Behavior Classification of A Grazing Goat through Machine Learning
The importance of goat production has increased during the last decades all over the world, predominantly in countries with harsh environmental conditions. This is due, among other factors, to the fact that goats have numerous advantages over other domestic animal species, that allow them to adapt and maintain their production in hard climatic conditions. Argentina has approximately 4.8 million goats, of which about 52\% are distributed in the Monte Desert region. This project aims at building a classification system based on information provided by inertial sensors for identifying a goat's grazing behavior. In particular, the study of grazing behavior help to understand how animals use vegetation and would allow to adjust the herd management according to the availability and conditions of the natural grassland. The time that animals spend on activities such as grazing, rumination and resting, reflects the climatic and pasture conditions, and therefore is highly related to the productive performance of animals. Therefore, the knowledge generated by animal behavior studies acquire great importance since it can be used to improve the efficiency of animal production systems. The project is carried out together with the Argentine Dryland Research Institute (IADIZA).
DNS attacks detection through Deep Learning
The Domain Name Service (DNS) is a central piece of the Internet. This importance, along with its lack of security, have turned it into a common target for different malicious behaviors like the use of Domain Generation Algoriths (DGA) to command and control a set of infected nodes, or Tunneling techniques to avoid restrictions of the system administrator. This project exploits the lexicographic characteristics present in normal domains, DGA and Tunneling through a multi-class Deep Learing model, based on Convolutional Neural Networks (CNN), capable of capturing combinations of characters that are important to discriminate malicious from non-malicious domains. The research is centered not only on the development of the neural network, but also its implementation for the detection of network traffic in real time.
Recommending malicious behavior
The recognition of malicious behavior in network traffic requires a big amount of resources, both human and computational. During the last few years, several approaches have been applied to try to alleviate the tasks of recognition and analysis. These approaches aim at making the task of netork security personnel easier, thus improving their ability to detect these behaviors and trying to increase the automation level of the process of malicious behaviors analysis and recognition. To this end, this project studies recommendation systems and their application to assist in the identification of threats to security in the network. In the labeling stage the goal is to assist in building a behavior model with a set of patterns compiled by experts using visual tools. Additionally, in the learning stage, supervised learning algorithms will be researched, to assist in the process of recommending labels for the traffic under analysis.