstraction, local events are hidden and more local events are achieved when subsystems are synchronized. This incremental abstraction is applied to a scalable production system, including parallel lines where buffers and machines in each line include some typical failures and feedback flows. For this modular system, the proposed diagnosability algorithm shows great results, where diagnosability of systems including millions of states is analyzed in less than a second.

In this paper, We propose DNA sticker model based algorithm, a computability model, which is a simulation of the parallel computations using the Molecular computing as in Adelman's DNA computing experiment, it demonstrates how to use a sticker-based model to design a simple DNA-based algorithm for attacking a linear and a non-linear feedback shift register (FSR) based stream cipher. The algorithm first construct the TEST TUBE contains all overall solution space of memory complexes for the cipher and initials of registers via the sticker-based model. Then, with biological operations, separate and combine, we remove those which encode illegal plain and key stream from the TEST TUBE of memory complexes, the decision based on verifying a key stream bit this bit represented by output of LFSRs equation. The model anticipates two basic groups of single stranded DNA molecules in its representation one of a genetic bases and second of a bit string, It invests parallel search into the space of solutions through the possibilities of DNA computing and makes use of the method of cryptanalysis of algebraic code as a decision technique to accept the solution or not, and their operations are repeated until one solution or limited group of solutions is reached. The main advantages of the suggested algorithm are limited number of cipher characters, and finding one exact solution The present work concentrates on showing the applicability of DNA computing concepts as a powerful tool in breaking cryptographic systems.

Abstract Despite decades of research into developing abstract security advice and improving interfaces, users still struggle to make passwords. Users frequently create passwords that are predictable for attackers [1, 9] or make other decisions (e.g., reusing the same password across accounts) that harm their security [2, 8]. In this thesis,1 I use data-driven methods to better understand how users choose passwords and how attackers guess passwords. I then combine these insights into a better password-strength meter that provides real-time, data-driven feedback about the user's password. I first quantify the impact on password security and usability of showing users different password-strength meters that score passwords using basic heuristics. I find in a 2,931- participant online study that meters that score passwords stringently and present their strength estimates visually lead users to create stronger passwords without significantly impacting password memorability [6]. Second, to better understand how attackers guess passwords, I perform comprehensive experiments on password-cracking approaches. I find that simply running these approaches in their default configuration is insufficient, but considering multiple well-configured approaches in parallel can serve as a proxy for guessing by an expert in password forensics [9]. The third and fourth sections of this thesis delve further into how users choose passwords. Through a series of analyses, I pinpoint ways in which users structure semantically significant content in their passwords [7]. I also examine the relationship between users' perceptions of password security and passwords' actual security, finding that while users often correctly judge the security impact of individual password characteristics, wide variance in their understanding of attackers may lead users to judge predictable passwords as sufficiently strong [5]. Finally, I integrate these insights into an open-source2 password-strength meter that gives users data-driven feedback about their specific password. This meter uses neural networks [3] and numerous carefully combined heuristics to score passwords and generate data-driven text feedback about a given password. I evaluate this meter through a ten-participant laboratory study and 4,509-participant online study [4]. Under the more common password-composition policy we tested, we find that the data-driven meter with detailed feedback leads users to create more secure, and no less memorable, passwords than a meter with only a bar as a strength indicator. In sum, the objective of this thesis is to demonstrate how integrating data-driven insights about how users create and how attackers guess passwords into a tool that presents real-time feedback can help users make better passwords.

As opposed to a traditional power grid, a smart grid can help utilities to save energy and therefore reduce the cost of operation. It also increases reliability of the system In smart grids the quality of monitoring and control can be adequately improved by incorporating computing and intelligent communication knowledge. However, this exposes the system to false data injection (FDI) attacks and the system becomes vulnerable to intrusions. Therefore, it is important to detect such false data injection attacks and provide an algorithm for the protection of system against such attacks. In this paper a comparison between three FDI detection methods has been made. An H2 control method has then been proposed to detect and control the false data injection on a 12th order model of a smart grid. Disturbances and uncertainties were added to the system and the results show the system to be fully controllable. This paper shows the implementation of a feedback controller to fully detect and mitigate the false data injection attacks. The controller can be incorporated in real life smart grid operations.

