Smart home devices collect and transmit user data to smart home Trigger Action Platforms (TAPs) for processing and executing automation rules. However, this data can also be used to infer user activities or other sensitive information. In this paper, we propose PTAP, a privacy-preserving approach based on adversarial example attacks. PTAP injects targeted perturbations into time-series sensor data, effectively confounding potentially malicious TAP classifiers. Our approach significantly reduces the chance of user activity recognition for a malicious TAP while preserving the essential information for automation rule execution, thus safeguarding TAP utility. We evaluated PTAP using a real-world smart-home dataset and examined its effectiveness in preserving utility through the execution of various IoT applications. Our results demonstrate that PTAP effectively preserves user privacy (reducing the accuracy of a malicious classifier 91 to 6 percent) while maintaining automation rule integrity, providing a practical and effective solution to protect user privacy in smart-home environments.

To improve the quality of Android apps, developers use automated debugging and testing solutions to determine whether the previously found crashes are reproducible. However, existing GUI fuzzing solutions for Android apps struggle to reproduce crashes efficiently based solely on a crash stack trace. This trace provides the location in the app where the crash occurs. GUI fuzzing solutions currently in use rely on heuristics to generate UI events. Unfortunately, these events often do not align with the investigation of an app's UI event space to reach a specific location of code. Hence, they generate numerous events unrelated to the crash, leading to an event explosion. To address this issue, a precise static UI model of widgets and screens can greatly enhance the efficiency of a fuzzing tool in its search. Building such a model requires considering all possible combinations of event sequences on widgets since the execution order of events is not statically determined. However, this approach presents scalability challenges in complex apps with several widgets. In this paper, we propose a directed-based fuzzing solution to reduce an app's event domain to the necessary ones to trigger a crash. Our insight is that the dependencies between widgets in their visual presentation and attribute states provide valuable information in precisely identifying events that trigger a crash. We propose an attribute-sensitive reachability analysis (ASRA) to track dependent widgets in reachable paths to the crash point and distinguish between events in terms of their relevancy to be generated in the crash reproduction process. With instrumentation, we inject code to prune irrelevant events, reducing the event domain to search at run time. We used four famous fuzzing tools, Monkey, Ape, Stoat, and FastBot2, to assess the impact of our solution in decreasing the crash reproduction time and increasing the possibility of reproducing a crash. Our results show that the success ratio of reproducing a crash has increased forone-fourthof crashes. In addition, the average reproduction time of a crash becomes at least 2x faster. Wilcoxon Mann-Whitney test shows this enhancement is significant when our tool is used compared to baseline and insensitive reachability analysis.

Widespread growth in Android malware stimulates security researchers to propose different methods for analyzing and detecting malicious behaviors in applications. Nevertheless, current solutions are ill-suited to extract the fine-grained behavior of Android applications accurately and efficiently. In this paper, we propose ServiceMonitor, a lightweight host-based detection system that dynamically detects malicious applications directly on mobile devices. ServiceMonitor reconstructs the fine-grained behavior of applications based on their interaction with system services (i.e. SMS manager, camera, wifi networking, etc). ServiceMonitor monitors the way applications request system services in order to build a statistical Markov chain model to represent what and how system services are used. Afterwards, we use this Markov chain as a feature vector to classify the application behavior into either malicious or benign using the Random Forests classification algorithm. We evaluated ServiceMonitor using a dataset of 8034 malware and 10024 benign applications and obtaining 96.7% of accuracy rate and negligible overhead and performance penalty.

Two main security issues in software as a service (SaaS) delivery model of cloud environments are access control and privacy preserving in basic web services as well as composite services where we require to infer policies through the automatic composition of the policies specified for their constituting basic services. In this paper, we present a privacy preserving access control model and framework for secure service provision and composition. The model is a combination of an attribute based access control model and a proposed purpose-based privacy model. Following this model, an access request for a service is permitted if the requester's attribute certificates and contextual conditions are in compliance with the access control policies specified by the service provider and simultaneously the privacy preferences of the requester is compatible with the privacy policies of the service provider. In the framework proposed in this paper, for secure service composition, possible chains of composite services are ranked according to the users' preferences and sensitivity level of their data. The security policies of the composite service, established by the chosen chain of services, are inferred by automatic composition of policies specified for the basic services in the chain.

