Adaptive Learning Based Whale Optimization and Convolutional Neural Network Algorithm for Distributed Denial of Service Attack Detection in Software Defined Network Environment

SDNs (Software Defined Networks) have emerged as a game-changing network concept. It can fulfill the ever-increasing needs of future networks and is increasingly being employed in data centres and operator networks. It does, however, confront certain fundamental security concerns, such as DDoS (Distributed Denial of Service) assaults. To address the aforementioned concerns, the ALWO+CNN method, which combines ALWOs (Adaptive Learning based Whale Optimizations) with CNNs (Convolution Neural Networks), is suggested in this paper. Initially, preprocessing is performed using the KMC (K-Means Clustering) algorithm, which is used to significantly reduce noise data. The preprocessed data is then used in the feature selection process, which is carried out by ALWOs. Its purpose is to pick out important and superfluous characteristics from the dataset. It enhances DDoS classification accuracy by using the best algorithms.  The selected characteristics are then used in the classification step, where CNNs are used to identify and categorize DDoS assaults efficiently. Finally, the ALWO+CNN algorithm is used to leverage the rate and asymmetry properties of the flows in order to detect suspicious flows specified by the detection trigger mechanism. The controller will next take the necessary steps to defend against DDoS assaults. The ALWO+CNN algorithm greatly improves detection accuracy and efficiency, as well as preventing DDoS assaults on SDNs. Based on the experimental results, it was determined that the suggested ALWO+CNN method outperforms current algorithms in terms of better accuracies, precisions, recalls, f-measures, and computational complexities

A thermostatically controlled miniature glass-house

This article does not have an abstract

The Application of DBSCAN Algorithm to Improve Variogram Estimation and Interpretation in Irregularly-Sampled Fields

The empirical variogram is a measure of spatial data correlation in geostatistical modeling and simulations. Typically, the empirical variogram is estimated for some defined lag intervals by applying method of moments on an underlying variogram cloud. Depending on the distribution of pair-wise lag values, the variogram cloud of an irregularly-sampled field may exhibit clusteredness. Issues of noisy, uninterpretable and inconsistent empirical variogram plots are commonly encountered in cases of irregularly-sampled fields with clustered variogram clouds. An insightful diagnosis of these problems and a practical solution are the subject of this paper. This research establishes the fact that these problems are caused by the neglect of variogram cloud cluster configurations when defining lag intervals. It is here shown that such neglect hinders the optimal use of spatial correlation information present in variogram clouds. Specifically, four sub-optimal effects are articulated in this paper as the consequence of the neglect. Consequently, this research presents an efficient cluster-analysis – driven technique for variogram estimation in cases of irregularly-sampled fields with clustered variogram clouds. The cluster analysis required for this technique is implemented using an unsupervised machine learning algorithm known as Density-based Spatial Clustering of Applications with Noise (DBSCAN). This technique has been applied to a real field to obtain a stable, interpretable and geologically consistent variogram plot. It has also been applied to a synthetic field and was found to give the lowest estimation error among other techniques. This technique would find usefulness in geo-modeling of natural resource deposits wherein irregular sampling is prevalent

Giant cell tumor of the temporal bone – a case report

BACKGROUND: Giant cell tumor is a benign but locally aggressive bone neoplasm which uncommonly involves the skull. The petrous portion of the temporal bone forms a rare location for this tumor. CASE PRESENTATION: The authors report a case of a large giant cell tumor involving the petrous and squamous portions of the temporal bone in a 26 year old male patient. He presented with right side severe hearing loss and facial paresis. Radical excision of the tumor was achieved but facial palsy could not be avoided. CONCLUSION: Radical excision of skull base giant cell tumor may be hazardous but if achieved is the optimal treatment and may be curative

Impact of Macrophage Inflammatory Protein-1α Deficiency on Atherosclerotic Lesion Formation, Hepatic Steatosis, and Adipose Tissue Expansion