With the development of wireless communication, the reliability and the security of data is very significant for the wireless communication. In this paper, a delayed feedback chaotic encryption algorithm based on polar codes is proposed. In order to protect encoding information, we make uses of wireless channels to extract binary keys. The extracted binary keys will be used as the initial value of chaotic system to produce chaotic sequences. Besides, we use the chain effects of delayed feedback, which increase the difficulty of cryptanalysis. The results of the theoretical analyses and simulations show that the algorithm could guarantee the security of data transmission without affecting reliability.

In this paper, we address the stability problem of a noiseless linear time invariant control system over parallel Gaussian channels with feedback. It is shown that the eigenvalues-rate condition which has been proved as a necessary condition, is also sufficient for stability over parallel Gaussian channels. In fact, it is proved that for stabilizing a control system over the parallel Gaussian channels, it suffices that the Shannon channel capacity obtained by the water filling technique is greater than the sum of the logarithm of the unstable eigenvalues magnitude. In order to prove this sufficient condition, we propose a new nonlinear joint source channel coding for parallel Gaussian channels by which the initial state is transmitted through communication steps. This coding scheme with a linear control policy results in the stability of the system under the eigenvalues-rate condition. Hence, the proposed encoder, decoder and controller are efficient for this problem.

Networked control systems improve the efficiency of cyber-physical plants both functionally, by the availability of data generated even in far-flung locations, and operationally, by the adoption of standard protocols. A side-effect, however, is that now the safety and stability of a local process and, in turn, of the entire plant are more vulnerable to malicious agents. Leveraging the communication infrastructure, the authors here present the design of networked control systems with built-in resilience. Specifically, the paper addresses attacks known as false data injections that originate within compromised sensors. In the proposed framework for closed-loop control, the feedback signal is constructed by weighted consensus of estimates of the process state gathered from other interconnected processes. Observers are introduced to generate the state estimates from the local data. Side-channel monitors are attached to each primary sensor in order to assess proper code execution. These monitors provide estimates of the trust assigned to each observer output and, more importantly, independent of it; these estimates serve as weights in the consensus algorithm. The authors tested the concept on a multi-sensor networked physical experiment with six primary sensors. The weighted consensus was demonstrated to yield a feedback signal within specified accuracy even if four of the six primary sensors were injecting false data.

Cloud computing is being deployed more and more widely. However, the difficulty of monitoring the huge east-west traffic is a great security concern. In this paper, we proposed FBSample, a sampling method which employs the central control feature of SDN and feedback information of IDS. Evaluation results show FBSample can largely reduce the amount of packets to be transferred while maintaining a relatively high detection precision.

Swarm robotics, a combination of Swarm intelligence and robotics, is inspired from how the nature swarms, such as flock of birds, swarm of bees, ants, fishes etc. These group behaviours show great flexibility and robustness which enable the robots to perform various tasks like pattern formation, rescue and military operation, space expedition etc. This paper discusses an algorithm for forming patterns, which are English alphabets, by identical robots, in a finite amount of time and also analyses outcome of the algorithm. In order to implement the algorithm, 9 identical circular robots of diameter 15 cm are used, each having a Node MCU module and a rotary encoder attached to one wheel of the robot. The robots are initially placed at the centres of an imaginary 3×3 grid, on a white sheet of paper, of dimensions 250cm × 250 cm. All the robots are connected to the laptop's network via wifi and data send from the laptop is received by the Node MCU modules. This data includes the distance to be moved and the angle to be turned by each robot in order to form the letter. The rotary encoders enable the robot to move specific distances and turn specific angles, with high accuracy, by real time feedback. The algorithm is written in Python and image processing is done using OpenCV. Certain approximations are used in order to implement collision avoidance. Finally after calibration, the word given as input, is formed letter by letter, using these 9 identical robots.

