Towards Bayesian-Based Trust Management for Insider Attacks in Healthcare Software-Defined Networks

© 2004-2012 IEEE. The medical industry is increasingly digitalized and Internet-connected (e.g., Internet of Medical Things), and when deployed in an Internet of Medical Things environment, software-defined networks (SDNs) allow the decoupling of network control from the data plane. There is no debate among security experts that the security of Internet-enabled medical devices is crucial, and an ongoing threat vector is insider attacks. In this paper, we focus on the identification of insider attacks in healthcare SDNs. Specifically, we survey stakeholders from 12 healthcare organizations (i.e., two hospitals and two clinics in Hong Kong, two hospitals and two clinics in Singapore, and two hospitals and two clinics in China). Based on the survey findings, we develop a trust-based approach based on Bayesian inference to figure out malicious devices in a healthcare environment. Experimental results in either a simulated and a real-world network environment demonstrate the feasibility and effectiveness of our proposed approach regarding the detection of malicious healthcare devices, i.e., our approach could decrease the trust values of malicious devices faster than similar approaches

Fuzzy identity-based data integrity auditing for reliable cloud storage systems

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.As a core security issue in reliable cloud storage, data integrity has received much attention. Data auditing protocols enable a verifier to efficiently check the integrity of the outsourced data without downloading the data. A key research challenge associated with existing designs of data auditing protocols is the complexity in key management. In this paper, we seek to address the complex key management challenge in cloud data integrity checking by introducing fuzzy identity-based auditing-the first in such an approach, to the best of our knowledge. More specifically, we present the primitive of fuzzy identity-based data auditing, where a user’s identity can be viewed as a set of descriptive attributes. We formalize the system model and the security model for this new primitive. We then present a concrete construction of fuzzy identity-based auditing protocol by utilizing biometrics as the fuzzy identity. The new protocol offers the property of error-tolerance, namely, it binds private key to one identity which can be used to verify the correctness of a response generated with another identity, if and only if both identities are sufficiently close. We prove the security of our protocol based on the computational Diffie-Hellman assumption and the discrete logarithm assumption in the selective-ID security model. Finally, we develop a prototype implementation of the protocol which demonstrates the practicality of the proposal.This work is supported by the National Natural Science Foundation of China (61501333,61300213,61272436,61472083), the Fundamental Research Funds for the Central Universities under Grant ZYGX2015J05

Blockchain-based privacy preservation for 5G-enabled drone communications

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record5G-enabled drones have potential applications in a variety of both military and civilian settings (e.g., monitoring and tracking of individuals in demonstrations and/or enforcing of social / physical distancing during pandemics such as COVID-19). Such applications generally involve the collection and dissemination of (massive) data from the drones to remote data centres for storage and analysis, for example via 5G networks. Consequently, there are security and privacy considerations underpinning 5G-enabled drone communications. We posit the potential of leveraging blockchain to facilitate privacy preservation, and therefore in this article we will review existing blockchain-based solutions after introducing the architecture for 5G-enabled drone communications and blockchain. We will also review existing legislation and data privacy regulations that need to be considered in the design of blockchain-based solutions, as well as identifying potential challenges and open issues which will hopefully inform future research agenda

A Blockchain-based Decentralized, Fair and Authenticated Information Sharing Scheme in Zero Trust Internet-of-Things

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordData availability statement: The [code] data used to support the findings of this study have been deposited in the [IEEE DATAPORT] repository ([10.21227/rtmq-t937]).Internet-of-Things (IoT) are increasingly operating in the zero-trust environments where any devices and systems may be compromised and hence untrusted. In addition, data collected by and sent from IoT devices may be shared with edge computing systems in order to reduce the reliance on centralized (cloud) servers, leading to further security and privacy issues. To cope with these challenges, this paper proposes an innovative blockchain-enabled information sharing solution in zero-trust context to guarantee anonymity yet entity authentication, data privacy yet data trustworthiness, and participant stimulation yet fairness. This new solution is able to support filtering of fabricated information through smart contracts, effective voting, and consensus mechanisms, which can prevent unauthenticated participants from sharing garbage information. We also prove the proposed solution is secure in the universal composability framework, and further evaluate its performance over an ETH-based platform to demonstrate its utility.Foundation of Yunnan Key Laboratory of Blockchain Application TechnologyNational Natural Science Foundation of ChinaProvincial Key Research and Development Program of HubeiFoundation of Henan Key Laboratory of Network Cryptography TechnologyFoundation of Hubei Key Laboratory of Intelligent Geo-Information Processin

