Security assessment framework for IoT service
207250443
Security assessment framework for IoT service
What are the critical requirements to be considered for the security measures in Internet of Things (IoT) services? Further, how should those security resources be allocated? To provide valuable insight into these questions, this paper introduces a security assessment framework for the IoT service environment from an architectural perspective. Our proposed framework integrates fuzzy DEMATEL and fuzzy ANP to reflect dependence and feedback interrelations among security criteria and, ultimately, to weigh and prioritize them. The results, gleaned from the judgments of 38 security experts, revealed that security design should put more importance on the service layer, especially to ensure availability and trust. We believe that these results will contribute to the provision of more secure and reliable IoT services.
Telecommun. Syst.
Keon Chul Park Donghee Shin
216017415
Security assessment framework for IoT service
What are the critical requirements to be considered for the security measures in Internet of Things (IoT) services? Further, how should those security resources be allocated? To provide valuable in.
ParkKeon Chul
37660690
Erratum to: Security assessment framework for IoT service
Telecommun. Syst.
Keon Chul Park Donghee Don Shin
59555430
Enhancing Cloud Based IoT Security Through Trustworthy Cloud Service: An Integration of Security and Reputation Approach
The Internet of Things (IoT) provides a new paradigm for the development of heterogeneous and distributed systems, and it has increasingly become a ubiquitous computing service platform. However, due to the lack of sufficient computing and storage resources dedicated to the processing and storage of huge volumes of the IoT data, it tends to adopt a cloud based architecture to address the issues of resource constraints. Hence, a series of challenging security and trust concerns have arisen in the cloud based IoT context. To this end, a novel trust assessment framework for the security and reputation of cloud services is proposed. This framework enables the trust evaluation of cloud services in order to ensure the security of the cloud based IoT context via integrating security and reputation based trust assessment methods. The security based trust assessment method employs the cloud specific security metrics to evaluate the security of a cloud service. Furthermore, the feedback ratings on the quality of cloud service are exploited in the reputation based trust assessment method in order to evaluate the reputation of a cloud service. The experiments conducted using a synthesized dataset of security metrics and a real world web service dataset show that our proposed trust assessment framework can efficiently and effectively assess the trustworthiness of a cloud service while outperforming other trust assessment methods.
IEEE Access
Xiang Li Qixu Wang Xiao Lan Xingshu Chen Ning Zhang Dajiang Chen
53293774
SCCAF: A Secure and Compliant Continuous Assessment Framework in Cloud Based IoT Context
The Internet of Things (IoT) offers a wide variety of benefits to our daily lives in many ways, ranging from smart wearable devices to industrial systems. However, it also brings well known security and compliance concerns, especially in the physical layer. In addition, due to numerous IoT architectures which have been developed and deployed based on the cloud, the security and compliance of IoT depend on the cloud thoroughly. In this paper, a secure and compliant continuous assessment framework (SCCAF) is proposed to evaluate the security and compliance levels of cloud services in life cycle. The SCCAF facilitates cloud service to customers to select an optimal cloud service provider (CSP) which satisfies their desired security requirements. Moreover, it also enables cloud service customers to evaluate the compliance of the selected CSP in the process of using cloud services. To evaluate the performance and availability of SCCAF, we carry out a series of experiments with case study and real world scenario datasets. Experimental results show that SCCAF can assess the security and compliance of CSPs efficiently and effectively.
Wirel. Commun. Mob. Comput.
Xiang Lorraine Li Xin Jin Qixu Wang Mingsheng Cao Xingshu Chen
209404957
COBRA: Context aware Bernoulli Neural Networks for Reputation Assessment
Trust and reputation management (TRM) plays an increasingly important role in large scale online environments such as multi agent systems (MAS) and the Internet of Things (IoT) One main objective of TRM is to achieve accurate trust assessment of entities such as agents or IoT service providers. However, this encounters an accuracy privacy dilemma as we identify in this paper, and we propose a framework called Context aware Bernoulli Neural Network based Reputation Assessment (COBRA) to address this challenge. COBRA encapsulates agent interactions or transactions, which are prone to privacy leak, in machine learning models, and aggregates multiple such models using a Bernoulli neural network to predict a trust score for an agent. COBRA preserves agent privacy and retains interaction contexts via the machine learning models, and achieves more accurate trust prediction than a fully connected neural network alternative. COBRA is also robust to security attacks by agents who inject fake machine learning models; notably, it is resistant to the 51 percent attack. The performance of COBRA is validated by our experiments using a real dataset, and by our simulations, where we also show that COBRA outperforms other state of the art TRM systems.
