Secure State Research Articles

IOS-based mobile application for network performance analysis

In today's world, when technology is developing faster and faster, many people face network malfunctions and threats. Ensuring the security and performance of your own network is a critical task for many IT professionals and ordinary users. There are many tools and ways to monitor, analyse and secure your network, but a mobile app is an extremely convenient option. A mobile application that combines several network functions at once saves time and is always at hand. Networking tools in a smartphone can replace many different desktop applications and websites (web APIs), provide command line tools, etc. The iOS operating system has high security standards and its ecosystem is quite closed. This protects users from a lot of malware and makes the device extremely resistant to hacking. However, it also imposes some restrictions on the network and system for the average user. That's why iOS apps that include a variety of networking tools are a really useful tool for users. To keep the network in an efficient and secure state, iOS has specific ways to monitor and detect network problems: Wi-Fi analysers, checking network routes and interfaces, checking Internet speed, ping, traceroute, etc. The use of these technologies in the application under consideration will enable efficient analysis of network productivity.

Secure state estimation of memristive neural networks with dynamic self-triggered strategy subject to deception attacks

This paper is dedicated to addressing state estimation for memristive neural networks (MNNs) featuring dynamic self-triggered mechanisms (DSTM) subject to deception attacks (DA). Taking into account the constrained channel bandwidth, the data sampling controller by dynamic self-triggering is proposed for measurement output. The network transmission of data among sensor and estimator is susceptible to deception attacks, and a corresponding state estimator is developed. Utilizing Lyapunov stability theory, it is demonstrated that the state error system is exponentially ultimately bounded in the mean square, and the dynamic self-triggered strategy avoids Zeno behavior. Furthermore, the estimation gains are obtained using a linear matrix inequality (LMI) approach. Lastly, simulated examples are provided to demonstrate the efficacy of the proposed approach.

Secure state estimation of networked switched systems under denial-of-service attacks

This paper studies the problem of secure state estimation of networked switched systems in the presence of denial-of-service (DoS) attacks, as well as disturbances and measurement noise. Firstly, a state transformation rule is designed to partition the original system into two subsystems, facilitating the design of discrete and continuous state observers. Secondly, by modifying the traditional super-twisting sliding-mode method and taking into account the frequency and duration characteristics of DoS attacks, we employ dynamic differential properties between different modes to design a switching law identification strategy. We show that this strategy can accurately estimate the switching state without imposing any requirement on the switching times and sequences. Thirdly, based on the identified activated mode, a set of mode-dependent continuous state sliding-mode observers is designed, that achieves continuous state estimation in finite time. The practicality and applicability of the developed results are validated through numerical simulations.

Secure state estimation with disturbance rejection against switched sparse sensor attacks

This paper studies the issue of secure state estimation for cyber-physical systems vulnerable to sparse sensor attacks, where attackers might change their attack targets at any time to deteriorate the estimation performance. To address this issue, a Luenberger-like observer is designed to estimate the system state under the attack interference, which employs an improved projection operator and the least square method to handle the switched attacks and introduces a disturbance estimator to enhance the anti-disturbance capability. It is proven that the observation error system can achieve asymptotic stability and dissipative performance, even under the influence of switched sensor attacks and disturbances. Different from the existing results, this method can not only defend against more erratic attacks but also effectively reject disturbances. The conclusion presented is validated through simulation experiments.

Secure state estimation for affine T-S fuzzy systems under sparse sensor attacks

This paper is concerned with the secure state estimation problem of affine T-S fuzzy systems under sparse sensor attacks. The objective is to construct a bank of fuzzy state estimators with an adaptive switching mechanism to ensure the attacked sensor measurements be isolated, and the estimation error system is asymptotic stable with a guaranteed H∞ performance. The case of unmeasurable plant premise variables is considered, which leads to the estimator implementation according to estimators’ state-space partitions may not be always synchronous with the plant. By constructing piecewise Lyapunov functions and employing the S-procedure and matrices decoupled techniques, the constructed state estimators synthesis conditions are derived in linear matrix inequalities (LMIs) form. Compared with existing methods, the proposed approach avoids restrictions on the structure of Lyapunov matrices and derives less conservative estimator design conditions. Finally, the effectiveness and superiorities of the proposed method is verified with a numerical example.

