How anxiety alters the perception of time: probing the neurocognitive impacts of anxiety using a translational temporal judgement task

Anxiety, the state of anticipating that a negative event may occur, can be adaptive by promoting harm-avoidant behaviours, and thus preparing an organism to react to threats. However, it can also spiral out of control, resulting in anxiety disorders, with these being one of the most common mental health issues leading to disability. Despite decades of research, progress on treating anxiety seems to have stalled. This lack of progress has been attributed, at least in part, to the gap between animal and human research. By adopting a cognitive task and anxiety manipulation that are translational, this thesis attempts to bridge the aforementioned gap by investigating the neurocognitive effects of adaptive and pathological anxiety in humans; research that could be in turn translated into animals. Towards that goal, a temporal bisection task and a threat-of-shock manipulation were used. The first experimental chapter (Chapter 3) showed that induced anxiety can reliably shift time perception, while fear does not, suggesting that anxiety and fear might be distinct entities. The second experimental chapter (Chapter 4) attempted to tease apart the mechanism of the aforementioned effect, by investigating whether a load manipulation shifts time perception similarly to induced anxiety. Load did not shift time perception; hence it is unclear whether anxiety leads to temporal alterations via ‘overloading’ limited cognitive resources. The third experimental (Chapter 5) chapter explored the neural correlates of the effect of anxiety on time perception using functional magnetic resonance imaging, employing a pilot and a pre-registered study. The findings suggested some overlap between anxiety and task related processing, leaving open the possibility that anxiety impacts cognition via commandeering finite mental resources. The (preliminary) data of the fourth experimental chapter (Chapter 6) suggested that time perception is not impaired in clinically anxious individuals, but working memory is, highlighting potential dissociations between adaptive and pathological anxiety. In the final chapter the findings are discussed in light of neurocognitive theories of anxiety, alongside a discussion of the overall approach of the thesis and future experiments that could clarify disparate findings

An analytical framework in LEO mobile satellite systems servicing batched Poisson traffic

The authors consider a low earth orbit (LEO) mobile satellite system (MSS) that accepts new and handover calls of multirate service-classes. New calls arrive in the system as batches, following the batched Poisson process. A batch has a generally distributed number of calls. Each call is treated separately from the others and its acceptance is decided according to the availability of the requested number of channels. Handover calls follow also a batched Poisson process. All calls compete for the available channels under the complete sharing policy. By considering the LEO-MSS as a multirate loss system with ‘satellite-fixed’ cells, it can be analysed via a multi-dimensional Markov chain, which yields to a product form solution (PFS) for the steady-state distribution. Based on the PFS, they propose a recursive and yet efficient formula for the determination of the channel occupancy distribution, and consequently, for the calculation of various performance measures including call blocking and handover failure probabilities. The latter are much higher compared to the corresponding probabilities in the case of the classical (and less bursty) Poisson process. Simulation results verify the accuracy of the proposed formulas. Furthermore, they discuss the applicability of the proposed model in software-defined LEO-MSS

NeuralPot: an industrial honeypot implementation based on convolutional neural networks

Honeypots are powerful security tools, which are developed to shield commercial and industrial networks from malicious activity. Honeypots act as passive and interactive decoys in a network by attracting malicious activity away from critical network devices. Given that the security incidents against industrial and critical infrastructure are getting sophisticated and persistent, advanced security systems are needed. In this paper, a novel industrial honeypot implementation is presented, which is based on the Modbus protocol, entitled NeuralPot. The presented NeuralPot honeypot is able to emulate industrial Modbus entities in order to actively confuse the intruders. It achieves this by introducing two distinct deep neural networks, a Generative Adversarial Network and an Autoencoder Network, which learn Modbus device behavior and generate realistic-looking traffic behavior. Based on the evaluation results, the proposed industrial honeypot performs well in terms of accuracy, similarity, and elapsed time of data generation