SMEs' Confidentiality Concerns for Security Information Sharing

Small and medium-sized enterprises are considered an essential part of the EU economy, however, highly vulnerable to cyberattacks. SMEs have specific characteristics which separate them from large companies and influence their adoption of good cybersecurity practices. To mitigate the SMEs' cybersecurity adoption issues and raise their awareness of cyber threats, we have designed a self-paced security assessment and capability improvement method, CYSEC. CYSEC is a security awareness and training method that utilises self-reporting questionnaires to collect companies' information about cybersecurity awareness, practices, and vulnerabilities to generate automated recommendations for counselling. However, confidentiality concerns about cybersecurity information have an impact on companies' willingness to share their information. Security information sharing decreases the risk of incidents and increases users' self-efficacy in security awareness programs. This paper presents the results of semi-structured interviews with seven chief information security officers of SMEs to evaluate the impact of online consent communication on motivation for information sharing. The results were analysed in respect of the Self Determination Theory. The findings demonstrate that online consent with multiple options for indicating a suitable level of agreement improved motivation for information sharing. This allows many SMEs to participate in security information sharing activities and supports security experts to have a better overview of common vulnerabilities. The final publication is available at Springer via https://doi.org/10.1007/978-3-030-57404-8_22Comment: 10 pages, 2 figures, 14th International Symposium on Human Aspects of Information Security & Assurance (HAISA 2020

Statistical quality assessment and outlier detection for liquid chromatography-mass spectrometry experiments

<p>Abstract</p> <p>Background</p> <p>Quality assessment methods, that are common place in engineering and industrial production, are not widely spread in large-scale proteomics experiments. But modern technologies such as Multi-Dimensional Liquid Chromatography coupled to Mass Spectrometry (LC-MS) produce large quantities of proteomic data. These data are prone to measurement errors and reproducibility problems such that an automatic quality assessment and control become increasingly important.</p> <p>Results</p> <p>We propose a methodology to assess the quality and reproducibility of data generated in quantitative LC-MS experiments. We introduce quality descriptors that capture different aspects of the quality and reproducibility of LC-MS data sets. Our method is based on the Mahalanobis distance and a robust Principal Component Analysis.</p> <p>Conclusion</p> <p>We evaluate our approach on several data sets of different complexities and show that we are able to precisely detect LC-MS runs of poor signal quality in large-scale studies.</p

Tandem mass spectrometry data quality assessment by self-convolution

<p>Abstract</p> <p>Background</p> <p>Many algorithms have been developed for deciphering the tandem mass spectrometry (MS) data sets. They can be essentially clustered into two classes. The first performs searches on theoretical mass spectrum database, while the second based itself on <it>de novo </it>sequencing from raw mass spectrometry data. It was noted that the quality of mass spectra affects significantly the protein identification processes in both instances. This prompted the authors to explore ways to measure the quality of MS data sets before subjecting them to the protein identification algorithms, thus allowing for more meaningful searches and increased confidence level of proteins identified.</p> <p>Results</p> <p>The proposed method measures the qualities of MS data sets based on the symmetric property of b- and y-ion peaks present in a MS spectrum. Self-convolution on MS data and its time-reversal copy was employed. Due to the symmetric nature of b-ions and y-ions peaks, the self-convolution result of a good spectrum would produce a highest mid point intensity peak. To reduce processing time, self-convolution was achieved using Fast Fourier Transform and its inverse transform, followed by the removal of the "DC" (Direct Current) component and the normalisation of the data set. The quality score was defined as the ratio of the intensity at the mid point to the remaining peaks of the convolution result. The method was validated using both theoretical mass spectra, with various permutations, and several real MS data sets. The results were encouraging, revealing a high percentage of positive prediction rates for spectra with good quality scores.</p> <p>Conclusion</p> <p>We have demonstrated in this work a method for determining the quality of tandem MS data set. By pre-determining the quality of tandem MS data before subjecting them to protein identification algorithms, spurious protein predictions due to poor tandem MS data are avoided, giving scientists greater confidence in the predicted results. We conclude that the algorithm performs well and could potentially be used as a pre-processing for all mass spectrometry based protein identification tools.</p

