Exploratory Cluster Analysis from Ubiquitous Data Streams using Self-Organizing Maps

This thesis addresses the use of Self-Organizing Maps (SOM) for exploratory cluster analysis over ubiquitous data streams, where two complementary problems arise: first, to generate (local) SOM models over potentially unbounded multi-dimensional non-stationary data streams; second, to extrapolate these capabilities to ubiquitous environments. Towards this problematic, original contributions are made in terms of algorithms and methodologies. Two different methods are proposed regarding the first problem. By focusing on visual knowledge discovery, these methods fill an existing gap in the panorama of current methods for cluster analysis over data streams. Moreover, the original SOM capabilities in performing both clustering of observations and features are transposed to data streams, characterizing these contributions as versatile compared to existing methods, which target an individual clustering problem. Also, additional methodologies that tackle the ubiquitous aspect of data streams are proposed in respect to the second problem, allowing distributed and collaborative learning strategies. Experimental evaluations attest the effectiveness of the proposed methods and realworld applications are exemplified, namely regarding electric consumption data, air quality monitoring networks and financial data, motivating their practical use. This research study is the first to clearly address the use of the SOM towards ubiquitous data streams and opens several other research opportunities in the future

Lightweight Deep Learning Framework to Detect Botnets in IoT Sensor Networks by using Hybrid Self-Organizing Map

In recent years, we have witnessed a massive growth of intrusion attacks targeted at the internet of things (IoT) devices. Due to inherent security vulnerabilities, it has become an easy target for hackers to target these devices. Recent studies have been focusing on deploying intrusion detection systems at the edge of the network within these devices to localize threat mitigation to avoid computational expenses. Intrusion detection systems based on machine learning and deep learning algorithm have demonstrated the potential capability to detect zero-day attacks where traditional signature-based detection falls short. The paper aims to propose a lightweight and robust deep learning framework for intrusion detection that has computational potential to be deployed within IoT devices. The research builds upon previous researches showing the demonstrated efficiency of anomaly detection rates of self-organizing map-based intrusion. The paper will contribute to the existing body of knowledge by creating a hybrid self-organizing map (SOM) for the purpose of detecting botnet attacks and analyzing its accuracy compared with a traditional supervised artificial neural network (ANN). The paper also aims to answer questions regarding the computational efficiency of our hybrid self-organizing map by measuring the CPU consumption based on time to train model. The deep learning prototypes will be trained on the NSL-KDD dataset and Detection of IoT botnet Attacks dataset. The study will evaluate the performance of a self-organizing map based k-nearest neighbor prototype with the performance of a supervised artificial neural network based on validation metrics such as confusion matrix, f1, recall, precision, and accuracy score