This thesis examines the application of a Support Vector Machine (SVM) classifier to automatically detect sleep and quiet wake (rest) behavior in mice from pressure signals on their cage floor. Previous work employed Neural Networks (NN) and Linear Discriminant Analysis (LDA) to successfully detect sleep and wake behaviors in mice. Although the LDA was successful in distinguishing between the sleep and wake behaviors, it has several limitations, which include the need to select a threshold and difficulty separating additional behaviors with subtle differences, such as sleep and rest. The SVM has advantages in that it offers greater degrees of freedom than the LDA for working with complex data sets. In addition, the SVM has direct methods to limit overfitting for the training sets (unlike the NN method). This thesis develops an SVM classifier to characterize the linearly non separable sleep and rest behaviors using a variety of features extracted from the power spectrum, autocorrelation function, and generalized spectrum (autocorrelation of complex spectrum). A genetic algorithm (GA) optimizes the SVM parameters and determines a combination of 5 best features. Experimental results from over 9 hours of data scored by human observation indicate 75% classification accuracy for SVM compared to 68% accuracy for LDA.

Graphs (or networks) are now omnipresent, infusing into many aspects of society. This dissertation contributes unified graph-based algorithms and models to help solve large-scale societal problems affecting millions of individuals' daily lives, from cyber-attacks involving malware to tobacco and alcohol addiction. The main thrusts of our research are: (1) Propagation-based Graph Mining Algorithms: We develop graph mining algorithms to propagate information between the nodes to infer important details about the unknown nodes. We present three examples: AESOP (patented) unearths malware lurking in people's computers with 99.61% true positive rate at 0.01% false positive rate; our application of ADAGE on malware detection (patent-pending) enables to detect malware in a streaming setting; and EDOCS (patent-pending) flags comment spammers among 197 thousand users on a social media platform accurately and preemptively. (2) Graph-induced Behavior Characterization: We derive new insights and knowledge that characterize certain behavior from graphs using statistical and algorithmic techniques. We present two examples: a study on identifying attributes of smoking and drinking abstinence and relapse from an addiction cessation social media community; and an exploratory analysis of how company insiders trade. Our work has already made impact to society: deployed by Symantec, AESOP is protecting over 120 million people worldwide from malware; EDOCS has been deployed by Yahoo and it guards multiple online communities from comment spammers.

Although many counter-drone systems such as drone jammers and anti-drone guns have been implemented, drone incidents are still increasing. These incidents are categorized as deviant act, a criminal act, terrorist act, or an unintentional act (aka system failure). Examples of reported drone incidents are not limited to property damage, but include personal injuries, airport disruption, drug transportation, and terrorist activities. Researchers have examined only drone incidents from a technological perspective. The variance in drone architectures poses many challenges to the current investigation practices, including several operation approaches such as custom commutation links. Therefore, there is a limited research background available that aims to study the intercomponent mapping in unmanned aircraft system (UAS) investigation incorporating three critical investigative domains---behavioral analysis, forensic intelligence (FORINT), and unmanned aerial vehicle (UAV) forensic investigation. The UAS forensic intelligence-led taxonomy (U-FIT) aims to classify the technical, behavioral, and intelligence characteristics of four UAS deviant actions --- including individuals who flew a drone too high, flew a drone close to government buildings, flew a drone over the airfield, and involved in drone collision. The behavioral and threat profiles will include one criminal act (i.e., UAV contraband smugglers). The UAV forensic investigation dimension concentrates on investigative techniques including technical challenges; whereas, the behavioral dimension investigates the behavioral characteristics, distinguishing among UAS deviants and illegal behaviors. Moreover, the U-FIT taxonomy in this study builds on the existing knowledge of current UAS forensic practices to identify patterns that aid in generalizing a UAS forensic intelligence taxonomy. The results of these dimensions supported the proposed UAS forensic intelligence-led taxonomy by demystifying the predicted personality traits to deviant actions and drone smugglers. The score obtained in this study was effective in distinguishing individuals based on certain personality traits. These novel, highly distinguishing features in the behavioral personality of drone users may be of particular importance not only in the field of behavioral psychology but also in law enforcement and intelligence.