Artificial Immune Systems - Models, algorithms and applications

Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun

Artificial immune systems based committee machine for classification application

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A new adaptive learning Artificial Immune System (AIS) based committee machine is developed in this thesis. The new proposed approach efficiently tackles the general problem of clustering high-dimensional data. In addition, it helps on deriving useful decision and results related to other application domains such classification and prediction. Artificial Immune System (AIS) is a branch of computational intelligence field inspired by the biological immune system, and has gained increasing interest among researchers in the development of immune-based models and techniques to solve diverse complex computational or engineering problems. This work presents some applications of AIS techniques to health problems, and a thorough survey of existing AIS models and algorithms. The main focus of this research is devoted to building an ensemble model integrating different AIS techniques (i.e. Artificial Immune Networks, Clonal Selection, and Negative Selection) for classification applications to achieve better classification results. A new AIS-based ensemble architecture with adaptive learning features is proposed by integrating different learning and adaptation techniques to overcome individual limitations and to achieve synergetic effects through the combination of these techniques. Various techniques related to the design and enhancements of the new adaptive learning architecture are studied, including a neuro-fuzzy based detector and an optimizer using particle swarm optimization method to achieve enhanced classification performance. An evaluation study was conducted to show the performance of the new proposed adaptive learning ensemble and to compare it to alternative combining techniques. Several experiments are presented using different medical datasets for the classification problem and findings and outcomes are discussed. The new adaptive learning architecture improves the accuracy of the ensemble. Moreover, there is an improvement over the existing aggregation techniques. The outcomes, assumptions and limitations of the proposed methods with its implications for further research in this area draw this research to its conclusion

Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses

The ongoing deployment of the fifth generation (5G) wireless networks constantly reveals limitations concerning its original concept as a key driver of Internet of Everything (IoE) applications. These 5G challenges are behind worldwide efforts to enable future networks, such as sixth generation (6G) networks, to efficiently support sophisticated applications ranging from autonomous driving capabilities to the Metaverse. Edge learning is a new and powerful approach to training models across distributed clients while protecting the privacy of their data. This approach is expected to be embedded within future network infrastructures, including 6G, to solve challenging problems such as resource management and behavior prediction. This survey article provides a holistic review of the most recent research focused on edge learning vulnerabilities and defenses for 6G-enabled IoT. We summarize the existing surveys on machine learning for 6G IoT security and machine learning-associated threats in three different learning modes: centralized, federated, and distributed. Then, we provide an overview of enabling emerging technologies for 6G IoT intelligence. Moreover, we provide a holistic survey of existing research on attacks against machine learning and classify threat models into eight categories, including backdoor attacks, adversarial examples, combined attacks, poisoning attacks, Sybil attacks, byzantine attacks, inference attacks, and dropping attacks. In addition, we provide a comprehensive and detailed taxonomy and a side-by-side comparison of the state-of-the-art defense methods against edge learning vulnerabilities. Finally, as new attacks and defense technologies are realized, new research and future overall prospects for 6G-enabled IoT are discussed

Computational investigation of systemic pathway responses in severe pneumonia among the Gambian children and infants

