Data-Driven and Artificial Intelligence (AI) Approach for Modelling and Analyzing Healthcare Security Practice: A Systematic Review

Data breaches in healthcare continue to grow exponentially, calling for a rethinking into better approaches of security measures towards mitigating the menace. Traditional approaches including technological measures, have significantly contributed to mitigating data breaches but what is still lacking is the development of the “human firewall,” which is the conscious care security practices of the insiders. As a result, the healthcare security practice analysis, modeling and incentivization project (HSPAMI) is geared towards analyzing healthcare staffs’ security practices in various scenarios including big data. The intention is to determine the gap between staffs’ security practices and required security practices for incentivization measures. To address the state-of-the art, a systematic review was conducted to pinpoint appropriate AI methods and data sources that can be used for effective studies. Out of about 130 articles, which were initially identified in the context of human-generated healthcare data for security measures in healthcare, 15 articles were found to meet the inclusion and exclusion criteria. A thorough assessment and analysis of the included article reveals that, KNN, Bayesian Network and Decision Trees (C4.5) algorithms were mostly applied on Electronic Health Records (EHR) Logs and Network logs with varying input features of healthcare staffs’ security practices. What was found challenging is the performance scores of these algorithms which were not sufficiently outlined in the existing studies

Fake Malware Classification with CNN via Image Conversion: A Game Theory Approach

Improvements in malware detection techniques have grown significantly over the past decade. These improvements have resulted in better security for systems from various forms of malware attacks. However, it is also the reason for continuous evolution of malware which makes it harder for current security mechanisms to detect them. Hence, there is a need to understand different malwares and study classification techniques using the ever-evolving field of machine learning. The goal of this research project is to identify similarities between malware families and to improve on classification of malwares within different malware families by implementing Convolutional Neural Networks (CNNs) on their executable files. Moreover, there are different algorithms through which we can resize images. Classifying these malware images will help us understand effectiveness of the techniques. As malwares evolve continuously, we will generate fake malware image samples using Auxiliary Classifier Generative Adversarial Network (AC-GANs) and jumble the original dataset to try and break the CNN classifier

Anomaly detection system using system calls for android smartphone system

A smartphone is a mobile phone that provides advanced functions compared to traditional mobile phones. Smartphone systems have evolved considerably in terms of their capacity and functionality. Therefore, it is excessively used in personal and business life. Users of smartphone systems store all kinds of personal, business and confidential information on their systems, such as credit card and bank account information. In view of this popularity and storing confidential information, the cyber criminals and malware developers have set their eyes on the smartphone systems. Recent malware analysis reports show scared information about the serious threats that face smartphone systems. Thus, their protection is very important. Smartphone malwares detection techniques have been actively studied. Broadly, the two main techniques are: the signature-based techniques and the anomaly-based techniques. Each technique has its own advantages and drawbacks. In this Thesis, we are mainly interested in anomaly detection techniques. These techniques are useful for unknown malwares and variants of known ones. However, they still need more study and investigation to improve the malware detection accuracy and to consume as less resources as possible. This Thesis makes contributions on three levels to improve the efficiency, accuracy and adaptability of anomaly-based techniques for smartphone system based on Android operating system. The first contribution presents a study and review of the existing malware detection techniques. This survey provides a comprehensive classification of the studied techniques according to well defined criteria. The second contribution is based upon the dataset level and it is twofold. Firstly, we introduce dataset feature vector representation as a new factor that can improve the efficiency and the accuracy of malware detection solution. Secondly, we introduce filtering and abstraction process that refines the system call traces. The refined traces are much more compact and are closer to the main application behavior. The third contribution of this Thesis is on the benign behavior model level and it is biflod. In the first place, we build canonical database representing generic benign behavior from limited number of representative applications. In the second place, instead of using single machine learning classifier to model the benign behavior, we use hybrid machine learning classifier

