Biblio

Trust Management (TM) systems for authentication are vital to the security of online interactions, which are ubiquitous in our everyday lives. Various systems, like the Web PKI (X.509) and PGP's Web of Trust are used to manage trust in this setting. In recent years, blockchain technology has been introduced as a panacea to our security problems, including that of authentication, without sufficient reasoning, as to its merits.In this work, we investigate the merits of using open distributed ledgers (ODLs), such as the one implemented by blockchain technology, for securing TM systems for authentication. We formally model such systems, and explore how blockchain can help mitigate attacks against them. After formal argumentation, we conclude that in the context of Trust Management for authentication, blockchain technology, and ODLs in general, can offer considerable advantages compared to previous approaches. Our analysis is, to the best of our knowledge, the first to formally model and argue about the security of TM systems for authentication, based on blockchain technology. To achieve this result, we first provide an abstract model for TM systems for authentication. Then, we show how this model can be conceptually encoded in a blockchain, by expressing it as a series of state transitions. As a next step, we examine five prevalent attacks on TM systems, and provide evidence that blockchain-based solutions can be beneficial to the security of such systems, by mitigating, or completely negating such attacks.

There are only a few visualisations targeting the communication of trust statements. Even though there are some advanced and scientifically founded visualisations-like, for example, the opinion triangle, the human trust interface, and T-Viz-the stars interface known from e-commerce platforms is by far the most common one. In this paper, we propose two trust visualisations based on T-Viz, which was recently proposed and successfully evaluated in large user studies. Despite being the most promising proposal, its design is not primarily based on findings from human-computer interaction or cognitive psychology. Our visualisations aim to integrate such findings and to potentially improve decision making in terms of correctness and efficiency. A large user study reveals that our proposed visualisations outperform T-Viz in these factors.

Defending key network infrastructure, such as Internet backbone links or the communication channels of critical infrastructure, is paramount, yet challenging. The inherently complex nature and quantity of network data impedes detecting attacks in real world settings. In this paper, we utilize features of network flows, characterized by their entropy, together with an extended version of the original Replicator Neural Network (RNN) and deep learning techniques to learn models of normality. This combination allows us to apply anomaly-based intrusion detection on arbitrarily large amounts of data and, consequently, large networks. Our approach is unsupervised and requires no labeled data. It also accurately detects network-wide anomalies without presuming that the training data is completely free of attacks. The evaluation of our intrusion detection method, on top of real network data, indicates that it can accurately detect resource exhaustion attacks and network profiling techniques of varying intensities. The developed method is efficient because a normality model can be learned by training an RNN within a few seconds only.

Intrusion Detection Systems (IDSs) are an important defense tool against the sophisticated and ever-growing network attacks. These systems need to be evaluated against high quality datasets for correctly assessing their usefulness and comparing their performance. We present an Intrusion Detection Dataset Toolkit (ID2T) for the creation of labeled datasets containing user defined synthetic attacks. The architecture of the toolkit is provided for examination and the example of an injected attack, in real network traffic, is visualized and analyzed. We further discuss the ability of the toolkit of creating realistic synthetic attacks of high quality and low bias.