Secure state estimation for cyber-physical systems with unknown input sliding mode observer

Secure state estimation for cyber-physical systems with unknown input sliding mode observer

Optimal design and performance analysis of secure estimator for cyber‐physical systems against deception attacks

AbstractCyber‐physical systems (CPSs) are increasingly vulnerable to the threat from malicious adversaries owing to the deep integration of networks and facilities; therefore, CPSs' security against cyber attacks has attracted extensive attention. The deception attack is one of the typical cyber attacks targeting CPSs, and a deception attack mode is established to describe the simultaneous occurrence of actuator and sensor attacks in this paper. Compared with single‐target deception attacks with constant value, the designed attack mode that can describe deception attacks against multiple components and embedded random characteristics has higher applicability in practical engineering processes. To counter such attack behavior, a secure estimator is proposed based on the ideology of augmented state to achieve the optimal design of secure state estimation and deception attack identification. Taking the energy shortage problem of CPSs into account, a novel event‐triggered communication mechanism is introduced on the basis of measurement residuals to improve resource efficiency by reducing low‐value information transmission. The communication scheme and the augmented state estimator are co‐designed by adopting the implicit information of the event‐triggered mechanism. Then, the performance of the co‐designed estimator is analyzed by the upper bound of the estimation error covariance matrix. Besides, the reconciliation between estimation performance and communication energy consumption is achieved by the derivation of Pareto equilibrium. Finally, applications of our approaches to a 3D target tracking system are provided to illustrate the effectiveness of the proposed method.

Trust-Based Distributed Secure State Estimation Against Malicious Agents via Two-Hop Communication

This paper studies the distributed secure state estimation problem in unreliable multiagent networks through adversary detection. The agents are endowed with the capacity for detecting malicious neighbors and aim to collaboratively estimate the state variables of a dynamic system in the presence of malicious agents. A trust mechanism is first introduced to characterize the reliability of the information received from in-neighbors and identify the reliable information of two-hop in-neighbors. With the help of the trust mechanism and two-hop communication, two distributed secure state estimation schemes with adversary detection algorithms are developed under different resource requirements and adversary models. Necessary and sufficient conditions on the graph connectivity and joint detectability are established to guarantee that all the regular agents can detect the malicious in-neighbors and generate accurate estimates. An example is given to show the effectiveness of the proposed methods.

Secure State Estimation of Singularly Perturbed Switched Systems With Output Deception Attack via Discretized LKF Approach

Secure State Estimation of Singularly Perturbed Switched Systems With Output Deception Attack via Discretized LKF Approach

Secure state estimation via robust optimization for nonlinear cyber‐physical systems

AbstractThis article proposes secure state estimation for cyber‐physical systems against sensor attacks. The attack and defense strategies are established via additional historical data, and the defender aims to reduce the estimation error maximally while the attacker aims to degrade the system performance maximally. The algorithm is implemented in the Nash equilibrium framework where the defender first designs the defense strategy and then the attacker designs corresponding attack parameters to launch attacks. Then, a robust optimization problem is formulated using Wasserstein ambiguity sets, which turn out to be equivalent to a convex program. A novel secure observer is proposed, where the attack estimation is used to mitigate attacks. Moreover, the detector is to monitor system behavior and detects the existence of sensor attacks. Finally, simulation results and comparative results illustrate the effectiveness of the defense strategy.

Oxytocin and state attachment responses to secure base support after stress in middle childhood

ABSTRACT We tried to replicate the finding that receiving care increases children’s oxytocin and secure state attachment levels, and tested whether secure trait attachment moderates the oxytocin and state attachment response to care. 109 children (9-11 years old; M = 9.59; SD = 0.63; 34.9% boys) participated in a within-subject experiment. After stress induction (Trier Social Stress Test), children first remained alone and then received maternal secure base support. Salivary oxytocin was measured eight times. Secure trait and state attachment were measured with questionnaires, and Secure Base Script knowledge was assessed. Oxytocin levels increased after receiving secure base support from mother after having been alone. Secure state attachment changed less. Trait attachment and Secure Base Script knowledge did not moderate oxytocin or state attachment responses to support. This might mean that, regardless of the attachment history, in-the-moment positive attachment experiences might have a beneficial effect on trait attachment development in middle childhood.

