An Overview of Risk Estimation Techniques in Risk-based Access Control for the Internet of Things <s> INTRODUCTION <s> A reversible, side mounted rearview mirror for vehicles adapted to be mounted at the exterior of the vehicle and having opposed reflective surfaces one of which is suitable for daytime use and the other which is suitable for nighttime use. The mirror is supported within the housing for rotation about a horizontal axis and for swinging movement about a vertical axis at one end of the mirror to provide a full range of adjustment and to permit selecting one or the other of the reflective surfaces. <s> BIB001 </s> An Overview of Risk Estimation Techniques in Risk-based Access Control for the Internet of Things <s> INTRODUCTION <s> This paper presents a new model for, or rather a new way of thinking about adaptive, risk–based access control. Our basic premise is that there is always inherent uncertainty and risk in access control decisions that is best addressed in an explicit way. We illustrate this concept by showing how the rationale of the well–known, Bell–Lapadula model based, Multi–Level Security (MLS) access control model could be used to develop a risk-adaptive access control model. This new model is more like a Fuzzy Logic control system [9] than a traditional access control system and hence the name “Fuzzy MLS”. The long version of this paper is published as an IBM Research Report [3]. <s> BIB002 </s> An Overview of Risk Estimation Techniques in Risk-based Access Control for the Internet of Things <s> INTRODUCTION <s> A statistical method is proposed for quantifying the impact of factors that influence the quality of the estimation of costs for IT-enabled business projects. We call these factors risk drivers as they influence the risk of the misestimation of project costs. The method can effortlessly be transposed for usage on other important IT key performance indicators (KPIs), such as schedule misestimation or functionality underdelivery. We used logistic regression as a modeling technique to estimate the quantitative impact of risk factors. We did so because logistic regression has been applied successfully in fields including medical science, e.g. in perinatal epidemiology, to answer questions that show a striking resemblance to the questions regarding project risk management. In our study we used data from a large organization in the financial services industry to assess the applicability of logistic modeling in quantifying IT risks. With this real-world example we illustrated how to scrutinize the quality and plausibility of the available data. We explained how to deal with factors that cannot be influenced, also called risk factors, by project management before or in the early stage of a project, but can have an influence on the outcome of the estimation process. We demonstrated how to select the risk drivers using logistic regression. Our research has shown that it is possible to properly quantify these risks, even with the help of crude data. We discussed the interpretation of the models found and showed that the findings are helpful in decision making on measures to be taken to identify potential misestimates and thus mitigate IT risks for individual projects. We proposed increasing the auditing process efficiency by using the found cost misestimation models to classify all projects as either risky projects or non-risky projects. We discovered through our analyses that projects must not be overstaffed and the ratio of external developers must be kept small to obtain better cost estimates. Our research showed that business units that report on financial information tend to be risk mitigating, because they have more cost underruns in comparison with business units without reporting; the latter have more cost overruns. We also discovered a maturity mismatch: an increase from CMM level 1 to 2 did not influence the disparity between a cost estimate and its actual if the maturity of the business is not also increased. <s> BIB003 </s> An Overview of Risk Estimation Techniques in Risk-based Access Control for the Internet of Things <s> INTRODUCTION <s> In traditional multi-level security systems, trust and risk values are pre-computed. Any change in these values requires manual intervention of an administrator. In many dynamic environments, however, these values should be auto-adaptive, and auto-tunable according to the usage history of the users. Moreover, occasional exceptions on resource needs, which are common in dynamic environments like healthcare, should be allowed if the subjects show a positive record of use toward resources they acquired in the past. Conversely, access of authorized users, who have negative record, should be restricted. These requirements are not taken into consideration in existing risk-based access control systems. In order to overcome these shortcomings and to meet different sensitivity requirements of various applications, we propose two dynamic risk-based decision methods for access control systems. We provide theoretical and simulation-based analysis and evaluation of both schemes. Also, we analytically prove that the proposed methods, not only allow exceptions under certain controlled conditions, but uniquely restrict legitimate access of bad authorized users. <s> BIB004 </s> An Overview of Risk Estimation Techniques in Risk-based Access Control for the Internet of Things <s> INTRODUCTION <s> Cloud Computing is already a successful paradigm for distributed computing and is still growing in popularity. However, many problems still linger in the application of this model and some new ideas are emerging to help leverage its features even further. One of these ideas is the cloud federation, which is a way of aggregating different clouds to enable the sharing of resources and increase scalability and availability. One of the great challenges in the deployment of cloud federations is Identity and Access Management. This issue is usually solved by the creation of identity federations, but this approach is not optimal. In this paper, we propose an access control system for a highly scalable cloud