Performance analysis of binary and multiclass models using azure machine learning

Network data is expanding and that too at an alarming rate. Besides, the sophisticated attack tools used by hackers lead to capricious cyber threat landscape. Traditional models proposed in the field of network intrusion detection using machine learning algorithms emphasize more on improving attack detection rate and reducing false alarms but time efficiency is often overlooked. Therefore, in order to address this limitation, a modern solution has been presented using Machine Learning-as-a-Service platform. The proposed work analyses the performance of eight two-class and three multiclass algorithms using UNSW NB-15, a modern intrusion detection dataset. 82,332 testing samples were considered to evaluate the performance of algorithms. The proposed two class decision forest model exhibited 99.2% accuracy and took 6 seconds to learn 1,75,341 network instances. Multiclass classification task was also undertaken wherein attack types like generic, exploits, shellcode and worms were classified with a recall percentage of 99%, 94.49%, 91.79% and 90.9% respectively by the multiclass decision forest model that also leapfrogged others in terms of training and execution time

A predictive model for network intrusion detection using stacking approach

Due to the emerging technological advances, cyber-attacks continue to hamper information systems. The changing dimensionality of cyber threat landscape compel security experts to devise novel approaches to address the problem of network intrusion detection. Machine learning algorithms are extensively used to detect intrusions by dint of their remarkable predictive power. This work presents an ensemble approach for network intrusion detection using a concept called Stacking. As per the popular no free lunch theorem of machine learning, employing single classifier for a problem at hand may not be ideal to achieve generalization. Therefore, the proposed work on network intrusion detection emphasizes upon a combinative approach to improve performance. A robust processing paradigm called Graphlab Create, capable of upholding massive data has been used to implement the proposed methodology. Two benchmark datasets like UNSW NB-15 and UGR’ 16 datasets are considered to demonstrate the validity of predictions. Empirical investigation has illustrated that the performance of the proposed approach has been reasonably good. The contribution of the proposed approach lies in its finesse to generate fewer misclassifications pertaining to various attack vectors considered in the study

Maternal and fetal out come in meconium stained amniotic fluid in a tertiary centre

Background: This study was undertaken to determine the correlation of amniotic fluid stained with meconium (MSAF) with maternal and fetal outcome.Methods: This prospective observational study was carried out in the Department of obstetrics and gynecology, Yenepoya medical college, Mangalore over a period of 14 months between January 2013 to march 2014. A total of 1000 pregnant women who had completed more than 37weeks of gestation with singleton pregnancies & cephalic presentation were included in this study. MSAF on spontaneous or artificial rupture of membranes were monitored during labour with fetal heart rate abnormality, consistency of liquor, 1 minute and 5 minute Apgar score, LSCS, instrumental delivery, NICU admissions and neonatal complications as outcome variables.Results: Women were divided into two groups: 350 women with MSAF as cases, while 650 women with clear liquor were taken as controls. Among 350 cases with MSAF, 70 % were unbooked and 30 % were booked pts. About 75.7% of women were between 20-30 years of age-group. Primi gravidas constituted 51.4% in study group. Approximately 41.4% cases had gestational ages of 39 -39+6. Among MSAF 55.4% were thin stained & 44.5%were thickly stained. 45.7% showed fetal heart abnormalities on electronic monitoring & presence of fetal bradycardia was higher. Caesarean section rates were nearly triple in cases (45.7% vs 15.7%). Fetal out come in regard to Apgar score at birth, birth asphyxia, MAS, increased NICU admissions were more in cases. Incidence of Male to female was high (52.6% vs 47.3%).Conclusions: Presence of MSAF is worrisome for both the obstetrician and pediatricians view as it increases surgical intervention, birth asphyxia, MAS & NICU admissions

Does pamidronate enhance the osteogenesis in mesenchymal stem cells derived from fibrous hamartoma in congenital pseudarthrosis of the tibia?

