PROTECT: Proximity-based Trust-advisor using Encounters for Mobile Societies

Many interactions between network users rely on trust, which is becoming particularly important given the security breaches in the Internet today. These problems are further exacerbated by the dynamics in wireless mobile networks. In this paper we address the issue of trust advisory and establishment in mobile networks, with application to ad hoc networks, including DTNs. We utilize encounters in mobile societies in novel ways, noticing that mobility provides opportunities to build proximity, location and similarity based trust. Four new trust advisor filters are introduced - including encounter frequency, duration, behavior vectors and behavior matrices - and evaluated over an extensive set of real-world traces collected from a major university. Two sets of statistical analyses are performed; the first examines the underlying encounter relationships in mobile societies, and the second evaluates DTN routing in mobile peer-to-peer networks using trust and selfishness models. We find that for the analyzed trace, trust filters are stable in terms of growth with time (3 filters have close to 90% overlap of users over a period of 9 weeks) and the results produced by different filters are noticeably different. In our analysis for trust and selfishness model, our trust filters largely undo the effect of selfishness on the unreachability in a network. Thus improving the connectivity in a network with selfish nodes. We hope that our initial promising results open the door for further research on proximity-based trust

Use of Thrust Vectors in Support of the CyberCraft Initiative

The CyberCraft Initiative is designing a framework for command and control of future Air Force Cyber-weapon systems, that autonomously operate and defend the Air Force networks and information systems to provide Cyberspace Superiority in support of the defense of the United States. The fundamental research question of the CyberCraft Initiative is What is required for a commander to trust a CyberCraft to autonomously defend military information systems? The Trust Vector model is one method of integrating trust into the CyberCraft fleet. Trust Vectors define trust and distrust between agents based on three components; current and historical data, intrinsic knowledge of the remote agent\u27s abilities, and recommendations from other agents. This research finds that the Trust Vector model can be modified to integrate trust into the CyberCraft Initiative. Several expansions to the model are proposed, including applying the Trust Vector model to an asynchronous paradigm for data transactions. This research also determines the limits of the utility of historical data for the Trust Vector model

The fetal mouse is a sensitive genotoxicity model that exposes lentiviral-associated mutagenesis resulting in liver oncogenesis

This article is available open access through the publisher’s website at the link below. Copyright @ 2013 The American Society of Gene & Cell Therapy.Genotoxicity models are extremely important to assess retroviral vector biosafety before gene therapy. We have developed an in utero model that demonstrates that hepatocellular carcinoma (HCC) development is restricted to mice receiving nonprimate (np) lentiviral vectors (LV) and does not occur when a primate (p) LV is used regardless of woodchuck post-translation regulatory element (WPRE) mutations to prevent truncated X gene expression. Analysis of 839 npLV and 244 pLV integrations in the liver genomes of vector-treated mice revealed clear differences between vector insertions in gene dense regions and highly expressed genes, suggestive of vector preference for insertion or clonal outgrowth. In npLV-associated clonal tumors, 56% of insertions occurred in oncogenes or genes associated with oncogenesis or tumor suppression and surprisingly, most genes examined (11/12) had reduced expression as compared with control livers and tumors. Two examples of vector-inserted genes were the Park 7 oncogene and Uvrag tumor suppressor gene. Both these genes and their known interactive partners had differential expression profiles. Interactive partners were assigned to networks specific to liver disease and HCC via ingenuity pathway analysis. The fetal mouse model not only exposes the genotoxic potential of vectors intended for gene therapy but can also reveal genes associated with liver oncogenesis.Imperial College London, the Wellcome Trust, and Brunel University

An explicit solution to the optimal LQG problem for flexible structures with collocated rate sensors

We present a class of compensators in explicit form (not requiring numerical computer calculations) for stabilizing flexible structures with collocated rate sensors. They are based on the explicit solution, valid for both Continuum and FEM Models, of the LQG problem for minimizing mean square rate. They are robust with respect to system stability (will not destabilize modes even with mismatch of parameters), can be instrumented in state space form suitable for digital controllers, and can be specified directly from the structure modes and mode 'signature' (displacement vectors at sensor locations). Some simulation results are presented for the NASA LaRC Phase-Zero Evolutionary Model - a modal Trust model with 86 modes - showing damping ratios attainable as a function of compensator design parameters and complexity

Integrating Social Circles and Network Representation Learning for Item Recommendation

With the increasing popularity of social network services, social network platforms provide rich and additional information for recommendation algorithms. More and more researchers utilize trust relationships of users to improve the performance of recommendation algorithms. However, most of existing social-network-based recommendation algorithms ignore the following problems: (1) In different domains, users tend to trust different friends. (2) the performance of recommendation algorithms is limited by the coarse-grained trust relationships. In this paper, we propose a novel recommendation algorithm that integrates social circles and network representation learning for item recommendation. Specifically, we first infer domain-specific social trust circles based on original users’ rating information and social network information. Next, we adopt network representation technique to embed domain-specific social trust circle into a low-dimensional space, and then utilize the low-dimensional representations of users to infer the fine-grained trust relationships between users. Finally, we integrate the fine-gained trust relationships into domain-specific matrix factorization model to learn latent user and item feature vectors. Experimental results on real-world datasets show that our proposed approach outperforms traditional social-network-based recommendation algorithms

Portunes: analyzing multi-domain insider threats

The insider threat is an important problem in securing information systems. Skilful insiders use attack vectors that yield the greatest chance of success, and thus do not limit themselves to a restricted set of attacks. They may use access rights to the facility where the system of interest resides, as well as existing relationships with employees. To secure a system, security professionals should therefore consider attacks that include non-digital aspects such as key sharing or exploiting trust relationships among employees. In this paper, we present Portunes, a framework for security design and audit, which incorporates three security domains: (1) the security of the computer system itself (the digital domain), (2) the security of the location where the system is deployed (the physical domain) and (3) the security awareness of the employees that use the system (the social domain). The framework consists of a model, a formal language and a logic. It allows security professionals to formally model elements from the three domains in a single framework, and to analyze possible attack scenarios. The logic enables formal specification of the attack scenarios in terms of state and transition properties

STUDY ON RETAIL BRAND AWARENESS IN RETAIL

Brand awareness, together with other behavioural indicators (sympathy, trust, image, satisfaction or loyalty), is one of the main vectors that has an essential contribution to the outline of brand equity in general and to that of retail brand, in particular. The perception upon these indicators must be taken into consideration by production, service or retail companies in order to be able to identify their position on target markets, and in order to be able to create an adequate strategy that would help them reach the desired positioning. The aim of this paper is, on one hand, to reveal both the dimensions of brand awareness, and the relationship between these and consumers' brand perception and, on the other hand, to offer a suitable instrument to measure awareness level of various retail chains. Questioning of almost 4.000 consumers indicates a significant awareness of the retailers that have been on the selected market for a longer period of time.awareness, recall, recognition, retail brands