Biblio
Multi-party and multi-layer nature of 5G networks implies the inherent distribution of management and orchestration decisions across multiple entities. Therefore, responsibility for management decisions concerning end-to-end services become blurred if no efficient liability and accountability mechanism is used. In this paper, we present the design, building blocks and challenges of a Liability-Aware Security Management (LASM) system for 5G. We describe how existing security concepts such as manifests and Security-by-Contract, root cause analysis, remote attestation, proof of transit, and trust and reputation models can be composed and enhanced to take risk and responsibilities into account for security and liability management.
Nowadays, trust and reputation models are used to build a wide range of trust-based security mechanisms and trust-based service management applications on the Internet of Things (IoT). Considering trust as a single unit can result in missing important and significant factors. We split trust into its building-blocks, then we sort and assign weight to these building-blocks (trust metrics) on the basis of its priorities for the transaction context of a particular goal. To perform these processes, we consider trust as a multi-criteria decision-making problem, where a set of trust worthiness metrics represent the decision criteria. We introduce Entropy-based fuzzy analytic hierarchy process (EFAHP) as a trust model for selecting a trustworthy service provider, since the sense of decision making regarding multi-metrics trust is structural. EFAHP gives 1) fuzziness, which fits the vagueness, uncertainty, and subjectivity of trust attributes; 2) AHP, which is a systematic way for making decisions in complex multi-criteria decision making; and 3) entropy concept, which is utilized to calculate the aggregate weights for each service provider. We present a numerical illustration in trust-based Service Oriented Architecture in the IoT (SOA-IoT) to demonstrate the service provider selection using the EFAHP Model in assessing and aggregating the trust scores.
In VANET, Sybil nodes generated by attackers cause serious damages to network protocols, resource allocation mechanisms, and reputation models. Other types of attacks can also be launched on the basis of Sybil attack, which bring more threats to VANET. To solve this problem, this paper proposes a Sybil nodes detection method based on RSSI sequence and vehicle driving matrix - RSDM. RSDM evaluates the difference between the RSSI sequence and the driving matrix by dynamic distance matching to detect Sybil nodes. Moreover, RSDM does not rely on VANET infrastructure, neighbor nodes or specific hardware. The experimental results show that RSDM performs well with a higher detection rate and a lower error rate.