Macrophage inflammatory protein-1α (CCL3) plays a well-known role in infectious and viral diseases; however, its contribution to atherosclerotic lesion formation and lipid metabolism has not been determined. Low density lipoprotein receptor deficient (LDLR−/−) mice were transplanted with bone marrow from CCL3−/− or C57BL/6 wild type donors. After 6 and 12 weeks on western diet (WD), recipients of CCL3−/− marrow demonstrated lower plasma cholesterol and triglyceride concentrations compared to recipients of C57BL/6 marrow. Atherosclerotic lesion area was significantly lower in female CCL3−/− recipients after 6 weeks and in male CCL3−/− recipients after 12 weeks of WD feeding (P<0.05). Surprisingly, male CCL3−/− recipients had a 50% decrease in adipose tissue mass after WD-feeding, and plasma insulin, and leptin levels were also significantly lower. These results were specific to CCL3, as LDLR−/− recipients of monocyte chemoattractant protein−/− (CCL2) marrow were not protected from the metabolic consequences of high fat feeding. Despite these improvements in LDLR−/− recipients of CCL3−/− marrow in the bone marrow transplantation (BMT) model, double knockout mice, globally deficient in both proteins, did not have decreased body weight, plasma lipids, or atherosclerosis compared with LDLR−/− controls. Finally, there were no differences in myeloid progenitors or leukocyte populations, indicating that changes in body weight and plasma lipids in CCL3−/− recipients was not due to differences in hematopoiesis. Taken together, these data implicate a role for CCL3 in lipid metabolism in hyperlipidemic mice following hematopoietic reconstitution

Identifying Molecular Effects of Diet through Systems Biology: Influence of Herring Diet on Sterol Metabolism and Protein Turnover in Mice

BACKGROUND: Changes in lifestyle have resulted in an epidemic development of obesity-related diseases that challenge the healthcare systems worldwide. To develop strategies to tackle this problem the focus is on diet to prevent the development of obesity-associated diseases such as cardiovascular disease (CVD). This will require methods for linking nutrient intake with specific metabolic processes in different tissues. METHODOLOGY/PRINCIPAL FINDING: Low-density lipoprotein receptor-deficient (Ldlr -/-) mice were fed a high fat/high sugar diet to mimic a westernized diet, being a major reason for development of obesity and atherosclerosis. The diets were supplemented with either beef or herring, and matched in macronutrient contents. Body composition, plasma lipids and aortic lesion areas were measured. Transcriptomes of metabolically important tissues, e.g. liver, muscle and adipose tissue were analyzed by an integrated approach with metabolic networks to directly map the metabolic effects of diet in these different tissues. Our analysis revealed a reduction in sterol metabolism and protein turnover at the transcriptional level in herring-fed mice. CONCLUSION: This study shows that an integrated analysis of transcriptome data using metabolic networks resulted in the identification of signature pathways. This could not have been achieved using standard clustering methods. In particular, this systems biology analysis could enrich the information content of biomedical or nutritional data where subtle changes in several tissues together affects body metabolism or disease progression. This could be applied to improve diets for subjects exposed to health risks associated with obesity

Metabolome Based Reaction Graphs of M. tuberculosis and M. leprae: A Comparative Network Analysis

BACKGROUND: Several types of networks, such as transcriptional, metabolic or protein-protein interaction networks of various organisms have been constructed, that have provided a variety of insights into metabolism and regulation. Here, we seek to exploit the reaction-based networks of three organisms for comparative genomics. We use concepts from spectral graph theory to systematically determine how differences in basic metabolism of organisms are reflected at the systems level and in the overall topological structures of their metabolic networks. METHODOLOGY/PRINCIPAL FINDINGS: Metabolome-based reaction networks of Mycobacterium tuberculosis, Mycobacterium leprae and Escherichia coli have been constructed based on the KEGG LIGAND database, followed by graph spectral analysis of the network to identify hubs as well as the sub-clustering of reactions. The shortest and alternate paths in the reaction networks have also been examined. Sub-cluster profiling demonstrates that reactions of the mycolic acid pathway in mycobacteria form a tightly connected sub-cluster. Identification of hubs reveals reactions involving glutamate to be central to mycobacterial metabolism, and pyruvate to be at the centre of the E. coli metabolome. The analysis of shortest paths between reactions has revealed several paths that are shorter than well established pathways. CONCLUSIONS: We conclude that severe downsizing of the leprae genome has not significantly altered the global structure of its reaction network but has reduced the total number of alternate paths between its reactions while keeping the shortest paths between them intact. The hubs in the mycobacterial networks that are absent in the human metabolome can be explored as potential drug targets. This work demonstrates the usefulness of constructing metabolome based networks of organisms and the feasibility of their analyses through graph spectral methods. The insights obtained from such studies provide a broad overview of the similarities and differences between organisms, taking comparative genomics studies to a higher dimension