Data security and quality evaluation framework: Implementation empirical study on android devices

Tremendous growth of a number of mobile devices and amount of data produced by sensors embedded therein requires new approaches to sensor data management. The main feature of the novel approach proposed in this paper includes an assignment of security and data quality indicators to data entities. These indicators represent the trustworthiness level, which a data consumer may have. Employing them as filters would allow for an optimization of diverse sensors data processing and fusing with a significant reduction in data volumes. The paper describes the developed comprehensive methodology that resulted in the evaluation framework. Framework merges together sensor data collection and security and quality evaluation methods as well as procedures for calculating various data quality metrics such as sensor accuracy, reliability, timeliness, correctness and their integration. The paper desribes main features of this framework and examples of its implementation on Android based smartphone devices. It presents the results of an empirical study of the framework implementation and discusses its application for an anomaly detection in sensor data

Privacy and Security Framework. OpenIoT Deliverable D522

This deliverable describes the Security and Privacy Framework of the OpenIoT platform, including details about its design and implementation. The aim of this framework is to ensure that Internet-Connected Objects (ICOs) contributing to the OpenIoT platform, its internal modules and external applications will communicate through secured IoT data interfaces (according to the target security/confidentiality level specified by the user). Moreover we show the feasibility of this security module in the implemented prototype, which is an integral part of the OpenIoT platform. In particular we describe the implementation of the Central Authorisation Server (CAS), the Security Management console, the Security Client, and the integration of the security framework in the core modules of the platform

A Game-Theoretic Approach for High-Assurance of Data Trustworthiness in Sensor Networks

Sensor networks are being increasingly deployed in many application domains ranging from environment monitoring to supervising critical infrastructure systems (e.g., the power grid). Due to their ability to continuously collect large amounts of data, sensor networks represent a key component in decision-making, enabling timely situation assessment and response. However, sensors deployed in hostile environments may be subject to attacks by adversaries who intend to inject false data into the system. In this context, {\em data trustworthiness} is an important concern, as false readings may result in wrong decisions with serious consequences (e.g., large-scale power outages). To defend against this threat, it is important to establish trust levels for sensor nodes and adjust node trustworthiness scores to account for malicious interferences. In this paper, we develop a game-theoretic defense strategy to protect sensor nodes from attacks and to guarantee a high level of trustworthiness for sensed data. We use a discrete time model, and we consider that there is a limited attack budget that bounds the capability of the attacker in each round. The defense strategy objective is to ensure that sufficient sensor nodes are protected in each round such that the discrepancy between the value accepted and the truthful sensed value is below a certain threshold. We model the attack-defense interaction as a Stackel berg game, and we derive the Nash equilibrium condition that is sufficient to ensure that the sensed data are truthful within a nominal error bound. We implement a prototype of the proposed strategy and we show through extensive experiments that our solution provides an effective and efficient way of protecting sensor networks from attacks