Cyber Switching Attacks on Smart Grids

As we live in smart grid revolution, the conventional power systems turn into a fast pace toward smart grids, this transition creates new and significant challenges on the electrical network security level; In addition to the important features of the smart grids, cyber security transpire to be a serious issue due to connecting all the loads, generation units, renewable resources, substations and switches via communication network. Cyber-physical attacks are classified as the major threatening of smart grids security, this attacks may lead to a many severe repercussions in the smart grid such as large blackout and destruction of infrastructures. Switching attack is one of the most serious cyber-physical attacks on smart grids because it is direct, fast, and effective in destabilizing the grids. We start the thesis by introducing a state-of-the-art on cyber attacks from the power layer point of view i.e. the cyber attacks that affect the smart grid stability and what are the power system based solutions have been done so far to prevent or reduce the cyber attacks severity .As we focus on cyber switching attack and the method of preventing it, firstly a study on the attack principles and effects is introduced, we construct the attack on a single machine connected to an infinite bus through a transmission line. The attack on the target generator implemented by modeling the system using swing equation on Matlab platform, then we verified the result by implementing the same attack on Simulink Platform. Finally we present a novel solution to mitigate such type of attacks by using Thyristor-Controlled Braking Resistor (TCBR).The suggested solution is able to recapture the machine stability directly after the attack

Detection of replay attacks in cyber-physical systems using a frequency-based signature

This paper proposes a frequency-based approach for the detection of replay attacks affecting cyber-physical systems (CPS). In particular, the method employs a sinusoidal signal with a time-varying frequency (authentication signal) into the closed-loop system and checks whether the time profile of the frequency components in the output signal are compatible with the authentication signal or not. In order to carry out this target, the couplings between inputs and outputs are eliminated using a dynamic decoupling technique based on vector fitting. In this way, a signature introduced on a specific input channel will affect only the output that is selected to be associated with that input, which is a property that can be exploited to determine which channels are being affected. A bank of band-pass filters is used to generate signals whose energies can be compared to reconstruct an estimation of the time-varying frequency profile. By matching the known frequency profile with its estimation, the detector can provide the information about whether a replay attack is being carried out or not. The design of the signal generator and the detector are thoroughly discussed, and an example based on a quadruple-tank process is used to show the application and effectiveness of the proposed method.Peer ReviewedPostprint (author's final draft

F-8C adaptive flight control extensions

An adaptive concept which combines gain-scheduled control laws with explicit maximum likelihood estimation (MLE) identification to provide the scheduling values is described. The MLE algorithm was improved by incorporating attitude data, estimating gust statistics for setting filter gains, and improving parameter tracking during changing flight conditions. A lateral MLE algorithm was designed to improve true air speed and angle of attack estimates during lateral maneuvers. Relationships between the pitch axis sensors inherent in the MLE design were examined and used for sensor failure detection. Design details and simulation performance are presented for each of the three areas investigated

Distributed Fault Detection in Formation of Multi-Agent Systems with Attack Impact Analysis

Autonomous Underwater Vehicles (AUVs) are capable of performing a variety of deepwater marine applications as in multiple mobile robots and cooperative robot reconnaissance. Due to the environment that AUVs operate in, fault detection and isolation as well as the formation control of AUVs are more challenging than other Multi-Agent Systems (MASs). In this thesis, two main challenges are tackled. We first investigate the formation control and fault accommodation algorithms for AUVs in presence of abnormal events such as faults and communication attacks in any of the team members. These undesirable events can prevent the entire team to achieve a safe, reliable, and efficient performance while executing underwater mission tasks. For instance, AUVs may face unexpected actuator/sensor faults and the communication between AUVs can be compromised, and consequently make the entire multi-agent system vulnerable to cyber-attacks. Moreover, a possible deception attack on network system may have a negative impact on the environment and more importantly the national security. Furthermore, there are certain requirements for speed, position or depth of the AUV team. For this reason, we propose a distributed fault detection scheme that is able to detect and isolate faults in AUVs while maintaining their formation under security constraints. The effects of faults and communication attacks with a control theoretical perspective will be studied. Another contribution of this thesis is to study a state estimation problem for a linear dynamical system in presence of a Bias Injection Attack (BIA). For this purpose, a Kalman Filter (KF) is used, where we show that the impact of an attack can be analyzed as the solution of a quadratically constrained problem for which the exact solution can be found efficiently. We also introduce a lower bound for the attack impact in terms of the number of compromised actuators and a combination of sensors and actuators. The theoretical findings are accompanied by simulation results and numerical can study examples

PMU Placement in Electric Transmission Networks for Reliable State Estimation against False Data Injection Attacks

Currently the false data injection (FDI) attack bring direct challenges in synchronized phase measurement unit (PMU) based network state estimation in wide-area measurement system (WAMS), resulting in degraded system reliability and power supply security. This paper assesses the performance of state estimation in electric cyber-physical system (ECPS) paradigm considering the presence of FDI attacks. The adverse impact on network state estimation is evaluated through simulations for a range of FDI attack scenarios using IEEE 14-bus network model. In addition, an algorithmic solution is proposed to address the issue of additional PMU installation and placement with cyber security consideration and evaluated for a set of standard electric transmission networks (IEEE 14-bus, 30-bus and 57-bus network). The numerical result confirms that the FDI attack can significantly degrade the state estimation and the cyber security can be improved by an appropriate placement of a limited number of additional PMUs