Pattern of Journalists’ Reportage of Security Issues in Anambra State, Nigeria, in 2023

Security in Anambra State in the Southeast geopolitical zone is a priority issue. This is because the free movement of people doing business and pursuing their goals, the prosperity and happiness of citizens, the freedom to go about achieving ones purposes is dependent on a secure State. Journalists on the other hand have a duty under their surveillance and correlation functions to provide news and information on the state of security within their areas of jurisdiction. It thus becomes imperative to evaluate the pattern of media reportage of security issues in Anambra State from the perspective of the journalists themslves. The objectives of the study were to; determine if security issues are well reported by the media in Awka, Anambra State, examine the slant of media reportage of security issues in terms of favour to some group, determine the basic challenges faced by the media in reportage of security issues and find out the level of cooperation between security agencies and journalists in the media reportage of security Issues.The study is anchored on Social Responsibility Theory and Framing Theory. It adopted a survey research approach and drew a sample size of 162 respondents based on census sampling. Questionnaire was the instrument for data collection. Major findings from the study reveal that journalists in Awka report security issues fairly well, that majority of the journalists do not slant their news, that fear was the basic among the challenges faced by the journalists in the reportage of security issues and that journalists in Awka do not have free access to the PPRO or DPO and other security agents in the State. Among others, the study recommended that media organisations, the security formations and the State government should pay careful attention to journalists in this sensitive and hazardous line of duty, that seminars and workshops involving journalists and security agents should also be organized from time to time in order to foster unity among journalists and security agents and government should also empower security agencies and journalists with requisite resources and motivation to do more in securing lives and property.

Secure State Estimation for Artificial Neural Networks With Unknown-But-Bounded Noises: A Homomorphic Encryption Scheme.

This article is concerned with the secure state estimation problem for artificial neural networks (ANNs) subject to unknown-but-bounded noises, where sensors and the remote estimator are connected via open and bandwidth-limited communication networks. Using the encoding-decoding mechanism (EDM) and the Paillier encryption technique, a novel homomorphic encryption scheme (HES) is introduced, which aims to ensure the secure transmission of measurement information within communication networks that are constrained by bandwidth. Under this encoding-decoding-based HES, the data being transmitted can be encrypted into ciphertexts comprising finite bits. The emphasis of this research is placed on the development of a secure set-membership state estimation algorithm, which allows for the computation of estimates using encrypted data without the need for decryption, thereby ensuring data security throughout the entire estimation process. Taking into account the unknown-but-bounded noises, the underlying ANN, and the adopted HES, sufficient conditions are determined for the existence of the desired ellipsoidal set. The related secure state estimator gains are then derived by addressing optimization problems using the Lagrange multiplier method. Lastly, an example is presented to verify the effectiveness of the proposed secure state estimation approach.

Lasso-based state estimation for cyber-physical systems under sensor attacks

The development of algorithms for secure state estimation in vulnerable cyber-physical systems has been gaining attention in the last years. A consolidated assumption is that an adversary can tamper a relatively small number of sensors. In the literature, block-sparsity methods exploit this prior information to recover the attack locations and the state of the system. In this paper, we propose an alternative, Lasso-based approach and we analyse its effectiveness. In particular, we theoretically derive conditions that guarantee successful attack/state recovery, independently of established time sparsity patterns. Furthermore, we develop a sparse state observer, by starting from the iterative soft thresholding algorithm for Lasso, to perform online estimation. Through several numerical experiments, we compare the proposed methods to the state-of-the-art algorithms.

