Learning Latent Distribution for Distinguishing Network Traffic in Intrusion Detection System

© 2019 IEEE. We develop a novel deep learning model, Multi-distributed Variational AutoEncoder (MVAE), for the network intrusion detection. To make the traffic more distinguishable, MVAE introduces the label information of data samples into the Kullback-Leibler (KL) term of the loss function of Variational AutoEncoder (VAE). This label information allows MVAEs to force/partition network data samples into different classes with different regions in the latent feature space. As a result, the network traffic samples are more distinguishable in the new representation space (i.e., the latent feature space of MVAE), thereby improving the accuracy in detecting intrusions. To evaluate the efficiency of the proposed solution, we carry out intensive experiments on two popular network intrusion datasets, i.e., NSL-KDD and UNSW-NB15 under four conventional classifiers including Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The experimental results demonstrate that our proposed approach can significantly improve the accuracy of intrusion detection algorithms up to 24.6% compared to the original one (using area under the curve metric)

Cyber security analysis of connected vehicles

\ua9 2024 The Authors. IET Intelligent Transport Systems published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.The sensor-enabled in-vehicle communication and infrastructure-centric vehicle-to-everything (V2X) communications have significantly contributed to the spark in the amount of data exchange in the connected and autonomous vehicles (CAV) environment. The growing vehicular communications pose a potential cyber security risk considering online vehicle hijacking. Therefore, there is a critical need to prioritize the cyber security issues in the CAV research theme. In this context, this paper presents a cyber security analysis of connected vehicle traffic environments (CyACV). Specifically, potential cyber security attacks in CAV are critically investigated and validated via experimental data sets. Trust in V2X communication for connected vehicles is explored in detail focusing on trust computation and trust management approaches and related challenges. A wide range of trust-based cyber security solutions for CAV have been critically investigated considering their strengths and weaknesses. Open research directions have been highlighted as potential new research themes in CAV cyber security area

Effective Dark Matter Model: Relic density, CDMS II, Fermi LAT and LHC

The Cryogenic Dark Matter Search recently announced the observation of two signal events with a 77% confidence level. Although statistically inconclusive, it is nevertheless suggestive. In this work we present a model-independent analysis on the implication of a positive signal in dark matter scattering off nuclei. Assuming the interaction between (scalar, fermion or vector) dark matter and the standard model induced by unknown new physics at the scale $\Lambda$, we examine various dimension-6 tree-level induced operators and constrain them using the current experimental data, e.g. the WMAP data of the relic abundance, CDMS II direct detection of the spin-independent scattering, and indirect detection data (Fermi LAT cosmic gamma-ray), etc. Finally, the LHC reach is also explored

Monodisperse α-Fe2O3 Mesoporous Microspheres: One-Step NaCl-Assisted Microwave-Solvothermal Preparation, Size Control and Photocatalytic Property

A simple one-step NaCl-assisted microwave-solvothermal method has been developed for the preparation of monodisperse α-Fe2O3 mesoporous microspheres. In this approach, Fe(NO3)3 · 9H2O is used as the iron source, and polyvinylpyrrolidone (PVP) acts as a surfactant in the presence of NaCl in mixed solvents of H2O and ethanol. Under the present experimental conditions, monodisperse α-Fe2O3 mesoporous microspheres can form via oriented attachment of α-Fe2O3 nanocrystals. One of the advantages of this method is that the size of α-Fe2O3 mesoporous microspheres can be adjusted in the range from ca. 170 to ca. 260 nm by changing the experimental parameters. High photocatalytic activities in the degradation of salicylic acid are observed for α-Fe2O3 mesoporous microspheres with different specific surface areas

Contractile and structural properties of detrusor from children with neurogenic lower urinary tract dysfunction