Pneumonia remains the leading cause of infectious mortality in under-five children, and the burden is highest in sub-Saharan Africa. To mitigate this burden, further knowledge is required to accelerate the development of innovative and cost-effective approaches. To gain a deeper insight into the pathogenesis of pneumonia, I investigated the central hypothesis that systemic pathway (cellular and molecular) responses underpin the development of severe pneumonia outcomes. Mainly, I compared whole blood transcriptomes between severe pneumonia cases (clinically stratified as mild, severe and very severe) and non-pneumonia community controls (prospectively matched by age and sex). In total, 803 whole blood RNA samples were collected from Gambian children (aged 2-59 months) between 2007 and 2010, of which, 518 passed laboratory quality control criteria for the microarray analysis. After data cleaning, the final database reduced to 503 samples including the training (n=345) and independent validation (n=158) data sets. To investigate the cellular responses, I applied computational deconvolution analysis to assess the variations of immune cell type proportions with pneumonia severity. To further enhance the computational performance, I applied a data fusion approach on 3,475 immune marker genes from different resources to derive an optimal and integrated blood marker list (IBML, m=277) for Neutrophils, Monocytes, NK, Dendritic, B and T cell types; which robustly performed better than the existing individual resources. Using the IBML resource, pneumonia severity was significantly associated with the depletion of B, T, Dendritic and NK cell types, and the elevation of Monocytes and neutrophil proportions (P-value<0.001). At the molecular level, pneumonia severity was associated (false discovery rate<0.05) with a battery of systemic pathway (innate, adaptive and metabolic) responses in a range of biomedical databases. While the up-regulation of inflammatory innate responses was also observed in mild cases, severe pneumonia cases were predominantly associated with the co-inhibition of the cells of the adaptive immune response (B and T) and Natural killer cells, and the up-regulation of fatty acid and lipid metabolism. While most of these findings were anticipated, the involvement of NK cells was unexpected, and potentially presents a novel immune-modulation target for mitigating the burden of pneumonia. Together, the cellular and molecular pathways responses consistently support the central hypothesis that systemic pathway responses contribute significantly to the development of severe pneumonia outcomes. Clinically, the identification and appropriate treatment of patients at the higher risk of developing severe pneumonia outcomes remains the major challenge. To address that, I applied supervised machine-learning approaches on cellular pathway based transcriptomic features; and derived a 33-gene classifier (representing the NK, T, and neutrophils cell types), which accurately detected severe pneumonia cases in both the training (leave-one-out cross-validated accuracy=99%) and independent validation (accuracy=98%) datasets. Independently, similar performance (98% in each dataset) was associated with a subset (m=18) of the validated 52-gene neonatal sepsis classifier. Conversely, at least 75% of the cellular biomarkers were differentially expressed (false discovery rate<0.05) in bacterial neonatal sepsis. Further, very severe pneumonia cases were predominantly associated with antibacterial responses; and mild pneumonia cases with blood-culture-confirmed positivity were also associated with an increased frequency of differentially expressed genes. These findings suggest the significant contribution of bacterial septicaemia in the development of serious pneumonia outcomes. Together, this study highlights the future potential of host-derived systemic biomarkers for early identification and novel treatment modalities of high-risk cases presenting at a resource-constrained clinic with mild pneumonia. However, further validation studies are required

Innate lymphoid cell plasticity and heterogeneity in human tissues

The human immune system is a vital mechanism that protects the host from outside threats. The two main branches of this system, consisting of innate and adaptive immunity, aid the host when dangers of both imminent and protracted nature occur. Innate lymphoid cells (ILC) play a key role in innate immunity and are classified into distinct groups based on their function, transcriptional profile and development. Their discovery is young, with much research needed to understand all aspects of their phenotype, genotype, behavior in homeostasis and disease. This thesis describes the breakthroughs my collaborators and I were able to reach during my five years of doctoral studies, in order to better understand the biology and physiology of ILC. In the first part of this thesis I describe the history and classification of ILC, the efforts being done so far by the field to understand their development, and the possible combinations of changes in ILC status, known as plasticity or trans-differentiation properties of ILC under particular environments or stimuli. As Paper II’s results are based on a cohort of Inflammatory Bowel Disease (IBD) samples, and in Paper I we analyze ILC in intestinal biopsies from IBD patients, I also outline the main characteristics representing this disorder while focusing on the role of ILC in IBD. Next, after defining the aim of my studies, I describe how the data was obtained, as a big part of my work consisted in learning how to handle methods and technologies such as flow cytometry, fluorescence-activated cell sorting (FACS) and single cell RNA sequencing, among others. Finally, a discussion of the results is aimed at highlighting the major breakthroughs achieved. In Paper I, we set out to better understand the function of the Ikaros family of transcription factors in ILC, focusing our efforts on IKZF3 (encoding Aiolos) and its role in ILC trans- differentiation via a drug-induced silencing approach with the immune-modulatory agent lenalidomide. In Paper II, a large cohort of IBD samples was analyzed in order to find disturbances in peripheral blood ILC protein expression. We were able to uncover differences in several activation proteins in the IBD cohort, when compared to a like-sized cohort of samples from healthy controls. In Paper III, a big effort was put into implementing Smart- seq2 RNA sequencing technology to a large number of ILC from a variety of tissues. This allowed us to better understand the heterogeneity of ILC in the circulation, secondary lymphoid and mucosal tissues. We generated a large dataset that will require time to be exploited in full, constituting a roadmap for future studies aimed at understanding human ILC biology and function. In summary, the work presented in this thesis provides findings and datasets that have the potential to advance the ILC field

