DCDIDP: A Distributed, Collaborative, and Data-driven IDP Framework for the Cloud

Recent advances in distributed computing, grid computing, virtualization mechanisms, and utility computing led into Cloud Computing as one of the industry buzz words of our decade. As the popularity of the services provided in the cloud environment grows exponentially, the exploitation of possible vulnerabilities grows with the same pace. Intrusion Detection and Prevention Systems (IDPSs) are one of the most popular tools among the front line fundamental tools to defend the computation and communication infrastructures from the intruders. In this poster, we propose a distributed, collaborative, and data-driven IDP (DCDIDP) framework for cloud computing environments. Both cloud providers and cloud customers will benefit significantly from DCDIDP that dynamically evolves and gradually mobilizes the resources in the cloud as suspicion about attacks increases. Such system will provide homogeneous IDPS for all the cloud providers that collaborate distributively. It will respond to the attacks, by collaborating with other peers and in a distributed manner, as near as possible to attack sources and at different levels of operations (e.g. network, host, VM). We present the DCDIDP framework and explain its components. However, further explanation is part of our ongoing work

Ontology-based access control for social network systems

As the information flowing around in social network systems is mainly related or can be attributed to their users, controlling access to such information by individual users becomes a crucial requirement. The intricate semantic relations among data objects, different users, and between data objects and users further add to the complexity of access control needs. In this paper, we propose an access control model based on semantic web technologies that takes into account the above mentioned complex relations. The proposed model enables expressing much more fine-grained access control policies on a social network knowledge base than the existing models. We demonstrate the applicability of our approach by implementing a proof-of-concept prototype of the proposed access control framework and evaluating its performance

Analysing security and privacy issues of using e-mail address as identity

Nowadays, many websites allow or require users to use their e-mail addresses either as identity or for other purposes. Although username-based identity problems resulting from users’ behaviours have been a research focus for quite some time, the serious issues of using e-mail address as identity and the associated online behaviours of users have not been well investigated. In this paper, we discuss and analyse security and privacy problems resulting from using e-mail address as identity via well-designed user behaviour survey and by investigating websites’ design schemes. Our results illustrate that using e-mail address as identity poses high security and privacy risks. This is mainly because of the multiple usages of e-mail addresses and users’ improper online habits. Moreover, we discuss drawbacks of existing solutions for e-mail address as identity and related password problems, and present potential solutions that may be used to secure online identity management systems in future

Rhizosphere dynamics of inoculated cyanobacteria and their growth-promoting role in rice crop

Nitrogen fixing cyanobacteria are the predominant flora in waterlogged paddy fields which contribute significantly towards nitrogen budgeting in these ecosystems. Their establishment and role in plant growth promotion and soil microbial activity is poorly known. Under greenhouse conditions, pots were inoculated with one of a set of twenty cyanobacterial strains isolated from the rhizosphere of diverse rice and wheat varieties. Several strains established in the soil and persisted up to the harvest stage in soil and roots, significantly enhancing soil microbial biomass carbon, available nitrogen, and related soil microbiological parameters, and increased grain yields and grain weight. This can help in selecting promising strains for developing carrier-based inoculants to promote the growth of crop and soil microflora, leading to enhanced soil fertility and crop yields

Security in Dynamic Spectrum Access Systems: A Survey

Dynamic Spectrum Access (DSA) systems are being developed to improve spectrum utilization. Most of the research on DSA systems assumes that the participants involved are honest, cooperative, and that no malicious adversaries will attack or exploit the network. Some recent research efforts have focused on studying security issues in cognitive radios but there are still significant security challenges in the implementation of DSA systems that have not been addressed. In this paper we focus on security issues in DSA. We identify various attacks (e.g., DoS attacks, system penetration, repudiation, spoofing, authorization violation, malware infection, data modification, etc.) and suggest various approaches to address them. We show that significant security issues exist that should be addressed by the research community if DSA is to find its way into production systems. We also show that, in many cases, existing approaches to securing IT systems can be applied to DSA and identify other DSA specific security challenges where additional research will be required

Expression of nm23 in the spectrum of pre-invasive, invasive and metastatic breast lesions

