Data Mining - Concepts and Techniques.

Preparing the books to read every day is enjoyable for many people. However, there are still many people who also don't like reading. This is a problem. But, when you can support others to start reading, it will be better. One of the books that can be recommended for new readers is experience and education. This book is not kind of difficult book to read. It can be read and understand by the new readers.

Experience and Education.

Self-Directed Learning, A Guide for Learners and Teachers

Beyond Freedom and Dignity

Cyberphysical systems (CPSs) are new class of engineered systems that offer close interaction between cyber and physical components. The field of CPS has been identified as a key area of research, and CPSs are expected to play a major role in the design and development of future systems. In this paper, we survey recent advancements made in the development and applications of CPSs. We classify the existing research work based on their characteristics and identify the future challenges. We also discuss the examples of prototypes of CPSs. The aim of this survey is to enable researchers and system designers to get insights into the working and applications of CPSs and motivate them to propose novel solutions for making wide-scale adoption of CPS a tangible reality.

Design Techniques and Applications of Cyberphysical Systems: A Survey

Synchrophasor systems provide an immense volume of data for wide area monitoring and control of power systems to meet the increasing demand of reliable energy The construction of traditional intrusion detection systems (IDSs) that use manually created rules based upon expert knowledge is knowledge-intensive and is not suitable in the context of this big data problem This paper presents a systematic and automated approach to build a hybrid IDS that learns temporal state-based specifications for power system scenarios including disturbances, normal control operations, and cyber-attacks A data mining technique called common path mining is used to automatically and accurately learn patterns for scenarios from a fusion of synchrophasor measurement data, and power system audit logs As a proof of concept, an IDS prototype was implemented and validated The IDS prototype accurately classifies disturbances, normal control operations, and cyber-attacks for the distance protection scheme for a two-line three-bus power transmission system

Developing a Hybrid Intrusion Detection System Using Data Mining for Power Systems

Power system disturbances are inherently complex and can be attributed to a wide range of sources, including both natural and man-made events. Currently, the power system operators are heavily relied on to make decisions regarding the causes of experienced disturbances and the appropriate course of action as a response. In the case of cyber-attacks against a power system, human judgment is less certain since there is an overt attempt to disguise the attack and deceive the operators as to the true state of the system. To enable the human decision maker, we explore the viability of machine learning as a means for discriminating types of power system disturbances, and focus specifically on detecting cyber-attacks where deception is a core tenet of the event. We evaluate various machine learning methods as disturbance discriminators and discuss the practical implications for deploying machine learning systems as an enhancement to existing power system architectures.

Machine learning for power system disturbance and cyber-attack discrimination

Kolb’s experiential learning cycle is perhaps the most scholarly influential and cited model regarding experiential learning theory. However, a key issue in interpreting Kolb’s model concerns a lac...

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Experiential learning – a systematic review and revision of Kolb’s model

SCADA systems are widely used in critical infrastructure sectors, including electricity generation and distribution, oil and gas production and distribution, and water treatment and distribution. SCADA process control systems are typically isolated from the internet via firewalls. However, they may still be subject to illicit cyber penetrations and may be subject to cyber threats from disgruntled insiders. We have developed a set of command injection, data injection, and denial of service attacks which leverage the lack of authentication in many common control system communication protocols including MODBUS, DNP3, and EtherNET/IP. We used these exploits to aid in development of a neural network based intrusion detection system which monitors control system physical behavior to detect artifacts of command and response injection attacks. Finally, we present intrusion detection accuracy results for our neural network based IDS which includes input features derived from physical properties of the control system.

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Thomas Morris

Papers

Developing a Hybrid Intrusion Detection System Using Data Mining for Power Systems

Machine learning for power system disturbance and cyber-attack discrimination

Experiential learning – a systematic review and revision of Kolb’s model

On SCADA control system command and response injection and intrusion detection

A control system testbed to validate critical infrastructure protection concepts