As the scale of distributed control systems over networks increases and interactions between different subsystems become more sophisticated, questions of the resilience of such networks to attacks and faults increase in importance. The need to adapt (and redefine) classical system and control-theoretic notions to answer such questions using the language of graphs has recently started to gain attention as a fertile and important area of research. This paper presents an overview of graph-theoretic methods for analyzing the resilience of distributed control systems. We survey distributed algorithms that have been proposed to solve a variety of problems in networked systems, and summarize their resilience against adversarial actions in the context of the structure of the underlying networks. We also discuss graph-theoretic methods to quantify the attack impact, and reinterpret some system-theoretic notions of robustness from a graph-theoretic standpoint to mitigate the impact of the attacks. We conclude by introducing some avenues for further research in this field. 

Graph-theoretic approaches for analyzing the resilience of distributed control systems: A tutorial and survey

Abstract Network Intrusion Detection Systems (NIDS) are utilized to find hostile network connections. This can be accomplished by looking at traffic network activity, but it takes a lot of work. The NIDS heavily utilizes approaches for data extraction and machine learning to find anomalies. In terms of feature selection, NIDS is far more effective. This is accurate since anomaly identification uses a number of time-consuming features. Because of this, the feature selection method influences how long it takes to analyze movement patterns and how clear it is. The goal of the study is to provide NIDS with an attribute selection approach. PSO has been used for that purpose. The Network Intrusion Detection System that is being developed will be able to identify any malicious activity in the network or any unusual behavior in the network, allowing the identification of the illegal activities and safeguarding the enormous amounts of confidential data belonging to the customers from being compromised. In the research, datasets were produced utilising both a network infrastructure and a simulation network. Wireshark is used to gather data packets whereas Cisco Packet Tracer is used to build a network in a simulated environment. Additionally, a physical network consisting of six node MCUs connected to a laptop and a mobile hotspot, has been built and communication packets are being recorded using the Wireshark tool. To train several machine learning models, all the datasets that were gathered—created datasets from our own studies as well as some common datasets like NSDL and UNSW acquired from Kaggle—were employed. Additionally, PSO, which is an optimization method, has been used with these ML algorithms for feature selection. In the research, KNN, decision trees, and ANN have all been combined with PSO for a specific case study. And it was found demonstrated the classification methods PSO + ANN outperformed PSO + KNN and PSO + DT in this case study. 

Intrusion detection systems for wireless sensor networks using computational intelligence techniques

Resilient consensus in continuous-time networks with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e291" altimg="si62.svg"><mml:mi>ℓ</mml:mi></mml:math>-hop communication and time delay

This paper studies the robustness of observability of a linear time-invariant system under sensor failures from a computational perspective. To be precise, the problem of determining the minimum number of sensors whose removal can destroy system observability, as well as the problem of determining the minimum number of state variables that need to be prevented from being directly measured by the existing sensors to destroy observability, is investigated. The first one is closely related to the ability of unique state reconstruction of a system under adversarial sensor attacks, and the dual of both problems are in the opposite direction of the well-studied minimal controllability problems. It is proven that all these problems are NP-hard, both for a numerical system and a structured system, even restricted to some special cases. It is also shown that the first problems both for a numerical system and a structured one share a cardinality-constrained submodular minimization structure, for which there is no known constant or logarithmic factor approximation in general. On the other hand, for the first two problems, under a reasonable assumption often met by practical systems, that the eigenvalue geometric multiplicities of the numerical systems or the matching deficiencies of the structured systems are bounded by a constant, by levering the rank-one update property of the involved rank function, it is possible to obtain the corresponding optimal solutions by traversing a subset of the feasible solutions. We show such a method has polynomial time complexity in the system dimensions and the number of sensors.

Observability Robustness under Sensor Failures: a Computational Perspective.

With the increasing utilisation of information technology and artificial intelligence in practical control systems, particularly in large-scale distributed networked systems, growing concerns have arisen regarding the potential disclosure of individuals' sensitive information to adversaries. Consequently, safeguarding privacy security against the rapidly increasing risk of privacy leakages has become a top priority in modern control systems. This survey aims to offer a comprehensive review of privacy-preserving control and filtering problems in networked control system. First, we review some basic introductions to the privacy-preserving mechanisms from the perspective of control community. Then, we present recent advancements in the design of privacy-preserving strategies for various control and filtering problems. Moreover, several possible future research topics are outlined on the privacy-preserving issue of control systems.

A survey on privacy-preserving control and filtering of networked control systems

An overview of structural systems theory

A resilient continuous-time consensus method using a switching topology

This letter proposes a model predictive control framework to design autonomous rendezvous operations for terrestrial and space missions in the scope of spacecraft and drones, in the presence of obstacles in an unknown environment. Vehicles equipped with a LiDAR collecting points associated with the obstacles are considered. By proposing an efficient method to compute the smallest ellipse that contains the collected LiDAR points, this letter is able to correct, in run time, the vehicle trajectory to avoid collision and reach the rendezvous target. Finally, simulations of the proposed approach to show its effectiveness are presented.

Model Predictive Control With Collision Avoidance for Unknown Environment

Discrete-time consensus plays a key role in multi-agent systems and distributed protocols. Unfortunately, due to the self-loop dynamics of the agents (an agent's current state depends only on its own immediately previous state, i.e., one time-step in the past), they often lack privacy guarantees. Therefore, in this paper, we propose a novel design that consists of a network augmentation, where each agent uses the previous iteration values and the newly received ones to increase the privacy guarantees. To formally evaluate the privacy of a network of agents, we define the concept of privacy index, which intuitively measures the minimum number of agents that should work in coalition to recover all the initial states. Moreover, we aim to explore if there is a trade-off between privacy and accuracy (rate of convergence) or if we can increase both. We unveil that, with the proposed method, we can design networks with higher privacy index and faster convergence rates. Remarkably, we further ensure that the network always reaches consensus even when the original network does not. Finally, we illustrate the proposed method with examples and present networks that lead to higher privacy levels and, in the majority of the cases, to faster consensus rates. 

Designing communication networks for discrete-time consensus for performance and privacy guarantees

Controlling large-scale dynamical networks is crucial to understand and, ultimately, craft the evolution of complex behavior. While broadly speaking we understand how to control Markov dynamical networks, where the current state is only a function of its previous state, we lack a general understanding of how to control dynamical networks whose current state depends on states in the distant past (i.e. long-term memory). Therefore, we require a different way to analyze and control the more prevalent long-term memory dynamical networks. Herein, we propose a new approach to control dynamical networks exhibiting long-term power-law memory dependencies. Our newly proposed method enables us to find the minimum number of driven nodes (i.e. the state vertices in the network that are connected to one and only one input) and their placement to control a long-term power-law memory dynamical network given a specific time-horizon, which we define as the 'time-to-control'. Remarkably, we provide evidence that long-term power-law memory dynamical networks require considerably fewer driven nodes to steer the network's state to a desired goal for any given time-to-control as compared with Markov dynamical networks. Finally, our method can be used as a tool to determine the existence of long-term memory dynamics in networks. 

The role of long-term power-law memory in controlling large-scale dynamical networks

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