A Power Grid Incident Identification Based on Physically Derived Cyber-Event Detection

This article proposes a cyber-event detection framework to aid in incident Identification and digital forensics cases aimed at investigating cyber crime committed against the critical infrastructure power grid. However, unlike other similar investigative techniques, the proposed approach examines only the physical information to derive a cyber conclusion. The developed framework extracts information from the physical parameters stored in historical databases of SCADA systems. The framework uses a pseudo-trusted model derived from randomly selected power system observations found in the historical databases. Afterwards, a technique known as Bayesian Model Averaging is used to average the models and create a more trusted model. Results indicate a successful Classification of on average 89% for the simulated cyber events of varying magnitudes

Impact of Transmission Power Control in multi-hop networks

Many Transmission Power Control (TPC) algorithms have been proposed in the past, yet the conditions under which they are evaluated do not always reflect typical Internet-of-Things (IoT) scenarios. IoT networks consist of several source nodes transmitting data simultaneously, possibly along multiple hops. Link failures are highly frequent, causing the TPC algorithm to kick-in quite often. To this end, in this paper we study the impact that frequent TPC actions have across different layers. Our study shows how one node’s decision to scale its transmission power can affect the performance of both routing and MAC layers of multiple other nodes in the network, generating cascading packet retransmissions and forcing far too many nodes to consume more energy. We find that crucial objectives of TPC such as conserving energy and increasing network capacity are severely undermined in multi-hop networks

A knowledge discovery approach for the detection of power grid state variable attacks

As the level of sophistication in power system technologies increases, the amount of system state parameters being recorded also increases. This data not only provides an opportunity for monitoring and diagnostics of a power system, but it also creates an environment wherein security can be maintained. Being able to extract relevant information from this pool of data is one of the key challenges still yet to be obtained in the smart grid. The potential exists for the creation of innovative power grid cybersecurity applications, which harness the information gained from advanced analytics. Such analytics can be based on the extraction of key features from statistical measures of reported and contingency power system state parameters. These applications, once perfected, will be able to alert upon potential cyber intrusions providing a framework for the creation of power system intrusion detection schemes derived from the cyber-physical perspective. With the power grid having a growing cyber dependency, these systems are becoming increasingly the target of attacks. The current power grid is undergoing a state of transition where new monitoring and control devices are being constantly added. These newly connected devices, by means of the cyber infrastructure, are capable of executing remote control decisions along with reporting sensor data back to a centralized location. This dissertation is an examination of advanced data mining and data analytic techniques for the development of a framework for detecting malicious cyber activity in the power grid based solely on reported power system state parameters. Through this examination, results indicate the successful development of a cyber-event detection framework capable of detecting and localizing 92% of the simulated cyber-events. In focusing on specific types of intrusions, this work describes the utilization of machine learning techniques to examine key features of multiple power systems for the detection of said intrusions. System analysis is preformed using the Newton-Raphson method to solve the nonlinear power system partial differential power flow equations for a 5-Bus and 14-Bus power system. This examination offers the theory and simulated implementation examples behind a context specific detection approach for securing the current and next generation\u27s critical infrastructure power grid

A wireless mesh communication protocol for smart-metering

Worldwide there has been increasing interest over the past few years for so-called Smart Meters , in academia, governments and in industry. Such smart-metering systems need a way to communicate the collected data reliably and cost efficiently to the back-office for analysis. Several competing technologies exist and are in use world-wide. Mesh-networks have been the winning technology in the USA and Australia for the past two years, and are gaining interest in Europe at the moment due to its reduced costs and increased reliability. In this paper we present and evaluate a real-life implementation of a new routing protocol for use in smart-metering mesh-network grids. The routing protocol we present is designed with both technological constraints and legislative requirements as posed by the application area in mind. Our evaluation of the protocol is based on real-world experience and data collected from real-world devices, in combination with simulation studies of the protocol. Our evaluation shows that the protocol is a robust, reliable solution for communicating collected data in difficult scenarios, showing great resilience against both bit-errors and node-failure