Botnet detection in the Internet of Things using deep learning approaches.

The recent growth of the Internet of Things (IoT) has resulted in a rise in IoT based DDoS attacks. This paper presents a solution to the detection of botnet activity within consumer IoT devices and networks. A novel application of Deep Learning is used to develop a detection model based on a Bidirectional Long Short Term Memory based Recurrent Neural Network (BLSTM-RNN). Word Embedding is used for text recognition and conversion of attack packets into tokenised integer format. The developed BLSTM-RNN detection model is compared to a LSTM-RNN for detecting four attack vectors used by the mirai botnet, and evaluated for accuracy and loss. The paper demonstrates that although the bidirectional approach adds overhead to each epoch and increases processing time, it proves to be a better progressive model over time. A labelled dataset was generated as part of this research, and is available upon request

Towards situational awareness of botnet activity in the Internet of Things

An IoT botnet detection model is designed to detect anomalous attack traffic utilised by the mirai botnet malware. The model uses a novel application of Deep Bidirectional Long Short Term Memory based Recurrent Neural Network (BLSTMRNN), in conjunction with Word Embedding, to convert string data found in captured packets, into a format usable by the BLSTM-RNN. In doing so, this paper presents a solution to the problem of detecting and making consumers situationally aware when their IoT devices are infected, and forms part of a botnet. The proposed model addresses the issue of detection, and returns high accuracy and low loss metrics for four attack vectors used by the mirai botnet malware, with only one attack vector shown to be difficult to detect and predict. A labelled dataset was generated and used for all experiments, to test and validate the accuracy and data loss in the detection model. This dataset is available upon request

Effects of radiation and manganese oxide nanoparticles on human glioblastoma cell line U-87 MG glycolysis

Gliomas are the most common type of malignant brain tumors. Standard treatment of gliomas consists of surgical excision of the tumor with subsequent chemotherapy and radiotherapy. Tumor cells are characterized by rapid division with an increased uptake of glucose and its catabolism during glycolysis. To maintain rapid division, the level of glycolysis of the tumor cell is significantly increased, compared with normal cells. It is known that some nanoparticles (NP) have the property of accumulating in tumors. In particular, NPs of manganese oxide can penetrate into the brain and, with considerable accumulation, cause toxic effects. These facts served as a prerequisite for studying the effects of manganese oxide NPs on the viability of glioma cells. The purpose of this work was to study the effects of manganese oxide NPs, as well as their combination with gamma irradiation on the glycolysis of glioma cells. The cells were irradiated using the research radiobiological gamma-installation IGUR-1 based on 137Cs. The level of cell glycolysis was determined using the standard glycolytic stress test on a Seahorse XFp platform. Cell viability was determined using the ViaCount reagent staining of living and dead cells. Their count was performed using flow cytometry. We showed that the glycolysis of U-87 MG glioma cells was significantly reduced when incubated for 48 hours with manganese oxide NPs. Irradiation in combination with NPs or alone did not have significant effects on glycolysis of gliomas. Glioma incubation with manganese oxide NPs for 72 hours led to a significant reduction in cell viability. This study may be useful for the development of new therapies and diagnosis of gliomas

Study of the neuronal response to olfactory stimuli in control and LPS-stimulated mice by functional magnetic resonance imaging

Olfactory perception plays the key role in the inter­action of animals with biotic factors of the species-specific econiche. Identification of odorants informs nocturnal animals about social environment, presence of predators, or infected food. Olfactory efficiency depends on physiological conditions; in particular, odor sensitivity can be changed by infection. This work considers use of fMRI in the study of the influence of innate immunity activation on neuronal response during perception and differentiation of socially significant (2.5-dimethylpyrazine, 2-heptanon) and socially insignificant (1-hexanol and isoprene) olfactory stimuli by CD-1 mice. We stimulated innate immunity by intraperitoneal injection of bacterial lipopolysaccharide (LPS) at the dose 500 µg/kg three hours before tomography. Urethane anesthesia was used during MRI trail. Odor stimulation was done with a lab-made metering unit for supplying standard doses of volatile organic compounds. The supply of olfactory stimuli induced activation of neurons in the primary perceptual center and the centers of secondary processing of olfactory information. Olfactory stimulus type affected neuronal response rate in an olfactory bulb but did not affect response parameters in other brain regions studied. This increase in neuronal activity is likely to be of adaptive significance as a mechanism supporting olfactory sensitivity increase, which plays the key role in the identification of potential sources of infection

ATP Release from Dying Autophagic Cells and Their Phagocytosis Are Crucial for Inflammasome Activation in Macrophages

Pathogen-activated and damage-associated molecular patterns activate the inflammasome in macrophages. We report that mouse macrophages release IL-1β while co-incubated with pro-B (Ba/F3) cells dying, as a result of IL-3 withdrawal, by apoptosis with autophagy, but not when they are co-incubated with living, apoptotic, necrotic or necrostatin-1 treated cells. NALP3-deficient macrophages display reduced IL-1β secretion, which is also inhibited in macrophages deficient in caspase-1 or pre-treated with its inhibitor. This finding demonstrates that the inflammasome is activated during phagocytosis of dying autophagic cells. We show that activation of NALP3 depends on phagocytosis of dying cells, ATP release through pannexin-1 channels of dying autophagic cells, P2X7 purinergic receptor activation, and on consequent potassium efflux. Dying autophagic Ba/F3 cells injected intraperitoneally in mice recruit neutrophils and thereby induce acute inflammation. These findings demonstrate that NALP3 performs key upstream functions in inflammasome activation in mouse macrophages engulfing dying autophagic cells, and that these functions lead to pro-inflammatory responses

