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

Investigation of computational intelligence techniques for intrusion detection in wireless sensor networks.

Wireless Sensor Networks (WSNs) have become a key technology for the IoT and despite obvious benefits, challenges still exist regarding security. As more devices are connected to the internet, new cyber attacks are emerging which join well-known attacks posing significant threats to the confidentiality, integrity and availability of data in WSNs. In this work, we investigated two computational intelligence techniques for WSN intrusion detection. A back propagation neural network was compared with a support vector machine classifier. Using the NSL-KDD dataset, detection rates achieved by the two techniques for six cyber attacks were recorded. The results showed that both techniques offer a high true positive rate and a low false positive rate, making both of them good options for intrusion detection. In addition, we further show the support vector machine classifiers suitability for anomaly detection, by demonstrating its ability to handle low sample sizes, while maintaining an acceptable FPR rate under the required threshold

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

Paragraph: A graph-based structural variant genotyper for short-read sequence data

Accurate detection and genotyping of structural variations (SVs) from short-read data is a long-standing area of development in genomics research and clinical sequencing pipelines. We introduce Paragraph, an accurate genotyper that models SVs using sequence graphs and SV annotations. We demonstrate the accuracy of Paragraph on whole-genome sequence data from three samples using long-read SV calls as the truth set, and then apply Paragraph at scale to a cohort of 100 short-read sequenced samples of diverse ancestry. Our analysis shows that Paragraph has better accuracy than other existing genotypers and can be applied to population-scale studies. © 2019 The Author(s)

Cultivation and characterisation of human peripheral cornea-derived endothelial cells [abstract]

To confirm that human corneal rims left over from DALK/DSEK/PK surgeries could be useful sources for ex vivo endothelial cell expansion. Human corneal rims remaining from DALK/DSEK/PK surgeries were utilized (1:1 sex ratio, age 63+20 years, endothelial cell density >2,500 cells/mm2). The time from death to use varied between 3 days and 1.5 months. Endothelial cells isolated using a two-step, peel-and-digest method, whereby the Descemet’s membrane and endothelial cells were peeled off under a dissecting microscope, followed by digestion in collagenase. The isolated cells were suspended in TrypLE prior to plating onto FNC-coated tissue culture plates. The cells were then cultured in Ham’s F12:M199 (1:1) media supplemented with, ascorbic acid, transferrin, sodium selenite and bFGF. Characterisation of the cultured cells was performed by RT-qPCR and immunofluorescence staining accordingly. The number of isolated endothelial cells was repeatedly low (< 20,000 cells). However, improved techniques allowed to reduce stromal cell contamination. It was observed that endothelial cell proliferation was improved when the culture surface area was reduced. Furthermore, typical endothelial cobble stone morphology was observed when the cell density was high. Cell morphology and growth showed notable difference related to donor age and preservation time. ZO-1, Na/K-ATPase and PITX2 were used to confirm the endothelial phenotype. Preserved human corneal rims can be utilized for ex vivo expansion of corneal endothelial cells but further optimization is needed

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

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

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