Neurogenic lower urinary tract (NLUT) dysfunction in paediatric patients can arise after congenital or acquired conditions that affect bladder innervation. With some patients, urinary tract dysfunction remains and is more difficult to treat without understanding the pathophysiology. We measured in vitro detrusor smooth muscle function of samples from such bladders and any association with altered Wnt-signalling pathways that contribute to both foetal development and connective tissue deposition. A comparator group was tissue from children with normally functioning bladders. Nerve-mediated and agonist-induced contractile responses and passive stiffness were measured. Histology measured smooth muscle and connective tissue proportions, and multiplex immunohistochemistry recorded expression of protein targets associated with Wnt-signalling pathways. Detrusor from the NLUT group had reduced contractility and greater stiffness, associated with increased connective tissue content. Immunohistochemistry showed no major changes to Wnt-signalling components except down-regulation of c-Myc, a multifunctional regulator of gene transcription. NLUT is a diverse term for several diagnoses that disrupt bladder innervation. While we cannot speculate about the reasons for these pathophysiological changes, their recognition should guide research to understand their ultimate causes and develop strategies to attenuate and even reverse them. The role of changes to the Wnt-signalling pathways was minor. View Full-Tex

Coordinated Sumoylation and Ubiquitination Modulate EGF Induced EGR1 Expression and Stability

Abstract: Background: Human early growth response-1 (EGR1) is a member of the zing-finger family of transcription factors induced by a range of molecular and environmental stimuli including epidermal growth factor (EGF). In a recently published paper we demonstrated that integrin/EGFR cross-talk was required for Egr1 expression through activation of the Erk1/2 and PI3K/Akt/Forkhead pathways. EGR1 activity and stability can be influenced by many different post-translational modifications such as acetylation, phosphorylation, ubiquitination and the recently discovered sumoylation. The aim of this work was to assess the influence of sumoylation on EGF induced Egr1 expression and/or stability. Methods: We modulated the expression of proteins involved in the sumoylation process in ECV304 cells by transient transfection and evaluated Egr1 expression in response to EGF treatment at mRNA and protein levels. Results: We demonstrated that in ECV304 cells Egr1 was transiently induced upon EGF treatment and a fraction of the endogenous protein was sumoylated. Moreover, SUMO-1/Ubc9 over-expression stabilized EGF induced ERK1/2 phosphorylation and increased Egr1 gene transcription. Conversely, in SUMO-1/Ubc9 transfected cells, EGR1 protein levels were strongly reduced. Data obtained from protein expression and ubiquitination analysis, in the presence of the proteasome inhibitor MG132, suggested that upon EGF stimuli EGR1 sumoylation enhanced its turnover, increasing ubiquitination and proteasome mediated degradation. Conclusions: Here we demonstrate that SUMO-1 modification improving EGR1 ubiquitination is involved in the modulation of its stability upon EGF mediated induction

Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm

Over the past five decades, k-means has become the clustering algorithm of choice in many application domains primarily due to its simplicity, time/space efficiency, and invariance to the ordering of the data points. Unfortunately, the algorithm's sensitivity to the initial selection of the cluster centers remains to be its most serious drawback. Numerous initialization methods have been proposed to address this drawback. Many of these methods, however, have time complexity superlinear in the number of data points, which makes them impractical for large data sets. On the other hand, linear methods are often random and/or sensitive to the ordering of the data points. These methods are generally unreliable in that the quality of their results is unpredictable. Therefore, it is common practice to perform multiple runs of such methods and take the output of the run that produces the best results. Such a practice, however, greatly increases the computational requirements of the otherwise highly efficient k-means algorithm. In this chapter, we investigate the empirical performance of six linear, deterministic (non-random), and order-invariant k-means initialization methods on a large and diverse collection of data sets from the UCI Machine Learning Repository. The results demonstrate that two relatively unknown hierarchical initialization methods due to Su and Dy outperform the remaining four methods with respect to two objective effectiveness criteria. In addition, a recent method due to Erisoglu et al. performs surprisingly poorly.Comment: 21 pages, 2 figures, 5 tables, Partitional Clustering Algorithms (Springer, 2014). arXiv admin note: substantial text overlap with arXiv:1304.7465, arXiv:1209.196

Identification of the initial molecular changes in response to circulating angiogenic cells-mediated therapy in critical limb ischemia