Divergent Mating Behaviors and the Evolution of Reproductive Isolation

Sexual selection can cause rapid co-divergence of mating traits and mate preferences, generate reproductive barriers among individuals bearing divergent mating traits, and potentially lead to speciation. In my dissertation, I focused on two emerging topics that challenge this traditional speciation-by-mate-choice paradigm. First, sexual selection encompasses both mate preferences and intrasexual competition, yet speciation research disproportionally focused on the role of the former. Second, sexual behaviors are usually assumed to be genetically inherited, but they may often be shaped by learning instead, which can generate very different evolutionary trajectories for traits and preferences. Using studies of the highly polymorphic strawberry poison frogs (Oophaga pumilio), I demonstrated how incorporating (i) male-male competition and (ii) behavioral learning can enhance our understanding of the potential for speciation to be driven by sexual selection. I first characterized behavioral patterns across a natural contact zone between color morphs and showed that coloration (the divergent mating trait) mediates both female choice and male-male competition. Females often prefer males of their own (local) color over a novel color, and males, when defending territories, are more aggressive against their own color morph. I then tested how these color-mediated female preferences and male aggression biases interact to determine mating patterns. I conducted a controlled breeding experiment in which male-male competition and female mate choice act either in same or in opposing directions. In this study, females reproduced more often with the territorial male over the non-territorial male, regardless of the males’ coloration. This challenges the common assumption that knowledge of female preferences for male mating traits is sufficient to predict mating patterns. Finally, I discovered that learning from mothers during the tadpole stage shapes both female mate preferences and male aggression biases in O. pumilio. Based on this finding, I built a population genetic model and used it to demonstrate a simple and elegant mechanism by which sexual selection alone has the potential to initiate speciation. My research highlights the importance of considering interactions between mate choice, intrasexual competition, and behavioral learning, for studies of mating trait evolution and sexual selection’s role in speciation

Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps

As extension of Fuzzy Cognitive Maps are now introduced the Neutrosophic Cognitive Map

Final Report to NSF of the Standards for Facial Animation Workshop

The human face is an important and complex communication channel. It is a very familiar and sensitive object of human perception. The facial animation field has increased greatly in the past few years as fast computer graphics workstations have made the modeling and real-time animation of hundreds of thousands of polygons affordable and almost commonplace. Many applications have been developed such as teleconferencing, surgery, information assistance systems, games, and entertainment. To solve these different problems, different approaches for both animation control and modeling have been developed

Function of social calls in Brown Long-eared bats Plecotus auritus

Microchiropteran bats produce vocalisations for two purposes: echolocation and communication. Vocalisations used for communication are often referred to as social calls. In this thesis I examined the nature of Brown Long-eared bats Plecotus auritus social calls recorded at roost and foraging sites through a combination of recording and playback experiments. A total of 11,484 social calls were recorded at 20 maternity roosts sites and three types of vocalisations were dentified on the basis of shape, referred to as Type A, B, and C. Although Type A vocalisations shared the same basic pattern, it was a very large group within which there was a lot of variation in acoustic parameters. Principal component analysis and modelbased cluster analysis were used to look for patterns within this group, and this identified six clusters. Maternity colonies surveyed in this study varied in size from as few as nine up to 98 bats, and the number of social calls recorded at the roost sites was highly correlated with the numbers of bats present in the colony. The analysis of seasonal patterns of social call production revealed that the number of social calls recorded at maternity roost sites showed a linear increase from June to September, whereas, the number of bats emerging decreased sharply from August to September. Simulations of P. auritus social calls were used to investigate behavioural responses to calls away from roost sites using the Autobat. P. auritus were clearly much more responsive to simulations of their own species' social calls than to the other stimuli tested. This strongly suggests that the responses to the Autobat represent attempts to interact with the source of the stimulus. Recording with ultrasound and infra-red video was conducted to test the bats’ responses to the different types of synthesised call and whether these responses varied seasonally. A female’s approach response to the stimulus may represent an attempt to repel a perceived intruder from her foraging area. Alternatively, if calls were used to coordinate foraging by advertising the location of resources to other females that share the range, a response may represent an attempt to move towards such resources. Experiments showed that females were significantly more likely to respond to a stimulus produced within their core foraging area, than in the peripheral area, or outside their foraging area. On the other hand, while females regularly shared foraging ranges with other females, there was little evidence of co-ordination of movements between simultaneously radio-tracked dyads. It was concluded that responses to the stimuli probably represent attempts to repel perceived intruders from the foraging area. The thesis concludes with a discussion of some of the advantages and limitations of using play-back of synthesised social calls in the field to investigate vocal communication in bats. Ways in which studies of captive bats of known relatedness could be used to further elucidate the functions of social calls are discussed