Social engineering is used as an umbrella term for a broad spectrum of computer exploitations that employ a variety of attack vectors and strategies to psychologically manipulate a user. Semantic attacks are the specific type of social engineering attacks that bypass technical defences by actively manipulating object characteristics, such as platform or system applications, to deceive rather than directly attack the user. Commonly observed examples include obfuscated URLs, phishing emails, drive-by downloads, spoofed websites and scareware to name a few. This article presents a taxonomy of semantic attacks, as well as a survey of applicable defences. By contrasting the threat landscape and the associated mitigation techniques in a single comparative matrix, we identify the areas where further research can be particularly beneficial.

The modern Internet of Things (IoT)-based smart home is a challenging environment to secure: devices change, new vulnerabilities are discovered and often remain unpatched, and different users interact with their devices differently and have different cyber risk attitudes. A security breach’s impact is not limited to cyberspace, as it can also affect or be facilitated in physical space, for example, via voice. In this environment, intrusion detection cannot rely solely on static models that remain the same over time and are the same for all users. We present MAGPIE, the first smart home intrusion detection system that is able to autonomously adjust the decision function of its underlying anomaly classification models to a smart home’s changing conditions (e.g., new devices, new automation rules and user interaction with them). The method achieves this goal by applying a novel probabilistic cluster-based reward mechanism to non-stationary multi-armed bandit reinforcement learning. MAGPIE rewards the sets of hyperparameters of its underlying isolation forest unsupervised anomaly classifiers based on the cluster silhouette scores of their output. Experimental evaluation in a real household shows that MAGPIE exhibits high accuracy because of two further innovations: it takes into account both cyber and physical sources of data; and it detects human presence to utilise models that exhibit the highest accuracy in each case. MAGPIE is available in open-source format, together with its evaluation datasets, so it can benefit from future advances in unsupervised and reinforcement learning and be able to be enriched with further sources of data as smart home environments and attacks evolve.

Denial of service (DoS) is a prevalent threat in today's networks because DoS attacks are easy to launch, while defending a network resource against them is disproportionately difficult. Despite the extensive research in recent years, DoS attacks continue to harm, as the attackers adapt to the newer protection mechanisms. For this reason, we start our survey with a historical timeline of DoS incidents, where we illustrate the variety of types, targets and motives for such attacks and how they evolved during the last two decades. We then provide an extensive literature review on the existing research on DoS protection with an emphasis on the research of the last years and the most demanding aspects of defence. These include traceback, detection, classification of incoming traffic, response in the presence of an attack and mathematical modelling of attack and defence mechanisms. Our discussion aims to identify the trends in DoS attacks, the weaknesses of protection approaches and the qualities that modern ones should exhibit, so as to suggest new directions that DoS research can follow.