Semantic technology is widely used in distributed computational environments to increase interoperability and machine readability of information through giving semantics to the underlying information and resources. Semantic-awareness, distribution and interoperability of new generation of distributed systems demand an authorisation model and framework that satisfies essential authorisation requirements of such environments. In this study, the authors propose an authorisation model and framework based on multi-security-domain architecture for distributed semantic-aware environments. The proposed framework is founded based on the MA(DL)2 logic, which enables policy specification and inference (based on the defined semantic relationships) in both conceptual and ground (individual) levels. Also, it enables authorities to have cooperative security management in their shared domain of resources with different administration styles.

In order to overcome the shortcomings of the recent frameworks and mechanisms for semantic-based access control, this paper presents a semantic-based, context-aware, and multi-domain enabled framework implementing a semantic-based access control mechanism for Semantic Web. The access control framework is based on the MA(DL)2 model, which takes the semantic relationships among different entities into account. The framework handles the Semantic Web context by classifying and representing it through an ontology. Considering the MA(DL)2 model, the framework assumes Semantic Web having some overlapped domains, which each contains an authority and a security agent. As a domain authority responsibility is to specify the domain policies, its agent is to enforce them. The mechanism is designed using the semantic technologies, which make it fully consistent with the environment. The paper clarifies the usability of the designed mechanism through some examples of an elections system case study.

By the immense growth of the Web-based social networks (WBSNs), the role of trust in connecting people together through WBSNs is getting more important than ever. In other words, since the probability of malicious behavior in WBSNs is increasing, it is necessary to evaluate the reliability of a person before trying to communicate with. Hence, it is desirable to find out how much a person should trust another one in a network. The approach to answer this question is usually called trust inference. In this paper, we propose a new trust inference algorithm (called RN-trust) based on the resistive networks concept. The algorithm, in addition to being simple, resolves some problems of previously proposed approaches. The analysis of the algorithm demonstrates that RN-trust calculates the trust values more accurately than previous approaches.

As a security principle, separation of duty (SoD) is widely considered in computer security. In the role-based access control(RBAC) model, separation of duty constraints enforce conflict of interest policies. There are two main types of separation of duty policies in RBAC, Static SoD (SSoD) and Dynamic SoD (DSoD). In RBAC, Statically Mutually Exclusive Role (SMER) constraints are used to enforce Static Separation of Duty policies. Dynamic Separation of duty policies, like SSoD policies, are intended to limit the permissions that are available to a user. However, DSoD policies differ from SSoD policies by the context in which these limitations are imposed. A DSoD policy limits the availability of the permissions over a users permission space by placing constraints on the roles that can be activated within or across a users sessions. Like SMER, in RBAC Dynamically Mutually Exclusive Role (DMER) constraints are used to enforce DSoD policies. We investigated using of a fuzzy approach to address the issue in order to provide a more practical solution. In this paper, we propose a model to express the separation of duty policies in RBAC using the fuzzy set theory. The concept of trustworthiness, which is fuzzy in nature, is used to express this model. In comparison with non-fuzzy methods, our method is more pragmatic and more consistent with the real world. The expressiveness of our method is higher than the non- fuzzy ones. We show expression of some constraints in our method which cannot be expressed by non-fuzzy methods. Applicability of the method is shown through an example of the real world.

Role-Based Access Control (RBAC) model is naturally suitable to organizations where users are assigned organizational roles with well-defined privileges. However, due to the large number of users in nowadays online services of organizations and enterprises, assigning users to roles is a tiresome task and maintaining user-role assignment up- to-date is costly and error-prone. Additionally, with the increasing number of users, RBAC may have problems in prohibiting cheat and changing roles of users. In order to categorize information and formulate security policies, human decision making is required which is naturally fuzzy in the real world. This leads using a fuzzy approach to address the issue in order to provide a more practical solution. In this paper, applicability of fuzzy set theory to RBAC has been investigated by identifying access control building blocks which are fuzzy in essence. An existing RBAC model is extended to allow imprecise access control policies, using the concept of trustworthiness which is fuzzy in nature. We call the extended model as Fuzzy RBAC. Applicability of the extended model has been evaluated through some case studies.

Role based access control (RBAC) model is naturally suitable to organizations where users are assigned organizational roles with well-defined privileges. Nowadays, many organizations and enterprises such as banks, insurance industry and utility companies, provide online services to their very large number of users. This shows that assigning users to roles is a intolerable task and maintaining user-role assignment up-to-date is costly and error-prone. Also, with the increasing number of users, RBAC may have problems in prohibiting cheat and changing roles of users. To overcome these problems, user-role assignment decision can be made based on how much we trust him/her. In this paper, we propose a model to assign users to roles based on trustworthiness which is fuzzy in nature. The proposed model uses fuzzy relation equations to compute trust values.