Explainable AI-based Intrusion Detection in the Internet of Things

The revolution of Artificial Intelligence (AI) has brought about a significant evolution in the landscape of cyberattacks. In particular, with the increasing power and capabilities of AI, cyberattackers can automate tasks, analyze vast amounts of data, and identify vulnerabilities with greater precision. On the other hand, despite the multiple benefits of the Internet of Things (IoT), it raises severe security issues. Therefore, it is evident that the presence of efficient intrusion detection mechanisms is critical. Although Machine Learning (ML) and Deep Learning (DL)-based IDS have already demonstrated their detection efficiency, they still suffer from false alarms and explainability issues that do not allow security administrators to trust them completely compared to conventional signature/specification-based IDS. In light of the aforementioned remarks, in this paper, we introduce an AI-powered IDS with explainability functions for the IoT. The proposed IDS relies on ML and DL methods, while the SHapley Additive exPlanations (SHAP) method is used to explain decision-making. The evaluation results demonstrate the efficiency of the proposed IDS in terms of detection performance and explainable AI (XAI)

Hunting IoT Cyberattacks With AI - Powered Intrusion Detection

The rapid progression of the Internet of Things allows the seamless integration of cyber and physical environments, thus creating an overall hyper-connected ecosystem. It is evident that this new reality provides several capabilities and benefits, such as real-time decision-making and increased efficiency and productivity. However, it also raises crucial cybersecurity issues that can lead to disastrous consequences due to the vulnerable nature of the Internet model and the new cyber risks originating from the multiple and heterogeneous technologies involved in the loT. Therefore, intrusion detection and prevention are valuable and necessary mechanisms in the arsenal of the loT security. In light of the aforementioned remarks, in this paper, we introduce an Artificial Intelligence (AI)-powered Intrusion Detection and Prevention System (IDPS) that can detect and mitigate potential loT cyberattacks. For the detection process, Deep Neural Networks (DNNs) are used, while Software Defined Networking (SDN) and Q-Learning are combined for the mitigation procedure. The evaluation analysis demonstrates the detection efficiency of the proposed IDPS, while Q- Learning converges successfully in terms of selecting the appropriate mitigation action

Semi-grant-free non-orthogonal multiple access for tactile Internet of Things

Ultra-low latency connections for a massive number of devices are one of the main requirements of the next-generation tactile Internet-of-Things (TIoT). Grant-free non-orthogonal multiple access (GF-NOMA) is a novel paradigm that leverages the advantages of grant-free access and non-orthogonal transmissions, to deliver ultra-low latency connectivity. In this work, we present a joint channel assignment and power allocation solution for semi-GF-NOMA systems, which provides access to both grant-based (GB) and grant-free (GF) devices, maximizes the network throughput, and is capable of ensuring each device’s throughput requirements. In this direction, we provide the mathematical formulation of the aforementioned problem. After explaining that it is not convex, we propose a solution strategy based on the Lagrange multipliers and subgradient method. To evaluate the performance of our solution, we carry out system-level Monte Carlo simulations. The simulation results indicate that the proposed solution can optimize the total system throughput and achieve a high association rate, while taking into account the minimum throughput requirements of both GB and GF devices

Testing hypotheses about the harm that capitalism causes to the mind and brain: a theoretical framework for neuroscience research

In this paper, we will attempt to outline the key ideas of a theoretical framework for neuroscience research that reflects critically on the neoliberal capitalist context. We argue that neuroscience can and should illuminate the effects of neoliberal capitalism on the brains and minds of the population living under such socioeconomic systems. Firstly, we review the available empirical research indicating that the socio-economic environment is harmful to minds and brains. We, then, describe the effects of the capitalist context on neuroscience itself by presenting how it has been influenced historically. In order to set out a theoretical framework that can generate neuroscientific hypotheses with regards to the effects of the capitalist context on brains and minds, we suggest a categorization of the effects, namely deprivation, isolation and intersectional effects. We also argue in favor of a neurodiversity perspective [as opposed to the dominant model of conceptualizing neural (mal-)functioning] and for a perspective that takes into account brain plasticity and potential for change and adaptation. Lastly, we discuss the specific needs for future research as well as a frame for post-capitalist research