Biblio
When robots and human users collaborate, trust is essential for user acceptance and engagement. In this paper, we investigated two factors thought to influence user trust towards a robot: preference elicitation (a combination of user involvement and explanation) and embodiment. We set our experiment in the application domain of a restaurant recommender system, assessing trust via user decision making and perceived source credibility. Previous research in this area uses simulated environments and recommender systems that present the user with the best choice from a pool of options. This experiment builds on past work in two ways: first, we strengthened the ecological validity of our experimental paradigm by incorporating perceived risk during decision making; and second, we used a system that recommends a nonoptimal choice to the user. While no effect of embodiment is found for trust, the inclusion of preference elicitation features significantly increases user trust towards the robot recommender system. These findings have implications for marketing and health promotion in relation to Human-Robot Interaction and call for further investigation into the development and maintenance of trust between robot and user.
Common vulnerability scoring system (CVSS) is an industry standard that can assess the vulnerability of nodes in traditional computer systems. The metrics computed by CVSS would determine critical nodes and attack paths. However, traditional IT security models would not fit IoT embedded networks due to distinct nature and unique characteristics of IoT systems. This paper analyses the application of CVSS for IoT embedded systems and proposes an improved vulnerability scoring system based on CVSS v3 framework. The proposed framework, named CVSSIoT, is applied to a realistic IT supply chain system and the results are compared with the actual vulnerabilities from the national vulnerability database. The comparison result validates the proposed model. CVSSIoT is not only effective, simple and capable of vulnerability evaluation for traditional IT system, but also exploits unique characteristics of IoT devices.
A wide variety of security software systems need to be integrated into a Security Orchestration Platform (SecOrP) to streamline the processes of defending against and responding to cybersecurity attacks. Lack of interpretability and interoperability among security systems are considered the key challenges to fully leverage the potential of the collective capabilities of different security systems. The processes of integrating security systems are repetitive, time-consuming and error-prone; these processes are carried out manually by human experts or using ad-hoc methods. To help automate security systems integration processes, we propose an Ontology-driven approach for Security OrchestrAtion Platform (OnSOAP). The developed solution enables interpretability, and interoperability among security systems, which may exist in operational silos. We demonstrate OnSOAP's support for automated integration of security systems to execute the incident response process with three security systems (Splunk, Limacharlie, and Snort) for a Distributed Denial of Service (DDoS) attack. The evaluation results show that OnSOAP enables SecOrP to interpret the input and output of different security systems, produce error-free integration details, and make security systems interoperable with each other to automate and accelerate an incident response process.
A process of critical transmission lines identification in presented here. The criticality is based on network flow, which is essential for power grid connectivity monitoring as well as vulnerability assessment. The proposed method can be utilized as a supplement of traditional situational awareness tool in the energy management system of the power grid control center. At first, a flow network is obtained from topological as well as functional features of the power grid. Then from the duality property of a linear programming problem, the maximum flow problem is converted to a minimum cut problem. Critical transmission lines are identified as a solution of the dual problem. An overall set of transmission lines are identified from the solution of the network flow problem. Simulation of standard IEEE test cases validates the application of the method in finding critical transmission lines of the power grid.
This paper proposes a novel privacy-preserving smart metering system for aggregating distributed smart meter data. It addresses two important challenges: (i) individual users wish to publish sensitive smart metering data for specific purposes, and (ii) an untrusted aggregator aims to make queries on the aggregate data. We handle these challenges using two main techniques. First, we propose Fourier Perturbation Algorithm (FPA) and Wavelet Perturbation Algorithm (WPA) which utilize Fourier/Wavelet transformation and distributed differential privacy (DDP) to provide privacy for the released statistic with provable sensitivity and error bounds. Second, we leverage an exponential ElGamal encryption mechanism to enable secure communications between the users and the untrusted aggregator. Standard differential privacy techniques perform poorly for time-series data as it results in a Θ(n) noise to answer n queries, rendering the answers practically useless if n is large. Our proposed distributed differential privacy mechanism relies on Gaussian principles to generate distributed noise, which guarantees differential privacy for each user with O(1) error, and provides computational simplicity and scalability. Compared with Gaussian Perturbation Algorithm (GPA) which adds distributed Gaussian noise to the original data, the experimental results demonstrate the superiority of the proposed FPA and WPA by adding noise to the transformed coefficients.
Blockchain is an emerging technology for decentralized and transactional data sharing across a large network of untrusted participants. It enables new forms of distributed software architectures, where components can find agreements on their shared states without trusting a central integration point or any particular participating components. Considering the blockchain as a software connector helps make explicitly important architectural considerations on the resulting performance and quality attributes (for example, security, privacy, scalability and sustainability) of the system. Based on our experience in several projects using blockchain, in this paper we provide rationales to support the architectural decision on whether to employ a decentralized blockchain as opposed to other software solutions, like traditional shared data storage. Additionally, we explore specific implications of using the blockchain as a software connector including design trade-offs regarding quality attributes.