Scalable playback rate control in P2P live streaming systems

Current commercial live video streaming systems are based either on a typical client–server (cloud) or on a peer-to-peer (P2P) architecture. The former architecture is preferred for stability and QoS, provided that the system is not stretched beyond its bandwidth capacity, while the latter is scalable with small bandwidth and management cost. In this paper, we propose a P2P live streaming architecture in which by adapting dynamically the playback rate we guarantee that peers receive the stream even in cases where the total upload bandwidth changes very abruptly. In order to achieve this we develop a scalable mechanism that by probing only a small subset of peers monitors dynamically the total available bandwidth resources and a playback rate control mechanism that dynamically adapts playback rate to the aforementioned resources. We model analytically the relationship between the playback rate and the available bandwidth resources by using difference equations and in this way we are able to apply a control theoretical approach. We also quantify monitoring inaccuracies and dynamic bandwidth changes and we calculate dynamically, as a function of these, the maximum playback rate for which the proposed system able to guarantee the uninterrupted and complete distribution of the stream. Finally, we evaluate the control strategy and the theoretical model in a packet level simulator of a complete P2P live streaming system that we designed in OPNET Modeler. Our evaluation results show the uninterrupted and complete stream delivery (every peer receives more than 99 % of video blocks in every scenario) even in very adverse bandwidth changes

PHOENI2X -- A European Cyber Resilience Framework With Artificial-Intelligence-Assisted Orchestration, Automation and Response Capabilities for Business Continuity and Recovery, Incident Response, and Information Exchange

As digital technologies become more pervasive in society and the economy, cybersecurity incidents become more frequent and impactful. According to the NIS and NIS2 Directives, EU Member States and their Operators of Essential Services must establish a minimum baseline set of cybersecurity capabilities and engage in cross-border coordination and cooperation. However, this is only a small step towards European cyber resilience. In this landscape, preparedness, shared situational awareness, and coordinated incident response are essential for effective cyber crisis management and resilience. Motivated by the above, this paper presents PHOENI2X, an EU-funded project aiming to design, develop, and deliver a Cyber Resilience Framework providing Artificial-Intelligence-assisted orchestration, automation and response capabilities for business continuity and recovery, incident response, and information exchange, tailored to the needs of Operators of Essential Services and the EU Member State authorities entrusted with cybersecurity

Proposing a service-enabled semantic grid model

The semantic grid refers to an approach to grid computing in which information, computing resources and services are described in standard ways that can be processed by computer. This makes it easier for resources to be discovered and joined up automatically. Because semantic grids represent and reason about knowledge declaratively, additional capabilities of typical agents are then possible including learning, planning, self-repair, memory organisation, meta-reasoning and task-level coordination. Only a convergence of these technologies will provide the ingredients to create fabric for a new generation of distributed intelligent systems. Inspired from the concept of autonomous decentralised systems, we propose that the above-mentioned goals can be achieved by integrating FIPA multiagent systems with the grid service architecture and hence to lay the foundation for semantic grid. Semantic grid system architecture is aimed to provide an improved infrastructure by bringing autonomy, semantic interoperability and decentralisation in the grid computing for the emerging applications

End Node Security and Trust vulnerabilities in the Smart City Infrastructure

As cities gradually introduce intelligence in their core services and infrastructure thus becoming “smart cities”, they are deploying new Information Technology devices in the urban grid that are interconnected to a broad network. The main focus of widely implemented smart cities' services was the operation of sensors and smart devices across city areas that need low energy consumption and high connectivity. However, as 5G technologies are gradually been adopted in the smart city infrastructure thus solving that problem, the fundamental issue of addressing security becomes dominant. While latest network topologies and standards include security functions thus giving an illusion of security, there is little focus on the fact that many smart city end nodes cannot realize all security specifications without additional help. In this paper, we discuss briefly smart city security issues and focus on problem and security requirement that need to be address in the smart city end nodes, the sensors and actuators deployed within the city's grid. In this paper, attacks that cannot be thwarted by traditional cybersecurity solutions are discussed and countermeasures based on hardware are suggested in order to achieve a high level of trust. Also, the danger of microarchitectural and side channel attacks on these devices is highlighted and protection approaches are discussed

Machine Learning Attacks and Countermeasures on Hardware Binary Edwards Curve Scalar Multipliers

Machine Learning techniques have proven effective in Side Channel Analysis (SCA), enabling multiple improvements over the already-established profiling process of Template Attacks. Focusing on the need to mitigate their impact on embedded devices, a design model and strategy is proposed that can effectively be used as a backbone for introducing SCA countermeasures on Elliptic Curve Cryptography (ECC) scalar multipliers. The proposed design strategy is based on the decomposition of the round calculations of the Montgomery Power Ladder (MPL) algorithm and the Scalar Multiplication (SM) algorithm into the underlined finite field operations, and their restructuring into parallel-processed operation sets. Having as a basis the proposed design strategy, we showcase how advanced SCA countermeasures can be easily introduced, focusing on randomizing the projective coordinates of the MPL round’s ECC point results. To evaluate the design approach and its SCA countermeasures, several simple ML-based SCAs are performed, and an attack roadmap is provided. The proposed roadmap assumes attackers that do not have access to a huge number of leakage traces, and that have limited resources with which to mount Deep Learning attacks. The trained models’ performance reveals a high level of resistance against ML-based SCAs when including SCA countermeasures in the proposed design strategy