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Recently, Machine Learning (ML)-based solutions have been widely adopted to tackle the wide range of security challenges that have affected the progress of the Internet of Things (IoT) in various domains. Despite the reported promising results, the ML-based Intrusion Detection System (IDS) proved to be vulnerable to adversarial examples, which pose an increasing threat. In fact, attackers employ Adversarial Machine Learning (AML) to cause severe performance degradation and thereby evade detection systems. This promoted the need for reliable defense strategies to handle performance and ensure secure networks. This work introduces RobEns, a robust ensemble framework that aims at: (i) exploiting state-of-the-art ML-based models alongside ensemble models for IDSs in the IoT network; (ii) investigating the impact of evasion AML attacks against the provided models within a black-box scenario; and (iii) evaluating the robustness of the considered models after deploying relevant defense methods. In particular, four typical AML attacks are considered to investigate six ML-based IDSs using three benchmarking datasets. Moreover, multi-class classification scenarios are designed to assess the performance of each attack type. The experiments indicated a drastic drop in detection accuracy for some attempts. To harden the IDS even further, two defense mechanisms were derived from both data-based and model-based methods. Specifically, these methods relied on feature squeezing as well as adversarial training defense strategies. They yielded promising results, enhanced robustness, and maintained standard accuracy in the presence or absence of adversaries. The obtained results proved the efficiency of the proposed framework in robustifying IDS performance within the IoT context. In particular, the accuracy reached 100% for black-box attack scenarios while preserving the accuracy in the absence of attacks as well. Full article

This paper explores occupational safety and health regulation in Great Britain following the UK’s exit from the European Union. In particular, the paper focuses on the credibility of regulatory enforcement. The prospects raised by the UK’s exit from the European Union have long been part of a free-market fantasy—even obsession—of right-wing politicians and their ideologues. As the UK’s relationship with the EU is recalibrated, this will present right-wing opportunists with a new rationale for undermining health and safety law and enforcement. The paper uses empirical evidence of Great Britain’s record in health and safety law enforcement to evidence a drift towards an extreme form of self-regulation. It deepens this evidence with a detailed analysis of key international policy debates, arguing that Brexit now raises an imminent threat of the UK entering a ‘race to the bottom’. The paper concludes that the 2021 EU/UK Trade and Co-operation Agreement may enable the UK to evade its formal health and safety responsibilities under the treaty because of the lack of the prospect of significant retaliatory ‘rebalancing’ measures. Should minimal health and safety requirements cease to apply in the post-EU era, then the UK Government will be free to pursue a system of self-regulation that will allow health and safety standards to fall even further behind those of other developed economies. Full article