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The coronavirus pandemic is a new reality, and it severely affects the modus vivendi of the international community. In this context, governments are rushing to devise or embrace novel surveillance mechanisms and monitoring systems to fight the outbreak. The development of digital tracing apps, which among others are aimed at automatising and globalising the prompt alerting of individuals at risk in a privacy-preserving manner, is a prominent example of this ongoing effort. Very promptly, a number of digital contact tracing architectures have been sprouted, followed by relevant app implementations adopted by governments worldwide. Bluetooth, specifically its Low Energy (BLE) power-conserving variant, has emerged as the most promising short-range wireless network technology to implement the contact tracing service. This work offers the first to our knowledge full-fledged review of the most concrete contact tracing architectures proposed so far in a global scale. This endeavour does not only embrace the diverse types of architectures and systems, namely, centralised, decentralised, or hybrid, but also equally addresses the client side, i.e., the apps that have been already deployed in Europe by each country. There is also a full-spectrum adversary model section, which does not only amalgamate the previous work in the topic but also brings new insights and angles to contemplate upon.

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The intrusion detection systems (IDSs) are essential elements when it comes to the protection of an ICT infrastructure. A misuse IDS is a stable method that can achieve high attack detection rates (ADR) while keeping false alarm rates under acceptable levels. However, the misuse IDSs suffer from the lack of agility, as they are unqualified to adapt to new and ''unknown'' environments. That is, such an IDS puts the security administrator into an intensive engineering task for keeping the IDS up-to-date every time it faces efficiency drops. Considering the extended size of modern networks and the complexity of big network traffic data, the problem exceeds the substantial limits of human managing capabilities. In this regard, we propose a novel methodology which combines the benefits of self-taught learning and MAPE-K frameworks to deliver a scalable, self-adaptive, and autonomous misuse IDS. Our methodology enables the misuse IDS to sustain high ADR, even if it is imposed on consecutive and drastic environmental changes. Through the utilization of deep-learning based methods, the IDS is able to grasp an attack's nature based on the generalized feature reconstructions stemming directly from the unknown environment and its unlabeled data. The experimental results reveal that our methodology can breathe new life into the IDS without the constant need for manually refreshing its training set. We evaluate our proposal under several classification metrics and demonstrate that the ADR of the IDS increases up to 73.37% in critical situations where a statically trained IDS is rendered totally ineffective.

Mobile devices have evolved and experienced an immense popularity over the last few years. This growth however has exposed mobile devices to an increasing number of security threats. Despite the variety of peripheral protection mechanisms described in the literature, authentication and access control cannot provide integral protection against intrusions. Thus, a need for more intelligent and sophisticated security controls such as intrusion detection systems (IDSs) is necessary. Whilst much work has been devoted to mobile device IDSs, research on anomaly-based or behaviour-based IDS for such devices has been limited leaving several problems unsolved. Motivated by this fact, in this paper, we focus on anomaly-based IDS for modern mobile devices. A dataset consisting of iPhone users data logs has been created, and various classification and validation methods have been evaluated to assess their effectiveness in detecting misuses. Specifically, the experimental procedure includes and cross-evaluates four machine learning algorithms (i.e. Bayesian networks, radial basis function, K-nearest neighbours and random Forest), which classify the behaviour of the end-user in terms of telephone calls, SMS and Web browsing history. In order to detect illegitimate use of service by a potential malware or a thief, the experimental procedure examines the aforementioned services independently as well as in combination in a multimodal fashion. The results are very promising showing the ability of at least one classifier to detect intrusions with a high true positive rate of 99.8%.