DEVELOPMENT OF SECUREPLUS ANTIVIRUS WITH THE ARTIFICIAL IMMUNE SYSTEMMODEL

This paper is about Malware proliferation in the wide and the development of an Antivirus called Secure Plus. Malware is a generic name for malfunctioned program codes that could wreak destructive impacts on Information Technology critical infrastructures. These malware usually use various techniques to avoid being detected; usually they are encrypted using hybridized cryptographic algorithms. Malware may be detected using antivirus that can scan the database signatures already accumulated and stored by antivirus vendors in some server. These stored databases signatures can then be compared with zero-day malware through comparison with the benign software. The zero-day malware are of sophisticated program codes that can transmute into different transforming patterns; yet retain their portent functionalities attributes and are now of billion categories by deverse clones. This paper after over viewing the literatures on ground (and they are of large numerical numbers), attempts to make its contribution to the design and development of Antivirus that can detect those zero-day or metamorphic malware. This proposed Antivirus being developed is christened Secure Plus that applies the heuristic Artificial Immune System Algorithm for the design and development. The tested experimental outputs are provided as prove of the Secure Plus effectual functionality worthy of application but need further works through to detect malware proactively

Data Mining Methods For Malware Detection

This research investigates the use of data mining methods for malware (malicious programs) detection and proposed a framework as an alternative to the traditional signature detection methods. The traditional approaches using signatures to detect malicious programs fails for the new and unknown malwares case, where signatures are not available. We present a data mining framework to detect malicious programs. We collected, analyzed and processed several thousand malicious and clean programs to find out the best features and build models that can classify a given program into a malware or a clean class. Our research is closely related to information retrieval and classification techniques and borrows a number of ideas from the field. We used a vector space model to represent the programs in our collection. Our data mining framework includes two separate and distinct classes of experiments. The first are the supervised learning experiments that used a dataset, consisting of several thousand malicious and clean program samples to train, validate and test, an array of classifiers. In the second class of experiments, we proposed using sequential association analysis for feature selection and automatic signature extraction. With our experiments, we were able to achieve as high as 98.4% detection rate and as low as 1.9% false positive rate on novel malwares

Security and Privacy of Radio Frequency Identification

Tanenbaum, A.S. [Promotor]Crispo, B. [Copromotor

Current Research on HIV Drug Resistance

HIV drug resistance is an unavoidable challenge for HIV/AIDS professionals, and it has long been a major obstacle hindering the global effort to contain HIV/AIDS. Pathogens journal launched a topical collection in 2021 on “Current Research on HIV Drug Resistance”, aiming to catch the latest advancements in HIV resistance diagnosis, surveillance and research. This book compiles all articles published in this collection, combining original research and review articles on HIV drug resistance

Eco-Evolutionary Implications of Environmental Change Across Heterogeneous Landscapes

Species use a variety of mechanisms to adapt to environmental change. These range from spatially tracking optimal environments, to phenotypically plastic responses and evolutionary adaptation. Due to increases in anthropogenic influence on environments, characteristics of change such as their duration and magnitude are undergoing fundamental shifts away from the natural disturbance regimes that shaped species’ evolution. This dissertation uses empirical data and simulation models to examine the ecological and evolutionary consequences of environmental change across real, heterogeneous landscapes for multiple species, with an emphasis on anthropogenic changes. I used landscape genetics to evaluate the effects of urbanization on two native amphibian species, spotted salamanders (Ambystoma maculatum) and wood frogs (Lithobates sylvaticus). Population isolation was positively associated with local urbanization and lessened genetic diversity for both species. Resistance surface modelling revealed connectivity was diminished by developed land cover, light roads, interstates, and topography for both species, plus secondary roads and rivers for wood frogs, highlighting the influence of anthropogenic landscape features relative to natural features. Further study of a subset of wood frog populations revealed adaptive evolution associated with urban environments. I identified a set of 37 loci with the capacity to correctly reassign individuals into rural or urban populations with 87.5 and 93.8% accuracy, respectively. I developed an agent-based model to examine how gene flow, rates of change, and strength of landscape spatial and temporal autocorrelation influence abundance outcomes for species experiencing an environmental shift. Analysis of 36 environmental scenarios suggests that environmental variation, which is an emergent property of landscape autocorrelation, is negatively associated with the magnitude and duration of abundance declines following environmental change. Higher levels of gene flow lessened this effect, particularly in abrupt change scenarios, although gradual changes also resulted in demographic costs. Lastly, I used an investigation of an emerging disease in American lobsters (Homarus americanus) to study within-generation responses to environmental pressures. Using whole transcriptome shotgun sequencing I identified eight differentially expressed unigenes associated with the disease and seven related to environmental differences. Collectively, my dissertation provides numerous examples of how anthropogenically induced environmental change can direct ecological and evolutionary processes