AbstractNeurofibromatosis type 1 (NF1) is a commonly occurring genetic disorder in children. Mutation in the NF1 gene has its implication in poor osteoblastic capabilities. We hypothesised that pamidronate will enhance the osteoblastic potential of the mesenchymal stem cells (MSCs) derived from lipofibromatosis tissue of children with congenital pseudarthrosis tibia (CPT) associated with NF1. In this study, bone marrow MSCs (BM MSCs) and CPT MSCs were obtained from three patients undergoing salvage surgeries/bone grafting (healthy controls) and those undergoing excision of the hamartoma and corrective surgeries respectively. The effects of pamidronate (0, 10nM, 100nM and 1μM) on cell proliferation, toxicity and differentiation potential were assessed and the outcome was measured by staining and gene expression. Our outcome showed that CPT MSCs had more proliferation rate as compared to BM MSCs. All 3 doses of pamidronate did not cause any toxicity to the cells in both the groups. The CPT MSCs showed less differentiation with pamidronate compared to the healthy control MSCs. This was quantitated by staining and gene expression analysis. Therefore, supplementation with pamidronate alone will not aid in bone formation in patients diagnosed with CPT. An additional stimulus is required to enhance bone formation

RESEARCH ON IIOT SECURITY: NOVEL MACHINE LEARNING-BASED INTRUSION DETECTION USING TCP/IP PACKETS

The Industrial Internet of Things (IIoT) explosive expansion has raised questions regarding the safety of industrial systems. Networks like these are crucially protected from a variety of cyber threats by intrusion detection systems (IDSs). In order to detect intrusions in the IIoT environment utilizing TCP/IP packets, this work introduces a novel Hybrid Deep Convolutional Autoencoder and Splinted Decision Tree (HDCA-SDT) technique. High-level features are extracted from the unprocessed TCP/IP packet data using the DCA. The retrieved features are then classified using the SDT algorithm into various intrusion categories. In order to enable quicker decision-making yet preserve accurate results, the SDT technique effectively divides the feature space. The NSL-KDD dataset is used to train and assess the model. The efficiency of the suggested hybrid strategy is shown by experimental findings. Comparing the proposed hybrid approach to conventional intrusion detection methods, it acquired higher detection accuracy. The model also demonstrates robustness to fluctuations in traffic on the network and possesses the ability to identify known and unidentified intrusions with high recall rates

Evaluation of Tensile Bond Strength of Zinc Containing and Zinc Free Denture Adhesives on Different Denture Base Resin Materials: An in Vitro Study

Background and aim: Denture adhesives augment the retention and stability of the complete denture. The included studies have not directly compared tensile bond strength between zinc and zinc-free denture adhesives. This study compared the tensile bond strength of zinc-containing and zinc-free denture adhesives on different denture base resin materials at various intervals.Material and methods: Four groups of denture base resin materials (Acralyn H, Lucitone199- DB1, SR Ivocap-DB2, Polytray-DB3) were fabricated using different polymerization techniques. Each group had ten specimens. The control group consisted of resin cylinders coated with artificial saliva, while the test groups had denture adhesive applied between the test and control cylinders. Tensile bond strength was measured using a universal testing machine.Results: The tensile bond strength values of Fixodent with DBI &DB3 and DB2 &DB3 at 5 min (P < 0.01), 3 hours (P < 0.01), and 6 hours (P < 0.061 and P < 0.020) alongside with DB1 & DB2, DBI & DB3, and DB2 & DB3 at 12 hours (P < 0.01) were found to be statistically significant. The tensile bond strengths variations of Fittydent with DB1 & DB3 and DB2 & DB3 at 3 hours (P =0.013, P =0.012) and 6 hours (P < 0.01), and DB2 & DB3 at 12 hours (P=0.015), was statistically significant at 0.05 level.Conclusions: The zinc-containing and zinc-free denture adhesives exhibited a significant increase in tensile bond strength compared to the control group (artificial saliva) at all time intervals

A Stacking Ensemble for Network Intrusion Detection Using Heterogeneous Datasets

The problem of network intrusion detection poses innumerable challenges to the research community, industry, and commercial sectors. Moreover, the persistent attacks occurring on the cyber-threat landscape compel researchers to devise robust approaches in order to address the recurring problem. Given the presence of massive network traffic, conventional machine learning algorithms when applied in the field of network intrusion detection are quite ineffective. Instead, a hybrid multimodel solution when sought improves performance thereby producing reliable predictions. Therefore, this article presents an ensemble model using metaclassification approach enabled by stacked generalization. Two contemporary as well as heterogeneous datasets, namely, UNSW NB-15, a packet-based dataset, and UGR’16, a flow-based dataset, that were captured in emulated as well as real network traffic environment, respectively, were used for experimentation. Empirical results indicate that the proposed stacking ensemble is capable of generating superior predictions with respect to a real-time dataset (97% accuracy) than an emulated one (94% accuracy)