The recent advancements in computing and sensor technologies, coupled with improvements in embedded system design methodologies, have resulted in the novel paradigm called the Internet of Things (IoT). IoT is essentially a network of small embedded devices enabled with sensing capabilities that can interact with multiple entities to relay information about their environments. This sensing information can also be stored in the cloud for further analysis, thereby reducing storage requirements on the devices themselves. The above factors, coupled with the ever increasing needs of modern society to stay connected at all times, has resulted in IoT technology penetrating all facets of modern life. In fact IoT systems are already seeing widespread applications across multiple industries such as transport, utility, manufacturing, healthcare, home automation, etc. Although the above developments promise tremendous benefits in terms of productivity and efficiency, they also bring forth a plethora of security challenges. Namely, the current design philosophy of IoT devices, which focuses more on rapid prototyping and usability, results in security often being an afterthought. Furthermore, one needs to remember that unlike traditional computing systems, these devices operate under the assumption of tight resource constraints. As such this makes IoT devices a lucrative target for exploitation by adversaries. This inherent flaw of IoT setups has manifested itself in the form of various distributed denial of service (DDoS) attacks that have achieved massive throughputs without the need for techniques such as amplification, etc. Furthermore, once exploited, an IoT device can also function as a pivot point for adversaries to move laterally across the network and exploit other, potentially more valuable, systems and services. Finally, vulnerable IoT devices operating in industrial control systems and other critical infrastructure setups can cause sizable loss of property and in some cases even lives, a very sobering fact. In light of the above, this dissertation research presents several novel strategies for identifying known and zero-day attacks against IoT devices, as well as identifying infected IoT devices present inside a network along with some mitigation strategies. To this end, network telescopes are leveraged to generate Internet-scale notions of maliciousness in conjunction with signatures that can be used to identify such devices in a network. This strategy is further extended by developing a taxonomy-based methodology which is capable of categorizing unsolicited IoT behavior by leveraging machine learning (ML) techniques, such as ensemble learners, to identify similar threats in near-real time. Furthermore, to overcome the challenge of insufficient (malicious) training data within the IoT realm, a generative adversarial network (GAN) based framework is also developed to identify known and unseen attacks on IoT devices. Finally, a software defined networking (SDN) based solution is proposed to mitigate threats from unsolicited IoT devices.

Neural networks are seldom used as a modelling tool by statisticians. This is often due to the lack of knowledge in the eld of neural networks as neural networks are frequently perceived as mysterious methods that evolved from the eld of computer science. In this dissertation an attempt will be made to show that neural network methods are closely related to statistical methods. In particular we will show how a backpropagation neural network can be used for statistical applications like regression and classi cation which will include the setting up a of neural network for di erent objectives and also using a neural network for predictive inference. Through simulations we will show an e cient method to t a neural network in practical applications. A neural network will then be employed in a practical application to illustrate how to use a neural network in a regression or classi cation context. This application will also show the necessity of statistical knowledge when using a neural network as a modelling tool. / Dissertation (MSc)--University of Pretoria, 2010. / Statistics / unrestricted

For an IT company that provides a service over the Internet like Facebook or Spotify, it is very important to provide a high quality of service; however, predicting the quality of service is generally a hard task. The goal of this thesis is to investigate whether an approach that makes use of statistical learning to predict the quality of service can obtain accurate predictions for a Voldemort key-value store [1] in presence of dynamic load patterns and network statistics. The approach follows the idea that the service-level metrics associated with the quality of service can be estimated from serverside statistical observations, like device and network statistics. The advantage of the approach analysed in this thesis is that it can virtually work with any kind of service, since it is based only on device and network statistics, which are unaware of the type of service provided. The approach is structured as follows. During the service operations, a large amount of device statistics from the Linux kernel of the operating system (e.g. cpu usage level, disk activity, interrupts rate) and some basic end-to-end network statistics (e.g. average round-trip-time, packet loss rate) are periodically collected on the service platform. At the same time, some service-level metrics (e.g. average reading time, average writing time, etc.) are collected on the client machine as indicators of the store’s quality of service. To emulate network statistics, such as dynamic delay and packet loss, all the traffic is redirected to flow through a network emulator. Then, different types of statistical learning methods, based on linear and tree-based regression algorithms, are applied to the data collections to obtain a learning model able to accurately predict the service-level metrics from the device and network statistics. The results, obtained for different traffic scenarios and configurations, show that the thesis’ approach can find learning models that can accurately predict the service-level metrics for a single-node store with error rates lower than 20% (NMAE), even in presence of network impairments.

This paper studies the topological properties of the International Trade Network (ITN) among world countries using a network analysis. We explore the distribu- tions of the most important network statistics measuring connectivity, assortativ- ity and clustering. We show that the topological properties of the weighted rep- resentation of the ITN are very different from those obtained by a binary network approach. In particular, we find that: (i) the majority of countries are character- ized by weak trade relationships, (ii) well connected countries tend to trade with poorly connected partners and (iii) countries holding more intense trade relation- ships are more clustered. Finally, we display that all structural properties of the ITN have remained remarkably stable over time.

This work deals with data capturing from the active network elements and storing and displaying the captured data. It describes the SNMP, analyzes and compares available tools for monitoring computer networks. It describes the design of own system for monitoring computer networks and deployment in computer network in the BUT campus.

This thesis proposes and discusses a statistical model for interpreting forensic DNA mixtures. We develop methods for estimation of model parameters and assessing the uncertainty of the estimated quantities. Further, we discuss how to interpret the mixture in terms of predicting the set of contributors. We emphasise the importance of challenging any interpretation of a particular mixture, and for this purpose we develop a set of diagnostic tools that can be used in assessing the adequacy of the model to the data at hand as well as in a systematic validation of the model on experimental data. An important feature of this work is that all methodology is developed entirely within the framework of the adopted model, ensuring a transparent and consistent analysis. To overcome the challenge that lies in handling the large state space for DNA profiles, we propose a representation of a genotype that exhibits a Markov structure. Further, we develop methods for efficient and exact computation in a Bayesian network. An implementation of the model and methodology is available through the R package DNAmixtures.