No description available.

No description available.

Arc Flash events occur due to faults in electrical equipment combined with a significant release of energy across an electrical arc. Due to the large energy release, plasma is generated, pressures increase, and the plasma expands. Under these conditions the plasma becomes excited enough to liquefy metal causing physical damage to equipment and any humans in the vicinity. This thesis investigates the state of art for detection of arc flash events and investigates a method of improving detection reliability, and speed by monitoring the high frequency voltage / current patterns utilizing methods similar to arc flash circuit interrupters (AFCI). A second alternative detection approach is determined through analysis of the physics of plasma development. The current state of art is based upon light detection. However this thesis experimentally investigates what happens before the arc event emits visible light. The results show that current flows to ground during an arc event slightly prior to the production of light. Further it shows through analysis of the physics of plasma that a high speed plasma detector has the potential to identify an arc event before the presence of visible light. Through the design and construction of experimental test setups, and physics analysis, this thesis provides new paths for detecting arc events that present opportunities to improve detection time.

Despite increased awareness of threats against Critical Infrastructure (CI), securing of Supervisory Control and Data Acquisition (SCADA) systems remains incomplete. The majority of research focuses on preventative measures such as improving communication protocols and implementing security policies. New attempts are being made to use commercial Intrusion Detection System (IDS) software to protect SCADA systems. These have limited effectiveness because the ability to detect specific threats requires the context of the SCADA system. SCADA context is defined as any information that can be used to characterise the current status and function of the SCADA system. In this thesis the standard IDS model will be used with the varying SCADA data sources to provide SCADA context to a signature and anomaly detection engine. A novel addition to enhance the IDS model will be to use the SCADA data sources to simulate the remote SCADA site. The data resulting from the simulation is used by the IDS to make behavioural comparison between the real and simulated SCADA site. To evaluate the enhanced IDS model the specific context of a water and wastewater system is used to develop a prototype. Using this context it was found that the inflow between sites has similar diurnal characteristic to network traffic. This introduced the idea of using inflow data to detect abnormal behaviour for a remote wastewater site. Several experiments are proposed to validate the prototype using data from a real SCADA site. Initial results show good promise for detecting abnormal behaviour and specific threats against water and wastewater SCADA systems.

In this paper, system of automatic processing of attacks using honeypots is discussed. The first goal of the thesis is to become familiar with the issue of signatures to detect malware on the network, especially the analysis and description of existing methods for automatic generation of signatures using honeypots. The main goal is to use the acquired knowledge to the design and implementation of tool which will perform the detection of new malicious software on the network or end user's workstation.