The effect of early pregnancy following chemotherapy on disease relapse and foetal outcome in women treated for gestational trophoblastic tumours

Little literature exists on the safety of early pregnancy following chemotherapy. Here we assess the rate of relapse and foetal outcome in women who have completed single and multi-agent chemotherapy for gestational trophoblastic tumours. The records of 1532 patients treated for persistent gestational trophoblastic tumours at Charing Cross Hospital between 1969 and 1998 were reviewed. Patients were defined as receiving single agent or multi-agent treatment. Relapse rates and foetal outcome were reviewed in the 230 patients who became pregnant within 12 months of completing chemotherapy. In the single agent group 153 (22%) of 691 patients conceived early. Three subsequently relapsed. In the multi-agent group, 77 (10%) of 779 patients conceived early, two then relapsed. Relapse rates were 2% (3 out of 153) and 2.5% (2 out of 77) for each group compared to 5% and 5.6% in the comparative non-pregnant groups. Outcomes of 230 early pregnancies: 164 (71%) delivered at full term, 35 (15%) terminations, 26 (11%) spontaneous abortions, three (1.3%) new hydatidiform moles and two (1%) stillbirths. Early pregnancies were more common in the single agent group (P<0.001), but spontaneous miscarriages and terminations were more likely to occur in the multi-agent group (P=0.04 and 0.03, respectively). Of the full-term pregnancies, three (1.8%) babies were born with congenital abnormalities. Patients in either group who conceive within 12 months of completing chemotherapy are not at increased risk of relapse. Though, we still advise avoiding pregnancy within 12 months of completing chemotherapy, those that do conceive can be reassured of a likely favourable outcome

Moving out of the shadows: accomplishing bisexual motherhood

Our qualitative study explored the ways in which bisexual mothers came to identify as such and how they structured their relationships and parenting within hetero-patriarchal society. The experiences of seven self-identified White bisexual women (aged from 28 to 56-years-old) from across England and the Republic of Ireland were investigated through semi-structured interviews. Participants’ children were aged 8 months to 28 years old at the time of their interviews. A thematic narrative analysis highlighted the following issues that participants had encountered in constructing their self-identity: prioritizing children; connecting and disconnecting with others and finessing self-definition; questioning societal relationship expectations. Nevertheless, participants varied considerably in how each of the themes identified were reflected in their lives, in particular depending upon each participant’s interpretation of her local social context. Both motherhood and self-identifying as bisexual gave a sense of meaning and purpose to participants’ life stories, although participants sometimes foregrounded their commitment to their children even at a personal cost to their bisexual identity. Using three different theoretical perspectives from feminist theory, queer theory and life course theory, the narratives analysed revealed ways in which bisexual motherhood not only had been influenced both intentionally and unintentionally by heteronormative expectations but also had directly and indirectly challenged these expectations

Flavonoids from Pterogyne nitens Inhibit Hepatitis C Virus Entry

Hepatitis C virus (HCV) is one of the leading causes of liver diseases and transplantation worldwide. The current available therapy for HCV infection is based on interferon-α, ribavirin and the new direct-acting antivirals (DAAs), such as NS3 protease and NS5B polymerase inhibitors. However, the high costs of drug design, severe side effects and HCV resistance presented by the existing treatments demonstrate the need for developing more efficient anti-HCV agents. This study aimed to evaluate the antiviral effects of sorbifolin (1) and pedalitin (2), two flavonoids from Pterogyne nitens on the HCV replication cycle. These compounds were investigated for their anti-HCV activities using genotype 2a JFH-1 subgenomic replicons and infectious virus systems. Flavonoids 1 and 2 inhibited virus entry up to 45.0% and 78.7% respectively at non-cytotoxic concentrations. The mechanism of the flavonoid 2 block to virus entry was demonstrated to be by both the direct action on virus particles and the interference on the host cells. Alternatively, the flavonoid 1 activity was restricted to its virucidal effect. Additionally, no inhibitory effects on HCV replication and release were observed by treating cells with these flavonoids. These data are the first description of 1 and 2 possessing in vitro anti-HCV activity