Whole-exome sequencing in undiagnosed genetic diseases: interpreting 119 trios

Purpose: Despite the recognized clinical value of exome-based diagnostics, methods for comprehensive genomic interpretation remain immature. Diagnoses are based on known or presumed pathogenic variants in genes already associated with a similar phenotype. Here, we extend this paradigm by evaluating novel bioinformatics approaches to aid identification of new gene–disease associations. Methods: We analyzed 119 trios to identify both diagnostic genotypes in known genes and candidate genotypes in novel genes. We considered qualifying genotypes based on their population frequency and in silico predicted effects we also characterized the patterns of genotypes enriched among this collection of patients. Results: We obtained a genetic diagnosis for 29 (24%) of our patients. We showed that patients carried an excess of damaging de novo mutations in intolerant genes, particularly those shown to be essential in mice (P = 3.4 × 10−8). This enrichment is only partially explained by mutations found in known disease-causing genes. Conclusion: This work indicates that the application of appropriate bioinformatics analyses to clinical sequence data can also help implicate novel disease genes and suggest expanded phenotypes for known disease genes. These analyses further suggest that some cases resolved by whole-exome sequencing will have direct therapeutic implications

Measurement of the Bottom-Strange Meson Mixing Phase in the Full CDF Data Set

We report a measurement of the bottom-strange meson mixing phase \beta_s using the time evolution of B0_s -> J/\psi (->\mu+\mu-) \phi (-> K+ K-) decays in which the quark-flavor content of the bottom-strange meson is identified at production. This measurement uses the full data set of proton-antiproton collisions at sqrt(s)= 1.96 TeV collected by the Collider Detector experiment at the Fermilab Tevatron, corresponding to 9.6 fb-1 of integrated luminosity. We report confidence regions in the two-dimensional space of \beta_s and the B0_s decay-width difference \Delta\Gamma_s, and measure \beta_s in [-\pi/2, -1.51] U [-0.06, 0.30] U [1.26, \pi/2] at the 68% confidence level, in agreement with the standard model expectation. Assuming the standard model value of \beta_s, we also determine \Delta\Gamma_s = 0.068 +- 0.026 (stat) +- 0.009 (syst) ps-1 and the mean B0_s lifetime, \tau_s = 1.528 +- 0.019 (stat) +- 0.009 (syst) ps, which are consistent and competitive with determinations by other experiments.Comment: 8 pages, 2 figures, Phys. Rev. Lett 109, 171802 (2012

MiDAS 4: A global catalogue of full-length 16S rRNA gene sequences and taxonomy for studies of bacterial communities in wastewater treatment plants

Microbial communities are responsible for biological wastewater treatment, but our knowledge of their diversity and function is still poor. Here, we sequence more than 5 million high-quality, full-length 16S rRNA gene sequences from 740 wastewater treatment plants (WWTPs) across the world and use the sequences to construct the ‘MiDAS 4’ database. MiDAS 4 is an amplicon sequence variant resolved, full-length 16S rRNA gene reference database with a comprehensive taxonomy from domain to species level for all sequences. We use an independent dataset (269 WWTPs) to show that MiDAS 4, compared to commonly used universal reference databases, provides a better coverage for WWTP bacteria and an improved rate of genus and species level classification. Taking advantage of MiDAS 4, we carry out an amplicon-based, global-scale microbial community profiling of activated sludge plants using two common sets of primers targeting regions of the 16S rRNA gene, revealing how environmental conditions and biogeography shape the activated sludge microbiota. We also identify core and conditionally rare or abundant taxa, encompassing 966 genera and 1530 species that represent approximately 80% and 50% of the accumulated read abundance, respectively. Finally, we show that for well-studied functional guilds, such as nitrifiers or polyphosphate-accumulating organisms, the same genera are prevalent worldwide, with only a few abundant species in each genus

Systems genetics identifies Sestrin 3 as a regulator of a proconvulsant gene network in human epileptic hippocampus

Gene-regulatory network analysis is a powerful approach to elucidate the molecular processes and pathways underlying complex disease. Here we employ systems genetics approaches to characterize the genetic regulation of pathophysiological pathways in human temporal lobe epilepsy (TLE). Using surgically acquired hippocampi from 129 TLE patients, we identify a gene-regulatory network genetically associated with epilepsy that contains a specialized, highly expressed transcriptional module encoding proconvulsive cytokines and Toll-like receptor signalling genes. RNA sequencing analysis in a mouse model of TLE using 100 epileptic and 100 control hippocampi shows the proconvulsive module is preserved across-species, specific to the epileptic hippocampus and upregulated in chronic epilepsy. In the TLE patients, we map the trans-acting genetic control of this proconvulsive module to Sestrin 3 (SESN3), and demonstrate that SESN3 positively regulates the module in macrophages, microglia and neurons. Morpholino-mediated Sesn3 knockdown in zebrafish confirms the regulation of the transcriptional module, and attenuates chemically induced behavioural seizures in vivo