AAAI
Leonit Zeynalvand Tie Luo Jie Zhang
12199828
From Privacy Impact Assessment to Social Impact Assessment
In order to address the continued decline in consumer trust in all things digital, and specifically the Internet of Things (IoT) we propose a radical overhaul of IoT design processes. Privacy by Design has been proposed as a suitable framework, but we argue the current approach has two failings: it presents too abstract a framework to inform design, and it is often applied after many critical design decisions have been made in defining the business opportunity. To rebuild trust we need the philosophy of Privacy by Design to be transformed into a wider Social Impact Assessment and delivered with practical guidance to be applied at product/service concept stage as well as throughout the system's engineering.
2016 IEEE Security and Privacy Workshops (SPW)
Lilian Edwards Derek McAuley Laurence Diver
52162600
Quality of Information as an indicator of Trust in the Internet of Things
The past decade has seen a rise in complexity and scale of software systems, particularly with the emerging of the Internet of Thing consisting of large scale and heterogeneous entities which results in difficulties in providing trustworthy services. To overcome such challenges, providing high quality information for IoT service provider as well as monitoring trust relationship of end users toward the services are paramount. Such trust relationships are user oriented and subjective phenomenon that hook on specific quality of data. Following this catalyst, we propose a mechanism to evaluate quality of information (QoI) for streaming data from sensor device; then use the QoI evaluation score as an indicator of trust. Concepts and assessment methodology for QoI along with a trust monitoring system are described. We also develop a framework that classifies streaming of data based on semantic context and generate QoI score as a relevant input for a trust monitoring component. This framework enables a dynamic trust management in the context of IoT for both end users and services that empowers service providers to deliver trustworthy and high quality IoT services. Challenges encountered during implementation and contribution in standardization are discussed. We believe this paper offers better understanding on QoI and its importance in trust evaluation in IoT applications; also provides detailed implementation of the QoI and Trust components for real world applications and services.
2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE)
Hamza Baqa Nguyen Binh Truong Noel Crespi Gyu Myoung Lee Franck Le Gall
1660208
Multi layer cloud architectural model and ontology based security service framework for IoT based smart homes
Abstract The Smart Home concept, associated with the pervasiveness of network coverage and embedded computing technologies is assuming an ever growing significance for people living in the highly developed areas. However, the heterogeneity of devices, services, communication protocols, standards and data formats involved in most of the available solutions developed by different vendors, is adversely affecting its widespread application. In this paper, promoted by several promising opportunities provided by the advances in Internet of Things (IoT) and Cloud Computing technologies for facing these challenges, a novel multi layer cloud architectural model is developed to enable effective and seamless interactions/interoperations on heterogeneous devices/services provided by different vendors in IoT based smart home. In addition, to better solve the heterogeneity issues in the presented layered cloud platform, ontology has been used as a promising way to address data representation, knowledge, and application heterogeneity, and an ontology based security service framework is designed for supporting security and privacy preservation in the process of interactions/interoperations. Challenges and directions for future work on smart home management have been also discussed at the end of this paper.
Future Gener. Comput. Syst.
Ming Tao Jinglong Zuo Zhusong Liu Aniello Castiglione Francesco Palmieri
219460364
A Machine Learning Security Framework for Iot Systems
Internet of Things security is attracting a growing attention from both academic and industry communities. Indeed, IoT devices are prone to various security attacks varying from Denial of Service (DoS) to network intrusion and data leakage. This paper presents a novel machine learning (ML) based security framework that automatically copes with the expanding security aspects related to IoT domain. This framework leverages both Software Defined Networking (SDN) and Network Function Virtualization (NFV) enablers for mitigating different threats. This AI framework combines monitoring agent and AI based reaction agent that use ML Models divided into network patterns analysis, along with anomaly based intrusion detection in IoT systems. The framework exploits the supervised learning, distributed data mining system and neural network for achieving its goals. Experiments results demonstrate the efficiency of the proposed scheme. In particular, the distribution of the attacks using the data mining approach is highly successful in detecting the attacks with high performance and low cost. Regarding our anomaly based intrusion detection system (IDS) for IoT, we have evaluated the experiment in a real Smart building scenario using one class SVM. The detection accuracy of anomalies achieved 99.71% A feasibility study is conducted to identify the current potential solutions to be adopted and to promote the research towards the open challenges.
IEEE Access
Miloud Bagaa Tarik Taleb Jorge Bernal Bernabe Antonio F Skarmeta