Secure State Estimation Against Eavesdropping Attacks Based on Time-Varying Coding and Noise-Adding

Secure State Estimation Against Eavesdropping Attacks Based on Time-Varying Coding and Noise-Adding

Secure Particle Filtering With Paillier Encryption–Decryption Scheme: Application to Multi-Machine Power Grids

This paper is concerned with the encryption-decryption-based state estimation problem for a class of multi-machine power grids with non-Gaussian noises. For the purposes of security enhancement and data privacy protection, the Paillier encryption-decryption scheme is adopted to map the measurement data into the ciphertext space before being transmitted through the communication network. The aim of this paper is to develop a novel secure particle filter algorithm to cope with the nonlinearity/non-Gaussianity from the system plant and the decrypted signals after the measurement transmission. In particular, a modified likelihood function is proposed to obtain the importance weights where the encryption-decryption process of the measurement data is taken into full consideration. The developed algorithm is applied to multi-machine power grids, and it is demonstrated via simulation studies (on three test scenarios of the IEEE 39-bus power system) that our proposed secure state estimation scheme possesses the desired performance index in terms of security and accuracy.

Event-Based Secure State Estimation for 2-D CPSs Under Deception Attacks: A Game Theoretic Approach

Event-Based Secure State Estimation for 2-D CPSs Under Deception Attacks: A Game Theoretic Approach

Safeguarding Autonomous Surface Vessels Against Cyber-Attacks on Sensor Systems

This paper presents a novel approach to enhance the cybersecurity of autonomous surface vessels (ASVs) by introducing a secure state estimation framework. The proposed methodology focuses on detecting and estimating the magnitude of sensor attacks through the utilization of an adaptive observer. The process involves filtering measurement signals and integrating them into the ASV's state space model. The adaptive observer is then applied to accurately estimate the magnitude of potential attacks. Simulation results illustrate the high accuracy of the proposed observer in both detecting and estimating the attack magnitude. This innovative approach contributes significantly to fortifying the resilience of ASVs against sensor threats, ultimately enhancing their reliability and security in operational scenarios.

Optimal resilient sensor placement problem for secure state estimation

In this paper, we consider a sensor placement problem for secure state estimation in an adversarial environment. Specifically, this paper deals with a system consisting of n agents where up to l sensor measurements are potentially compromised by malicious adversaries and tackles the problem of sensor placement such that the system state can be reconstructed even if the measurements are compromised. However, since it is costly to deploy sensors for all agents, we also aim to place sensors at the lowest possible cost. We call this problem the optimal resilient sensor placement problem (ORSPP) and show that this problem is coNP-hard. Therefore, it is, in general, impossible to compute the ORSPP in polynomial time unless P=coNP. On the positive side, however, we also provide that this problem can be solved in polynomial time if the network structure of the agents satisfies certain conditions. The concrete algorithm for the ORSPP is provided, and further, in the process of providing the coNP-hardness, we also derive a new form of a necessary and sufficient condition for secure state estimation. Finally, we show the results of numerical simulations using a random graph and a diffusion process.

Encryption-Decryption-Based State Estimation With Multirate Measurements Against Eavesdroppers: A Recursive Minimum-Variance Approach

This paper is concerned with the secure state estimation problem for a networked system with multi-rate measurements. The measurement data are transmitted to the estimator over a communication network, where an eavesdropper could intercept the transmitted information and generate the state estimates based on the overheard signals. In order to protect the privacy of the system state from information leakage, a novel encryptor is proposed to encrypt the measurement signals into codewords before being transmitted, and a decryptor is then adopted at the user side to decrypt the received codewords into the desired measurement signals, based on which a recursive state estimator is developed whose parameter is calculated by solving several coupled difference equations. Here, the secret key for decryption is available to the user but is unknown to the eavesdropper. The encryptor and the estimator are co-designed such that 1) at the user side, the estimation error covariance has an upper bound that is subsequently minimized in the sense of the matrix trace, where the uniform boundedness of the error covariance is also examined; and 2) at the eavesdropper side, the corresponding state estimation error is divergent. Two simulation examples are provided to demonstrate the effectiveness of our developed secure state estimation scheme.