SYNERGY OF BUILDING CYBERSECURITY SYSTEMS

The development of the modern world community is closely related to advances in computing resources and cyberspace. The formation and expansion of the range of services is based on the achievements of mankind in the field of high technologies. However, the rapid growth of computing resources, the emergence of a full-scale quantum computer tightens the requirements for security systems not only for information and communication systems, but also for cyber-physical systems and technologies. The methodological foundations of building security systems for critical infrastructure facilities based on modeling the processes of behavior of antagonistic agents in security systems are discussed in the first chapter. The concept of information security in social networks, based on mathematical models of data protection, taking into account the influence of specific parameters of the social network, the effects on the network are proposed in second chapter. The nonlinear relationships of the parameters of the defense system, attacks, social networks, as well as the influence of individual characteristics of users and the nature of the relationships between them, takes into account. In the third section, practical aspects of the methodology for constructing post-quantum algorithms for asymmetric McEliece and Niederreiter cryptosystems on algebraic codes (elliptic and modified elliptic codes), their mathematical models and practical algorithms are considered. Hybrid crypto-code constructions of McEliece and Niederreiter on defective codes are proposed. They can significantly reduce the energy costs for implementation, while ensuring the required level of cryptographic strength of the system as a whole. The concept of security of corporate information and educational systems based on the construction of an adaptive information security system is proposed. ISBN 978-617-7319-31-2 (on-line)ISBN 978-617-7319-32-9 (print) ------------------------------------------------------------------------------------------------------------------ How to Cite: Yevseiev, S., Ponomarenko, V., Laptiev, O., Milov, O., Korol, O., Milevskyi, S. et. al.; Yevseiev, S., Ponomarenko, V., Laptiev, O., Milov, O. (Eds.) (2021). Synergy of building cybersecurity systems. Kharkiv: РС ТЕСHNOLOGY СЕNTЕR, 188. doi: http://doi.org/10.15587/978-617-7319-31-2 ------------------------------------------------------------------------------------------------------------------ Indexing: &nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Розвиток сучасної світової спільноти тісно пов’язаний з досягненнями в області обчислювальних ресурсів і кіберпростору. Формування та розширення асортименту послуг базується на досягненнях людства у галузі високих технологій. Однак стрімке зростання обчислювальних ресурсів, поява повномасштабного квантового комп’ютера посилює вимоги до систем безпеки не тільки інформаційно-комунікаційних, але і до кіберфізичних систем і технологій. У першому розділі обговорюються методологічні основи побудови систем безпеки для об'єктів критичної інфраструктури на основі моделювання процесів поведінки антагоністичних агентів у систем безпеки. У другому розділі пропонується концепція інформаційної безпеки в соціальних мережах, яка заснована на математичних моделях захисту даних, з урахуванням впливу конкретних параметрів соціальної мережі та наслідків для неї. Враховуються нелінійні взаємозв'язки параметрів системи захисту, атак, соціальних мереж, а також вплив індивідуальних характеристик користувачів і характеру взаємовідносин між ними. У третьому розділі розглядаються практичні аспекти методології побудови постквантових алгоритмів для асиметричних криптосистем Мак-Еліса та Нідеррейтера на алгебраїчних кодах (еліптичних та модифікованих еліптичних кодах), їх математичні моделі та практичні алгоритми. Запропоновано гібридні конструкції криптокоду Мак-Еліса та Нідеррейтера на дефектних кодах. Вони дозволяють істотно знизити енергетичні витрати на реалізацію, забезпечуючи при цьому необхідний рівень криптографічної стійкості системи в цілому. Запропоновано концепцію безпеки корпоративних інформаційних та освітніх систем, які засновані на побудові адаптивної системи захисту інформації. ISBN 978-617-7319-31-2 (on-line)ISBN 978-617-7319-32-9 (print) ------------------------------------------------------------------------------------------------------------------ Як цитувати: Yevseiev, S., Ponomarenko, V., Laptiev, O., Milov, O., Korol, O., Milevskyi, S. et. al.; Yevseiev, S., Ponomarenko, V., Laptiev, O., Milov, O. (Eds.) (2021). Synergy of building cybersecurity systems. Kharkiv: РС ТЕСHNOLOGY СЕNTЕR, 188. doi: http://doi.org/10.15587/978-617-7319-31-2 ------------------------------------------------------------------------------------------------------------------ Індексація: &nbsp;&nbsp;&nbsp;&nbsp; &nbsp;&nbsp;&nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp

Systems Analytics and Integration of Big Omics Data

A “genotype"" is essentially an organism's full hereditary information which is obtained from its parents. A ""phenotype"" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This “Big Data” is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene–environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome