Abstract: In traditional intrusion detection systems (IDS) used for critical infrastructure protection, such as SCADA (Supervisory Control and Data Acquisition) systems, intrusion alerts are analyzed by human analysts (security analysts). They evaluate the alerts and take decisions accordingly. Nevertheless, this is an extremely difficult and time consuming task as the number of alerts generated could be quite large and the environment may also change rapidly. This makes automated detection techniques more efficient for intrusion detection than human analysts. This paper we describes a new European Framework-7 funded research project, CockpicCI, and introduce an intelligent rick detection and analysis technique for Critical Infrastructures (CI). Results show that the proposed OCSVM (One Class Support Vector Machine) based intrusion detection approach can be effectively used to detect both known and unknown attacks.

• Publication Date: 13-Oct-2013
• DOI: 10.15224/978-981-07-7965-8-11
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Abstract: A significant portion of the population (0.5% - 0.8%) suffers from epilepsy. This study is an effort to predict seizures in epileptic patients. Electroencephalogram (EEG) continues to be an attractive tool in clinical practice due to its non-invasive nature and its real time depiction of brain function. The Short time Fourier Transform (STFT) is used for time frequency analysis of signals. In the present analysis, the authors adopted an effective technique for the denoising of EEG signals corrupted by non-stationary noises using Undecimated Wavelet Transform (UWT) which is implemented through Laboratory Virtual Instrumentation Engineering Workbench LabVIEW platform This Technique has overcome some of the short comings of the STFT by performing a multi - resolution analysis of signals. In the Wavelet Transform, high frequency components are analyzed with a sharp time resolution than low frequency components. This is the most desirable property, especially in analyzing fast transient wav

• Publication Date: 13-Oct-2013
• DOI: 10.15224/978-981-07-7965-8-13
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