SANA - Network Protection through artificial Immunity

Current network protection systems use a collection of intelligent components - e.g. classifiers or rule-based firewall systems to detect intrusions and anomalies and to secure a network against viruses, worms, or trojans. However, these network systems rely on individuality and support an architecture with less collaborative work of the protection components. They give less administration support for maintenance, but offer a large number of individual single points of failures - an ideal situation for network attacks to succeed. In this work, we discuss the required features, the performance, and the problems of a distributed protection system called SANA. It consists of a cooperative architecture, it is motivated by the human immune system, where the components correspond to artificial immune cells that are connected for their collaborative work. SANA promises a better protection against intruders than common known protection systems through an adaptive self-management while keeping the resources efficiently by an intelligent reduction of redundant tasks. We introduce a library of several novel and common used protection components and evaluate the performance of SANA by a proof-of-concept implementation.Comment: 5 page

In silico CD4+, CD8+ T-cell and B-cell immunity associated immunogenic epitope prediction and HLA distribution analysis of Zika virus

Zika virus (ZIKV) is a mosquito-borne flavivirus distributed all over Africa, South America and Asia. The infection with the virus may cause acute febrile sickness that clinically resembles dengue fever, yet there is no vaccine, no satisfactory treatment, and no means of evaluating the risk of the disease or prognosis in the infected people. In the present study, the efficacy of the host\u27s immune response in reducing the risk of infectious diseases was taken into account to carry out immuno-informatics driven epitope screening strategy of vaccine candidates against ZIKV. In this study, HLA distribution analysis was done to ensure the coverage of the vast majority of the population. Systematic screening of effective dominant immunogens was done with the help of Immune Epitope & ABCPred databases. The outcomes suggested that the predicted epitopes may be protective immunogens with highly conserved sequences and bear potential to induce both protective neutralizing antibodies, T & B cell responses. A total of 25 CD4+ and 16 CD8+ peptides were screened for T-cell mediated immunity. The predicted epitope TGLDFSDLYYLTMNNKHWLV was selected as a highly immunogenic epitope for humoral immunity. These peptides were further screened as non-toxic, immunogenic and non-mutated residues of envelop viral protein. The predicted epitope could work as suitable candidate(s) for peptide based vaccine development. Further, experimental validation of these epitopes is warranted to ensure the potential of B- and T-cells stimulation for their efficient use as vaccine candidates, and as diagnostic agents against ZIKV

Comparison of Tagging Technologies for Safeguards of Copper Canisters for Nuclear Spent Fuel

Several countries are planning to store nuclear spent fuel in long term geological repositories, preserved by copper canisters with an iron insert. This new approach involves many challenging problems and one is to satisfy safeguards requirements: the Continuity of Knowledge (CoK) of the fuel must be kept from the encapsulation plant up to the final repository. To date, no measurement system has been suggested for a unique identification and authentication. Following the list of the most important safeguards, safety and security requirements for copper canisters identification and authentication, a review of conventional tagging technologies and measurement systems for nuclear items is reported in this paper. The aim of this study is to verify to what extent each technology could be potentially used for keeping the CoK of copper canisters. Several tagging methods are briefly described and compared, discussing advantages and disadvantages

An Evolutionary Algorithm to Generate Ellipsoid Detectors for Negative Selection

Negative selection is a process from the biological immune system that can be applied to two-class (self and nonself) classification problems. Negative selection uses only one class (self) for training, which results in detectors for the other class (nonself). This paradigm is especially useful for problems in which only one class is available for training, such as network intrusion detection. Previous work has investigated hyper-rectangles and hyper-spheres as geometric detectors. This work proposes ellipsoids as geometric detectors. First, the author establishes a mathematical model for ellipsoids. He develops an algorithm to generate ellipsoids by training on only one class of data. Ellipsoid mutation operators, an objective function, and a convergence technique are described for the evolutionary algorithm that generates ellipsoid detectors. Testing on several data sets validates this approach by showing that the algorithm generates good ellipsoid detectors. Against artificial data sets, the detectors generated by the algorithm match more than 90% of nonself data with no false alarms. Against a subset of data from the 1999 DARPA MIT intrusion detection data, the ellipsoids generated by the algorithm detected approximately 98% of nonself (intrusions) with an approximate 0% false alarm rate