With the growing complexity of networks and communications protocols that become increasingly enormous and extensive, we are confronted with the problem of covert channel that affects the confidentiality and integrity of data sent in the network. Covert channels also known as hidden channels can elude basic security systems such as Intrusion Detection Systems (IDS) and firewalls. We propose in this work a method to monitor and detect the presence of hidden channels that are based on an essential monitoring protocol "Internet Control Message Protocol" (ICMP). We undergo the network traffic with a set of verifications ranging from simple fields verification to more complex pattern matching operations. To validate our idea, we have installed Ptunnel, a tool that allows to tunnel TCP connections to a remote host using ICMP echo request and reply packets. Our experimental results show the possibility to discover such malicious traffic with high performance.
An important aspect of precision medicine consists in patient-centered contextualization analyses that are used as part of biomedical interactive tools. Such analyses often harness data of large populations of patients from different research centers and can often benefit from a distributed implementation. However, performance and the security and privacy concerns of sharing sensitive biomedical data can become a major issue. We have investigated these issues in the context of a kidney transplanted patient contextualization project: the Kidney Transplantation Application (KITAPP). In this paper, we present a motivation for distributed implementations in this context, notably for computing percentiles for contextualization. We present a corresponding system architecture, motivate privacy and performance issues, and present a novel distributed implementation that is evaluated in a realistic multi-site setting.
Aim: Human leukocyte antigen (HLA) population genetics has been a historical field centralizing data resource. HLA genetics databases typically facilitate access to frequencies of allele, haplotype, and genotype format information. Among many resources, the Allele Frequency Net Database (AFND) is a typical centralized repository that allows users to research and analyze immune gene frequencies in different populations around the world. With the massive increase in medical data and the strengthening of data governance laws, the proposal for a new distributed and secure model for the historical centralization method in population genetics has become important. In this paper, a new model of HLA population genetic resources, an alternative distributed version of HLA databases has been developed. It allows users to perform the same research and analysis with other remote sites without sharing their original data and monitoring data access. Methods: This new version uses the Master/Worker distributed model and offers distributed algorithms for the calculation of allelic frequencies, haplotypic frequencies and for individual genotypic calculations. The new model was evaluated on a distributed testbed for experiment-driven research Grid’5000 and has obtained good results of accuracy and execution time compared to the original centralized scheme used by researchers. Results: The results show that distributed algorithm applied to HLA population genetics resources enables usage control and enables enforcing the security framework of the data-owning institution. It gives the same results for all counting methods in population immunogenetics. With the same frequencies’ estimations, it yields a much quicker computation time in many cases, in particular for large samples. Conclusions: Distributing previously centralized resources is an interesting perspective enhancing better control of data sharing.
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