BackgroundCritical limb ischemia (CLI) constitutes the most aggressive form of peripheral arterial occlusive disease, characterized by the blockade of arteries supplying blood to the lower extremities, significantly diminishing oxygen and nutrient supply. CLI patients usually undergo amputation of fingers, feet, or extremities, with a high risk of mortality due to associated comorbidities.Circulating angiogenic cells (CACs), also known as early endothelial progenitor cells, constitute promising candidates for cell therapy in CLI due to their assigned vascular regenerative properties. Preclinical and clinical assays with CACs have shown promising results. A better understanding of how these cells participate in vascular regeneration would significantly help to potentiate their role in revascularization.Herein, we analyzed the initial molecular mechanisms triggered by human CACs after being administered to a murine model of CLI, in order to understand how these cells promote angiogenesis within the ischemic tissues.MethodsBalb-c nude mice (n:24) were distributed in four different groups: healthy controls (C, n:4), shams (SH, n:4), and ischemic mice (after femoral ligation) that received either 50 mu l physiological serum (SC, n:8) or 5x10(5) human CACs (SE, n:8). Ischemic mice were sacrificed on days 2 and 4 (n:4/group/day), and immunohistochemistry assays and qPCR amplification of Alu-human-specific sequences were carried out for cell detection and vascular density measurements. Additionally, a label-free MS-based quantitative approach was performed to identify protein changes related.ResultsAdministration of CACs induced in the ischemic tissues an increase in the number of blood vessels as well as the diameter size compared to ischemic, non-treated mice, although the number of CACs decreased within time. The initial protein changes taking place in response to ischemia and more importantly, right after administration of CACs to CLI mice, are shown.ConclusionsOur results indicate that CACs migrate to the injured area; moreover, they trigger protein changes correlated with cell migration, cell death, angiogenesis, and arteriogenesis in the host. These changes indicate that CACs promote from the beginning an increase in the number of vessels as well as the development of an appropriate vascular network.Institute of Health Carlos III, ISCIII; Junta de Andaluci

Strong Ultraviolet Pulse From a Newborn Type Ia Supernova

Type Ia supernovae are destructive explosions of carbon oxygen white dwarfs. Although they are used empirically to measure cosmological distances, the nature of their progenitors remains mysterious, One of the leading progenitor models, called the single degenerate channel, hypothesizes that a white dwarf accretes matter from a companion star and the resulting increase in its central pressure and temperature ignites thermonuclear explosion. Here we report observations of strong but declining ultraviolet emission from a Type Ia supernova within four days of its explosion. This emission is consistent with theoretical expectations of collision between material ejected by the supernova and a companion star, and therefore provides evidence that some Type Ia supernovae arise from the single degenerate channel.Comment: Accepted for publication on the 21 May 2015 issue of Natur

Facilitating functional annotation of chicken microarray data

<p>Abstract</p> <p>Background</p> <p>Modeling results from chicken microarray studies is challenging for researchers due to little functional annotation associated with these arrays. The Affymetrix GenChip chicken genome array, one of the biggest arrays that serve as a key research tool for the study of chicken functional genomics, is among the few arrays that link gene products to Gene Ontology (GO). However the GO annotation data presented by Affymetrix is incomplete, for example, they do not show references linked to manually annotated functions. In addition, there is no tool that facilitates microarray researchers to directly retrieve functional annotations for their datasets from the annotated arrays. This costs researchers amount of time in searching multiple GO databases for functional information.</p> <p>Results</p> <p>We have improved the breadth of functional annotations of the gene products associated with probesets on the Affymetrix chicken genome array by 45% and the quality of annotation by 14%. We have also identified the most significant diseases and disorders, different types of genes, and known drug targets represented on Affymetrix chicken genome array. To facilitate functional annotation of other arrays and microarray experimental datasets we developed an Array GO Mapper (<it>AGOM</it>) tool to help researchers to quickly retrieve corresponding functional information for their dataset.</p> <p>Conclusion</p> <p>Results from this study will directly facilitate annotation of other chicken arrays and microarray experimental datasets. Researchers will be able to quickly model their microarray dataset into more reliable biological functional information by using <it>AGOM </it>tool. The disease, disorders, gene types and drug targets revealed in the study will allow researchers to learn more about how genes function in complex biological systems and may lead to new drug discovery and development of therapies. The GO annotation data generated will be available for public use via AgBase website and will be updated on regular basis.</p