Information-Centric Networking (ICN) has arisen as an architectural solution that responds to the needs of today’s overloaded Internet, departing from the traditional host-centric access paradigm. In this paper we focus on Named Data Networking (NDN), the most prominent ICN architecture. In the NDN framework, disseminated content is at the core of the design and providing trusted content is essential. In this paper, we provide an overview of reputation-based trust approaches, present their design trade-offs and argue that these approaches can consolidate NDN trust and security by working complementary to the existing credential-based schemes. Finally, we discuss future research directions and challenges.

Anomaly detection (AD) has been recently employed in the context of edge cloud computing, e.g., for intrusion detection and identification of performance issues. However, state-of-the-art anomaly detection procedures do not systematically consider restrictions and performance requirements inherent to the edge, such as system responsiveness and resource consumption. In this paper, we attempt to investigate the performance of change-point based detectors, i.e., a class of lightweight and accurate AD methods, in relation to the requirements of edge cloud systems. Firstly, we review the theoretical properties of two major categories of change point approaches, i.e., Bayesian and cumulative sum (CUSUM), also discussing their suitability for edge systems. Secondly, we introduce a novel experimental methodology and apply it over two distinct edge cloud test-beds to evaluate the performance of such mechanisms in real-world edge environments. Our experimental results reveal important insights and trade-offs for the applicability and the online performance of the selected change point detectors.