Autonomic computing architecture for SCADA cyber security

Cognitive computing relates to intelligent computing platforms that are based on the disciplines of artificial intelligence, machine learning, and other innovative technologies. These technologies can be used to design systems that mimic the human brain to learn about their environment and can autonomously predict an impending anomalous situation. IBM first used the term ‘Autonomic Computing’ in 2001 to combat the looming complexity crisis (Ganek and Corbi, 2003). The concept has been inspired by the human biological autonomic system. An autonomic system is self-healing, self-regulating, self-optimising and self-protecting (Ganek and Corbi, 2003). Therefore, the system should be able to protect itself against both malicious attacks and unintended mistakes by the operator

Autonomic computing meets SCADA security

© 2017 IEEE. National assets such as transportation networks, large manufacturing, business and health facilities, power generation, and distribution networks are critical infrastructures. The cyber threats to these infrastructures have increasingly become more sophisticated, extensive and numerous. Cyber security conventional measures have proved useful in the past but increasing sophistication of attacks dictates the need for newer measures. The autonomic computing paradigm mimics the autonomic nervous system and is promising to meet the latest challenges in the cyber threat landscape. This paper provides a brief review of autonomic computing applications for SCADA systems and proposes architecture for cyber security

Autoencoder based anomaly detection for SCADA networks

Supervisory control and data acquisition (SCADA) systems are industrial control systems that are used to monitor critical infrastructures such as airports, transport, health, and public services of national importance. These are cyber physical systems, which are increasingly integrated with networks and internet of things devices. However, this results in a larger attack surface for cyber threats, making it important to identify and thwart cyber-attacks by detecting anomalous network traffic patterns. Compared to other techniques, as well as detecting known attack patterns, machine learning can also detect new and evolving threats. Autoencoders are a type of neural network that generates a compressed representation of its input data and through reconstruction loss of inputs can help identify anomalous data. This paper proposes the use of autoencoders for unsupervised anomaly-based intrusion detection using an appropriate differentiating threshold from the loss distribution and demonstrate improvements in results compared to other techniques for SCADA gas pipeline dataset

Analysis and testing of aeroelastic model stability augmentation systems

Testing and evaluation of a stability augmentation system for aircraft flight control were performed. The flutter suppression system and synthesis conducted on a scale model of a supersonic wing for a transport aircraft are discussed. Mechanization and testing of the leading and trailing edge surface actuation systems are described. The ride control system analyses for a 375,000 pound gross weight B-52E aircraft are presented. Analyses of the B-52E aircraft maneuver load control system are included

Atmospheric modelling for NOMAD-UVIS on board the ExoMars Trace Gas Orbiter mission

The Ultraviolet and Visible Spectrometer (UVIS) instrument development process requires the construction of an atmospheric model to provide synthetic UV transmission spectra. We discuss the requirements of the model to enable observational limits to be found, and the potential for certain atmospheric parameters to be further constrained

Charged analogue of Finch-Skea stars

We present solutions to the Einstein-Maxwell system of equations in spherically symmetric gravitational fields for static interior spacetimes with a specified form of the electric field intensity. The condition of pressure isotropy yields three category of solutions. The first category is expressible in terms of elementary functions and does not have an uncharged limit. The second category is given in terms of Bessel functions of half-integer order. These charged solutions satisfy a barotropic equation of state and contain Finch-Skea uncharged stars. The third category is obtained in terms of modified Bessel functions of half-integer order and does not have an uncharged limit. The physical features of the charged analogue of the Finch-Skea stars are studied in detail. In particular the condition of causality is satisfied and the speed of sound does not exceed the speed of light. The physical analysis indicates that this analogue is a realistic model for static charged relativistic perfect fluid spheres.Comment: 17 pages, To appear in Int. J. Mod. Phys.

Water ice clouds in a martian global climate model using data assimilation

The water cycle is one of the key seasonal cycles on Mars, and the radiative effects of water ice clouds have recently been shown to alter the thermal structure of the atmosphere. Current Mars General Circulation Models (MGCMs) are capable of representing the formation and evolution of water ice clouds, though there are still many unanswered questions regarding their effect on the water cycle, the local atmosphere and the global circulation. We discuss the properties of clouds in the LMD/UK MGCM and compare them with observations, focusing on the differences between the water ice clouds in a standalone model and those in a model which has been modified by assimilation of thermal and aerosol opacity spacecraft data

Assimilating the Martian water cycle

Water ice clouds have been shown to alter the thermal structure of the Martian atmosphere. Here we discuss the assimilation of total column water vapour and dust optical depth data from the Thermal Emission Spectrometer (TES) into the UK/LMD MGCM, and compare the predictions of cloud and temperature in the assimilation with observations

Influence of Electrode Material and Process Parameters on Surface Quality and MRR in EDM of AISI H13 using ANN

Electrical Discharge Machining (EDM) is a non conventional machining process where electrically conductive materials are machined by using precisely controlled spark that occurs between an electrode and a work piece in the presence of a dielectric fluid. It has been a demanding research area to model and optimize the EDM process in the present scenario. In the present p aper Artificial Neural Network (ANN) model has been proposed for the prediction of Material Removal Rate (MRR), Surface Roughness (SR) and Tool Wear Rate (TWR) in Electrical Discharge Machining (ED M) of AISI H13 Steel. For this purpose Neural Network Toolbox (nntool) with Matlab 7.1 has been used. The neural network based on process model has been generated to establish relationship between input process conditions ( Gap Voltage, Peak Current, Pulse On Time, Pulse Off Time and Electrode M aterial ) an d process responses (MRR, SR and TWR ). The ANN model has been trained and tested using the d ata generated from a series of experiments on EDM machine. The trained neural network system has been used to predict MRR , SR and TWR for different input conditions. The ANN model has been found efficient to predict EDM process response s for selected process conditions