International Journal of Information Security
Volume:5 Issue: 2, Jul 2013

Access control in open and dynamic Pervasive Computing Environments (PCEs) is a very complex mechanism and encompasses various new requirements. Excessive use of context information is one of the main characteristics of PCEs. Therefore, access control models designed for PCEs should be able to use accessible context information in their access decision process. However, it is not applicable to gather all context information completely and accurately all the time. Thus, a context-aware access control model must be able to deal with imperfect context information, which makes it a non-monotonic system, where the inferred access decision might change by more complete context information. In addition, due to the diversity and heterogeneity of resources and users and their security requirements in PCEs, a high expressive policy specification language is needed. Using a non-monotonic logic as a policy specification language provides a platform for handling incomplete context information as well as other non-monotonic security requirements including exception and default policies. This paper proposes a Semantic-Aware Role-Based Access Control (SARBAC) model which satisfies the aforementioned requirements using MKNF+, which is a combination of Description Logic (DL) and Answer Set Programming (ASP). Along with the use of DL to define an ontology for access control elements and context information; MKNF+ rules are used to define context-aware role activation and permission assignment policies. The proposed model inherits the advantages of ontological representation of access control elements and context information (such as interoperability among systems) as well as the ASP advantages in non-monotonic reasoning through the closed-world principle and negation-as-failure. The expressive power of the proposed model is demonstrated through a case study in this paper.

The spread of rumors, which are known as unverified statements of uncertain origin, may threaten the society and its controlling is important for national security councils of countries. If it would be possible to identify factors affecting spreading a rumor (such as agents’ desires, trust network, etc.), then this could be used to slow down or stop its spreading. Therefore, a computational model that includes rumor features, and the way rumor is spread among society’s members, based on their desires, is needed. Our research is focused on the relation between the homogeneity of the society and rumor convergence in it. Our result shows that the homogeneity of the society is a necessary condition for convergence of the spread rumor.

Nowadays, the growth of virtual environments such as virtual organizations, social networks, and ubiquitous computing, have led to the adoption of trust concept. One of the methods of making trust in such environments is to use long-term relationship with a trusted partner. The main problem of this kind of trust, which is based on personal experiences, is its limited domain. Moreover, both parties of such trust relationship will face big problems of collecting data and forming reasonable and reliable beliefs. Considering the concept of “group” in modeling trust is a way to overcome the above mentioned problems. Since,group-based trust is more suited with the nature of trust in new virtual environments. In this paper a new trust model called “GTrust” is proposed in which trust is considered as a collective and shared feature of all group members. Therefore, group membership is used as the judgment criteria regarding a person’s expected behavior and how he can be a trustee. GTrust is based on Metagraphs which is a graphical data structure for representing a collection of directed set-to-set mappings. We show that by using GTrust, large trust spaces between unknown individuals can be shaped effectively. The proposed model not only offers a better description of human sense of trust when considering communities, but also provides the setting for evaluating the trust of individuals whom we do not know, and therefore provides an extended evaluation domain.

In Social networks, users need a proper estimation of trust in others to be able to initialize reliable relationships. Some trust evaluation mechanisms have been offered, which use direct ratings to calculate or propagate trust values. However, in some web-based social networks where users only have binary relationships, there is no direct rating available. Therefore, a new method is required to infer trust values in these networks. To bridge this gap, this paper aims to propose a new method which takes advantages of user similarity to predict trust values without any need to direct ratings. In this approach, which is based on socio-psychological studies, user similarity is calculated from the profile information and the texts shared by the users via text-mining techniques. Applying Ziegler ratios to our approach revealed that users are more than 50% more similar to their trusted agents than to arbitrary peers, which proves the validity of the original idea of the study about inferring trust from language similarity. In addition, comparing the real assigned ratings, gathered directly from users, with the experimental results indicated that the predicted trust values are sufficiently acceptable (with a precision of 61%). We have also studied the benefits of using context in inferring trust. In this regard, the analysis revealed that the precision of the predictions can be improved up to 72%. Besides the application of this approach in web-based social networks, the proposed technique can also be of much help in any direct rating mechanism to evaluate the correctness of trust values assigned by users, and increases the robustness of trust and reputation mechanisms against possible security threats.

A convertible limited multi-verifier signature (CLMVS) provides controlled verifiability and preserves privacy of the signer. Furthermore, limited verifiers can designate the signature to a third party or convert it to a publicly verifiable signature when necessary. However, constructing an efficient scheme with a unique signature for more than two limited verifiers is remained unsolved. In this study, we first derive the general construction of convertible limited verifier signatures (CLVS) which previous secure CLVS schemes fit into this construction. Then, we extend this generic construction to produce two CLMVS constructions which are efficient in the sense of generating a unique signature for more than two limited verifiers. In the first generic construction, each limited verifier can check the validity of the signature solely and in the second generic construction, cooperation of all limited verifiers is necessary. Finally, based on our second generic construction, we present the first pairing-based CLMVS scheme secure in the standard model which has strong confirmation property. Then, we employ the proposed CLMVS scheme for one limited verifier (CLVS), to design a new electronic voting protocol.

Artificial Immune Systems (AIS) have long been used in the field of computer security and especially in Intrusion Detection systems. Intrusion detection based on AISs falls into two main categories. The first generation of AIS is inspired from adaptive immune reactions but, the second one which is called danger theory focuses on both adaptive and innate reactions to build a more biologically-realistic model of Human Immune System. Two algorithms named TLR and DCA are proposed in danger theory field that both of them are trying to identify the antigens based on a simple identifier. Both of them suffer from low accuracy and detection rate due to the fact that they are not taking thestructure of antigens into account. In this paper we propose an algorithm called STLR (structural TLR), which is an extended form of TLR algorithm. STLR tries to model the interaction of adaptive and innate biological immune systems and at the same time considers the structure of the antigens. The experimental results show that using the structural aspects of an antigen, STLR can lead to a great increase in the detection rate and accuracy.