Polymorphic Malware Detection Using Hierarchical Hidden Markov Model

2011 ◽  
Author(s):  
Fahad Bin Muhaya ◽  
Muhammad Khurram Khan ◽  
Yang Xiang
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Malware Detection ◽  
Hierarchical Hidden Markov Model ◽  
Polymorphic Malware

scholarly journals Statistical structure of locomotion and its modulation by odors

2018 ◽  
Author(s):  
Liangyu Tao ◽  
Siddhi Ozarkar ◽  
Jeff Beck ◽  
Vikas Bhandawat
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Analytical Methods ◽  
Hidden Markov ◽  
Stereotyped Behavior ◽  
Fruit Flies ◽  
Statistical Structure ◽  
Hierarchical Hidden Markov Model ◽  
Stereotyped Behaviors

AbstractMost behaviors such as making tea are not stereotypical but have an obvious structure. However, analytical methods to objectively extract structure from non-stereotyped behaviors are immature. In this study, we analyze the locomotion of fruit flies and show that this non-stereotyped behavior is well-described by a Hierarchical Hidden Markov Model (HHMM). HHMM shows that a fly’s locomotion can be decomposed into a small number of locomotor features, and odors modulate locomotion by altering the time a fly spends performing different locomotor features. Importantly, although all flies in our dataset use the same set of locomotor features, individual flies vary considerably in how often they employ a given locomotor feature, and how this usage is modulated by odor. This variation is so large that the behavior of individual flies is best understood as being grouped into at least 3-5 distinct clusters, rather than variations around an average fly.

Sign in / Sign up

Export Citation Format

Share Document