BACKGROUND: Nm23 protein is a metastasis suppressor protein, expressed in all tissues. Reduced Nm23 expression is related to a high incidence of lymph node and distant metastasis and poor prognosis in patients with cancers. The present study was done to analyze the expression of Nm23 using immunohistochemistry in non-neoplastic and neoplastic breast lesions. METHODS: Sections from 93 samples were studied and classified into non-proliferative breast lesion (13), fibrodenoma (7), proliferative breast lesion (13), carcinoma in situ (20), invasive carcinoma (23) and metastatic deposits in lymph nodes (17). RESULTS: Nm23 expression in these groups showed a progressive down regulation with increasing neoplastic transformation. On comparing the various groups, nm23 expression was significantly different between the various subgroups with greatest expression in non-proliferative lesions and least in metastatic deposits (p < 0.050). CONCLUSION: It is concluded that the modulation of nm23 in a spectrum of breast lesions can be indicative of metastatic phenotype and help to predict the aggressiveness of disease

An anticancer drug suppresses the primary nucleation reaction that initiates the production of the toxic Aβ42 aggregates linked with Alzheimer's disease.

The conversion of the β-amyloid (Aβ) peptide into pathogenic aggregates is linked to the onset and progression of Alzheimer's disease. Although this observation has prompted an extensive search for therapeutic agents to modulate the concentration of Aβ or inhibit its aggregation, all clinical trials with these objectives have so far failed, at least in part because of a lack of understanding of the molecular mechanisms underlying the process of aggregation and its inhibition. To address this problem, we describe a chemical kinetics approach for rational drug discovery, in which the effects of small molecules on the rates of specific microscopic steps in the self-assembly of Aβ42, the most aggregation-prone variant of Aβ, are analyzed quantitatively. By applying this approach, we report that bexarotene, an anticancer drug approved by the U.S. Food and Drug Administration, selectively targets the primary nucleation step in Aβ42 aggregation, delays the formation of toxic species in neuroblastoma cells, and completely suppresses Aβ42 deposition and its consequences in a Caenorhabditis elegans model of Aβ42-mediated toxicity. These results suggest that the prevention of the primary nucleation of Aβ42 by compounds such as bexarotene could potentially reduce the risk of onset of Alzheimer's disease and, more generally, that our strategy provides a general framework for the rational identification of a range of candidate drugs directed against neurodegenerative disorders.This work was supported by the Centre for Misfolding Diseases, University of Cambridge.This is the final published version. It first appeared at http://advances.sciencemag.org/content/2/2/e1501244

Evaluation of rate law approximations in bottom-up kinetic models of metabolism.

BackgroundThe mechanistic description of enzyme kinetics in a dynamic model of metabolism requires specifying the numerical values of a large number of kinetic parameters. The parameterization challenge is often addressed through the use of simplifying approximations to form reaction rate laws with reduced numbers of parameters. Whether such simplified models can reproduce dynamic characteristics of the full system is an important question.ResultsIn this work, we compared the local transient response properties of dynamic models constructed using rate laws with varying levels of approximation. These approximate rate laws were: 1) a Michaelis-Menten rate law with measured enzyme parameters, 2) a Michaelis-Menten rate law with approximated parameters, using the convenience kinetics convention, 3) a thermodynamic rate law resulting from a metabolite saturation assumption, and 4) a pure chemical reaction mass action rate law that removes the role of the enzyme from the reaction kinetics. We utilized in vivo data for the human red blood cell to compare the effect of rate law choices against the backdrop of physiological flux and concentration differences. We found that the Michaelis-Menten rate law with measured enzyme parameters yields an excellent approximation of the full system dynamics, while other assumptions cause greater discrepancies in system dynamic behavior. However, iteratively replacing mechanistic rate laws with approximations resulted in a model that retains a high correlation with the true model behavior. Investigating this consistency, we determined that the order of magnitude differences among fluxes and concentrations in the network were greatly influential on the network dynamics. We further identified reaction features such as thermodynamic reversibility, high substrate concentration, and lack of allosteric regulation, which make certain reactions more suitable for rate law approximations.ConclusionsOverall, our work generally supports the use of approximate rate laws when building large scale kinetic models, due to the key role that physiologically meaningful flux and concentration ranges play in determining network dynamics. However, we also showed that detailed mechanistic models show a clear benefit in prediction accuracy when data is available. The work here should help to provide guidance to future kinetic modeling efforts on the choice of rate law and parameterization approaches