Systematic Intrusion Detection Technique for an In-vehicle Network Based on Time-Series Feature Extraction

Author(s):  
Hiroki Suda ◽  
Masanori Natsui ◽  
Takahiro Hanyu
2016 ◽  
Vol 28 (S1) ◽  
pp. 183-195 ◽  
Author(s):  
Tianhong Liu ◽  
Haikun Wei ◽  
Chi Zhang ◽  
Kanjian Zhang

2021 ◽  
Vol MA2021-02 (57) ◽  
pp. 1939-1939
Author(s):  
Changhyun KIM ◽  
Junyeop Lee ◽  
Junkyu Park ◽  
Daewoong Jung ◽  
Chang-Woo Nam ◽  
...  

2011 ◽  
Vol 6 (2/3) ◽  
pp. 174 ◽  
Author(s):  
Nana K. Ampah ◽  
Cajetan M. Akujuobi ◽  
Mathew N.O. Sadiku ◽  
Shumon Alam

Author(s):  
Christian Herff ◽  
Dean J. Krusienski

AbstractClinical data is often collected and processed as time series: a sequence of data indexed by successive time points. Such time series can be from sources that are sampled over short time intervals to represent continuous biophysical wave-(one word waveforms) forms such as the voltage measurements representing the electrocardiogram, to measurements that are sampled daily, weekly, yearly, etc. such as patient weight, blood triglyceride levels, etc. When analyzing clinical data or designing biomedical systems for measurements, interventions, or diagnostic aids, it is important to represent the information contained within such time series in a more compact or meaningful form (e.g., noise filtering), amenable to interpretation by a human or computer. This process is known as feature extraction. This chapter will discuss some fundamental techniques for extracting features from time series representing general forms of clinical data.


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