Despite their ubiquity, many password meters provide inaccurate strength estimates. Furthermore, they do not explain to users what is wrong with their password or how to improve it. We describe the development and evaluation of a data-driven password meter that provides accurate strength measurement and actionable, detailed feedback to users. This meter combines neural networks and numerous carefully combined heuristics to score passwords and generate data-driven text feedback about the user's password. We describe the meter's iterative development and final design. We detail the security and usability impact of the meter's design dimensions, examined through a 4,509-participant online study. Under the more common password-composition policy we tested, we found that the data-driven meter with detailed feedback led users to create more secure, and no less memorable, passwords than a meter with only a bar as a strength indicator.

In cyberspace, unknown zero-day attacks can bring safety hazards. Traditional defense methods based on signatures are ineffective. Based on the Cyberspace Mimic Defense (CMD) architecture, the paper proposes a framework to detect the attacks and respond to them. Inputs are assigned to all online redundant heterogeneous functionally equivalent modules. Their independent outputs are compared and the outputs in the majority will be the final response. The abnormal outputs can be detected and so can the attack. The damaged executive modules with abnormal outputs will be replaced with new ones from the diverse executive module pool. By analyzing the abnormal outputs, the correspondence between inputs and abnormal outputs can be built and inputs leading to recurrent abnormal outputs will be written into the zero-day attack related database and their reuses cannot work any longer, as the suspicious malicious inputs can be detected and processed. Further responses include IP blacklisting and patching, etc. The framework also uses honeypot like executive module to confuse the attacker. The proposed method can prevent the recurrent attack based on the same exploit.

This paper proposed a feedback shift register structure which can be split, it is based on a research of operating characteristics about 70 kinds of cryptographic algorithms and the research shows that the “different operations similar structure” reconfigurable design is feasible. Under the configuration information, the proposed structure can implement the multiplication in finite field GF(2n), the multiply/divide linear feedback shift register and other operations. Finally, this paper did a logic synthesis based on 55nm CMOS standard-cell library and the results show that the proposed structure gets a hardware resource saving of nearly 32%, the average power consumption saving of nearly 55% without the critical delay increasing significantly. Therefore, the “different operations similar structure” reconfigurable design is a new design method and the proposed feedback shift register structure can be an important processing unit for coarse-grained reconfigurable cryptologic array.

This paper presents the results of research and simulation of feature automated control of a hysteretic object and the difference between automated control and automatic control. The main feature of automatic control is in the fact that the control loop contains human being as a regulator with its limited response speed. The human reaction can be described as integrating link. The hysteretic object characteristic is switching from one state to another. This is followed by a transient process from one to another characteristic. For this reason, it is very difficult to keep the object in a desired state. Automatic operation ensures fast switching of the feedback signal that produces such a mode, which in many ways is similar to the sliding mode. In the sliding mode control signal abruptly switches from maximum to minimum and vice versa. The average value provides the necessary action to the object. Theoretical analysis and simulation show that the use of the maximum value of the control signal is not required. It is sufficient that the switching oscillation amplitude is such that the output signal varies with the movement of the object along both branches with hysteretic characteristics in the fastest cycle. The average output value in this case corresponds to the prescribed value of the control task. With automated control, the human response can be approximately modeled by integrating regulator. In this case the amplitude fluctuation could be excessively high and the frequency could be excessively low. The simulation showed that creating an artificial additional fluctuation in the control signal makes possible to provide a reduction in the amplitude and the resulting increase in the frequency of oscillation near to the prescribed value. This should be evaluated as a way to improve the quality of automated control with the helps of human being. The paper presents some practical examples of the examined method.