federation. The presented system is dynamic and risk-based, allowing the use of cloud federations without the need of identity federations. We also present results of a prototype implementation and show that it is scalable and flexible enough to meet the requirements of this highly dynamic and heterogeneous environment. <s> BIB005 </s> An Overview of Risk Estimation Techniques in Risk-based Access Control for the Internet of Things <s> INTRODUCTION <s> Cloud computing is a distributed computing model that still faces problems. New ideas emerge to take advantage of its features and among the research challenges found in the cloud, we can highlight Identity and Access Management. The main problems of the application of access control in the cloud are the necessary flexibility and scalability to support a large number of users and resources in a dynamic and heterogeneous environment, with collaboration and information sharing needs. This paper proposes the use of risk-based dynamic access control for cloud computing. The proposal is presented as an access control model based on an extension of the XACML standard with three new components: the Risk Engine, the Risk Quantification Web Services and the Risk Policies. The risk policies present a method to describe risk metrics and their quantification, using local or remote functions. The risk policies allow users and cloud service providers to define how to handle risk-based access control for their resources, using different quantification and aggregation methods. The model reaches the access decision based on a combination of XACML decisions and risk analysis. A prototype of the model is implemented, showing it has enough expressivity to describe the models of related work. In the experimental results, the prototype takes between 2 and 6 milliseconds to reach access decisions using a risk policy. A discussion on the security aspects of the model is also presented. <s> BIB006 </s> An Overview of Risk Estimation Techniques in Risk-based Access Control for the Internet of Things <s> INTRODUCTION <s> Today, smart objects in Internet of Things (IOT) are able to detect their state and share it with other objects across the Internet, thus collaboratively making intelligent decisions on their own. Large number of objects is quietly introduced into the web every day. It is impossible for a user to keep a track of all objects available around him. Therefore a search engine that could find smart objects is used. If an exact object yielding the required service is not present then system requires explicit user inputs to find alternatives such as name of an alternate object that may provide the same service. Humans always find alternatives around them to carry out their work smoothly. Service provisioning in IOT should also be made capable of providing similar or alternate objects that are aligned with user requirements, current context and previous knowledge without any human intervention. We propose an architecture that uses Naive Bayesian prediction on user history and Ant Colony Optimization for accurately predicting objects of user interest so as to reduce the dependency of IOT application on human inputs for finding an object match. <s> BIB007
The Internet of Things (IoT) is growing in different ways. It is considered the next stage of the evolution of the Internet. In addition, the IoT is moving towards a stage in which all items around us will be connected to the Internet and will have the ability to communicate with each other with the minimum human intervention BIB007 . The IoT faces many challenges that stand as a barrier to the successful implementation of IoT applications. Security and privacy are considered the most difficult challenges that need to be addressed. These challenges are complicated due to the dynamic and heterogeneous nature of the IoT system BIB004 ; Ricardo dos BIB005 . Authentication and access control models are the essential elements to address security and privacy challenges in the IoT. They prevent unauthorized users from gaining access to system resources, prevent authorized users from accessing resources in an unauthorized manner whilst still allowing authorized users to access resources in an authorized manner BIB002 BIB001 . Due to the dynamic nature of the IoT, traditional access control approaches cannot provide the necessary security levels as they are context insensitive and require a complex authentication infrastructure. Dynamic access control approaches are more appropriate to IoT systems. This is because they are characterized by using not only the policies but also environment features that are estimated in real-time to determine access decisions. The dynamic features can include trust, risk, context, history and operational need BIB003 Ni et al., 2010) . A risk-based access control model is one of the dynamic models that permit or deny access requests dynamically based on the estimated risk of each access request BIB002 . This model performs a risk analysis on each user access request to make the access decision (Dos BIB006 . Risk estimation process is one of the primary tasks in the risk-based access control model. This process is concerned with estimating the risks that have been specified and creating the data that will be needed for making decisions. The objective of the estimation process is to establish a way of arranging risks in the order of importance and using the risk numeric values for making the decision BIB001 . The main objective of this paper is to provide a review of different risk estimation techniques that have been used in existing risk-based access control models. Furthermore, discussing requirements for choosing the appropriate risk estimation technique for the IoT system. The rest of this paper is organized as follows: Section II presents the concept of the access control and different access control models; Section III presents the risk-based access control model; Section IV presents different risk estimation techniques; Section V discusses the IoT requirements to determine the appropriate risk estimation technique, and Section VI is the conclusion 2.