Biblio

In recent years, technological advancements in the AI and VR fields have increasingly often been paired with considerations on ethics and safety aimed at mitigating unintentional design failures. However, cybersecurity-oriented AI and VR safety research has emphasized the need to additionally appraise instantiations of intentional malice exhibited by unethical actors at pre- and post-deployment stages. On top of that, in view of ongoing malicious deepfake developments that can represent a threat to the epistemic security of a society, security-aware AI and VR design strategies require an epistemically-sensitive stance. In this vein, this paper provides a theoretical basis for two novel AIVR safety research directions: 1) VR as immersive testbed for a VR-deepfake-aided epistemic security training and 2) AI as catalyst within a deepfake-aided so-called cyborgnetic creativity augmentation facilitating an epistemically-sensitive threat modelling. For illustration, we focus our use case on deepfake text – an underestimated deepfake modality. In the main, the two proposed transdisciplinary lines of research exemplify how AIVR safety to defend against unethical actors could naturally converge toward AIVR ethics whilst counteracting epistemic security threats.

Today's Internet is built on decades-old networking protocols that lack scalability, reliability, and security. In response, the networking community has developed path-aware Internet architectures that solve these issues while simultaneously empowering end hosts. In these architectures, autonomous systems construct authenticated forwarding paths based on their routing policies. Each end host then selects one of these authorized paths and includes it in the packet header, thus allowing routers to efficiently determine how to forward the packet. A central security property of these architectures is path authorization, requiring that packets can only travel along authorized paths. This property protects the routing policies of autonomous systems from malicious senders.The fundamental role of packet forwarding in the Internet and the complexity of the authentication mechanisms employed call for a formal analysis. In this vein, we develop in Isabelle/HOL a parameterized verification framework for path-aware data plane protocols. We first formulate an abstract model without an attacker for which we prove path authorization. We then refine this model by introducing an attacker and by protecting authorized paths using (generic) cryptographic validation fields. This model is parameterized by the protocol's authentication mechanism and assumes five simple verification conditions that are sufficient to prove the refinement of the abstract model. We validate our framework by instantiating it with several concrete protocols from the literature and proving that they each satisfy the verification conditions and hence path authorization. No invariants must be proven for the instantiation. Our framework thus supports low-effort security proofs for data plane protocols. The results hold for arbitrary network topologies and sets of authorized paths, a guarantee that state-of-the-art automated security protocol verifiers cannot currently provide.

The envisioned massive deployment of network slices in 5G and beyond mobile systems makes the shift towards zero-touch, scalable and secure slice lifecycle management a necessity. This is to harvest the benefits of network slicing in enabling profitable services. These benefits will not be attained without ensuring a high level security of the created network slices and the underlying infrastructure, above all in a zero-touch automated fashion. In this vein, this paper presents the architecture of an innovative network slicing security orchestration framework, being developed within the EU H2020 MonB5G project. The framework leverages the potential of Security as a Service (SECaaS) and Artificial Intelligence (AI) to foster fully-distributed, autonomic and fine-grained management of network slicing security from the node level to the end-to-end and inter-slice levels.

Modern vehicles in Intelligent Transportation Systems (ITS) can communicate with each other as well as roadside infrastructure units (RSUs) in order to increase transportation efficiency and road safety. For example, there are techniques to alert drivers in advance about traffic incidents and to help them avoid congestion. Threats to these systems, on the other hand, can limit the benefits of these technologies. Securing ITS itself is an important concern in ITS design and implementation. In this paper, we provide a security model of ITS which extends the classic layered network security model with transportation security and information security, and gives a reference for designing ITS architectures. Based on this security model, we also present a classification of ITS threats for defense. Finally a proof-of-concept example with malicious nodes in an ITS system is also given to demonstrate the impact of attacks. We analyzed the threat of malicious nodes and their effects to commuters, like increasing toll fees, travel distances, and travel times etc. Experimental results from simulations based on Veins shows the threats will bring about 43.40% more total toll fees, 39.45% longer travel distances, and 63.10% more travel times.

To manage and handle the issues of physical security in the modern world, there is a dire need for a multilevel security system to ensure the safety of precious belongings that could be money, military equipment or medical life-saving drugs. Security locker solution is proposed which is a multiple layer security system consisting of various levels of authentication. In most cases, only relevant persons should have access to their precious belongings. The unlocking of the box is only possible when all of the security levels are successfully cleared. The five levels of security include entering of password on interactive GUI, thumbprint, facial recognition, speech pattern recognition, and vein pattern recognition. This project is unique and effective in a sense that it incorporates five levels of security in a single prototype with the use of cost-effective equipment. Assessing our security system, it is seen that security is increased many a fold as it is near to impossible to breach all these five levels of security. The Raspberry Pi microcomputers, handling all the traits efficiently and smartly makes it easy for performing all the verification tasks. The traits used involves checking, training and verifying processes with application of machine learning operations.