Prediction of disease spread phenomena in large dynamic topology with application to malware detection in ad hoc networks

Prediction techniques based on data are applied in a broad range of applications such as bioinformatics, disease spread, and mobile intrusion detection, just to name a few. With the rapid emergence of on-line technologies numerous techniques for collecting and storing data for prediction-based analysis have been proposed in the literature. With the growing size of global population, the spread of epidemics is increasing at an alarming rate. Consequently, public and private health care officials are in a dire need of developing technological solutions for managing epidemics. Most of the existing syndromic surveillance and disease detection systems deal with a small portion of a real dataset. From the communication network perspective, the results reported in the literature generally deal with commonly known network topologies. Scalability of a disease detection system is a real challenge when it comes to modeling and predicting disease spread across a large population or large scale networks. In this dissertation, we address this challenge by proposing a hierarchical aggregation approach that classifies a dynamic disease spread phenomena at different scalability levels. Specifically, we present a finite state model (SEIR-FSM) for predicting disease spread, the model manifests itself into three different levels of data aggregation and accordingly makes prediction of disease spread at various scales. We present experimental results of this model for different disease spread behaviors on all levels of granularity. Subsequently, we present a mechanism for mapping the population interaction network model to a wireless mobile network topology. The objective is to analyze the phenomena of malware spread based on vulnerabilities. The goal is to develop and evaluate a wireless mobile intrusion detection system that uses a Hidden Markov model in connection with the FSM disease spread model (HMM-FSM). Subsequently, we propose a software-based architecture that acts as a network function virtualization (NFV) to combat malware spread in IoT based networks. Taking advantage of the NFV infrastructure's potential to provide new security solutions for IoT environments to combat malware attacks. We propose a scalable and generalized IDS that uses a Recurrent Neural Network Long Short Term Memory (RNN-LSTM) learning model for predicting malware attacks in a timely manner for the NFV to deploy the appropriate countermeasures. The analysis utilizes the susceptible (S), exposed (E), infected (I), and resistant (R) (SEIR) model to capture the dynamics of the spread of the malware attack and subsequently provide a patching mechanism for the network. Our analysis focuses primarily on the feasibility and the performance evaluation of the NFV RNN-LSTM proposed model.