Gamete differentiation : a genetic, biochemical and evolutionary study using Ectocarpus siliculosus

Despite its prevalence among eukaryotes, there are still many questions awaiting answers and plenty of discoveries to be made with regard to the mechanisms and maintenance of sexual reproduction. Brown algae (Phaeophyceae) represent a very interesting group to study the evolution of sexual reproduction, owing to the rich variation of life cycles, fertilization modes and sex determination systems displayed by this lineage. Particularly unique are various degrees of sexual dimorphism between gametes of closely related species, ranging from isogamy through anisogamy to oogamy. Therefore, the opportunity to compare these systems on the molecular level in Phaeophyceae promises advancement in our understanding of the evolution of sexual reproduction not only in algae, but in all living organisms. In this thesis we investigated the molecular basis of sexual reproduction and its link to reproductive isolation using a brown algal model organism Ectocarpus siliculosus. We constructed whole transcriptome libraries of male and female gametes to explore their functional differentiation at the gene expression level. We sequenced sex biased genes in a representative number of strains from distant geographical locations and varying stages of reproductive isolation, to search for signatures of positive selection. This thesis has offered the first insight into protist gametes transcriptomes, revealing complex, functional organization and differentiation according to the adopted sexual roles, as well as evolutionary variation of sex biased genes. Our study supports the relevance of algal systems in the research of sexual reproduction and provides a starting point for more exciting discoveries in that field

A quantitative FRET approach to characterize protein-protein interactions in living cells

The ability of proteins to specifically interact with each other is a key feature in the regulation of biological processes. Knowledge about interaction partners and characterization of protein-protein interactions contribute to the understanding of proper protein function and cell physiology. In particular, Förster resonance energy transfer (FRET) is a suitable method to analyze interactions between proteins in living cells. During the progress of this thesis, a quantitative FRET approach was established that aims to evaluate binding curves for interaction partners. Moreover, the quantitative FRET approach was applied to study biological questions, including the investigation of putative interaction partners of the endolysosomal ion channel two-pore channel 2 (TPC2), the Kv7.2 potassium channel and the photoreceptor-specific transmembrane protein peripherin 2. The FRET approach described in manuscript I computes calibrated FRET efficiencies from fluorescent measurements using three-filter cubes and correlates the FRET efficiencies to the concentration of donor and acceptor molecules to determine binding curves, which bear information about maximal FRET efficiencies and relative binding constants for individual FRET pairings. Calibration factors that represent the optical properties of the imaging setup and the fluorophores are crucial for quantitative measurements. A detailed description how to assess these factors is provided. The quantitative FRET approach is very robust as both donor-centric (E-FRET) and acceptor-centric (SE-FRET) efficiencies are obtained simultaneously from multiple cells. The method was further applied to investigate protein-protein interactions of membrane proteins. First of all, in manuscript II, an epilepsy-causing mutation in the Kv7.2 potassium channel was shown to be implicated in a reduced calmodulin binding affinity to the channel, which affects channel regulation. A second study identified SNARE proteins, such as syntaxin 7 and syntaxin 6, as novel interaction partners of the intracellular ion channel TPC2 (manuscript III), revealing TPC2 as a putative member of the late endosome-lysosome fusion machinery. In manuscript IV, the impact of polymorphic variants of TPC2 on channel dimerization and mTOR binding was investigated. Furthermore, in a study covered by manuscripts V and VI, rhodopsin as well as S- and M-opsins were identified as novel interaction partners of the retinal protein peripherin 2 in rods and cones, respectively. The binding domain underlying the interaction between peripherin 2 and rhodopsin, could be assigned to the fourth transmembrane domain of peripherin 2. Moreover, it could be demonstrated that disease-associated mutations in peripherin 2 attenuated this particular binding, suggesting differential pathophysiological consequences of disrupted interactions in rods and cones. In manuscript VIII, peripherin 2 and its homolog Rom-1 were shown to have opposing effects on rod outer segment targeting of disease-linked peripherin 2 mutants by evaluating their binding affinities. Peripherin 2 is a scaffold protein exclusively expressed in outer segments of rods and cones. As photoreceptors are polarized cells, FRET measurements were not only performed on transfected HEK293 cells but also on acutely isolated outer segments of virally transduced murine photoreceptors (manuscript VII). The results gained in this thesis demonstrate that protein interactions play a crucial role in the regulation of proper protein function. Loss of binding partners or a reduced binding affinity to particular proteins may result in pathophysiological conditions. A deeper knowledge about molecular interactions will contribute to the understanding of cellular mechanisms, etiology of diseases and may further evaluate putative targets of pharmacological interest