Automated Epileptic Seizure Detection Method Based on the Multi-attribute EEG Feature Pool and mRMR Feature Selection Method

2019 ◽  
pp. 45-59
Author(s):  
Bo Miao ◽  
Junling Guan ◽  
Liangliang Zhang ◽  
Qingfang Meng ◽  
Yulin Zhang
Keyword(s):  
Feature Selection ◽  
Epileptic Seizure ◽  
Detection Method ◽  
Feature Selection Method ◽  
Seizure Detection ◽  
Selection Method ◽  
Epileptic Seizure Detection

Metamorphic Malware Detection Using Minimal Opcode Statistical Patterns

2018 ◽  
pp. 337-359
Author(s):  
Mahmood Fazlali ◽  
Peyman Khodamoradi
Keyword(s):  
Feature Selection ◽  
Decision Tree ◽  
High Speed ◽  
Detection Method ◽  
Malware Detection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Input File ◽  
Feature Vectors ◽  
Metamorphic Malware

High-speed and accurate malware detection for metamorphic malware are two goals in antiviruses. To reach beyond this issue, this chapter presents a new malware detection method that can be summarized as follows: (1) Input file is disassembled and classified to obtain the minimal opcode pattern as feature vectors; (2) a forward feature selection method (i.e., maximum relevancy and minimum redundancy) is applied to remove the redundant as well as irrelevant features; and (3) the process ends by classification through using decision tree. The results indicate the proposed method can effectively detect metamorphic malware in terms of speed, efficiency, and accuracy.

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