Adaptive and Lightweight Abnormal Node Detection via Biological Immune Game in Mobile Multimedia Networks

Considering the contradiction between limited node resources and high detection costs in mobile multimedia networks, an adaptive and lightweight abnormal node detection algorithm based on artificial immunity and game theory is proposed in order to balance the trade-off between network security and detection overhead. The algorithm can adapt to the highly dynamic mobile multimedia networking environment with a large number of heterogeneous nodes and multi-source big data. Specifically, the heterogeneous problem of nodes is solved based on the non-specificity of an immune algorithm. A niche strategy is used to identify dangerous areas, and antibody division generates an antibody library that can be updated online, so as to realize the dynamic detection of the abnormal behavior of nodes. Moreover, the priority of node recovery for abnormal nodes is decided through a game between nodes without causing excessive resource consumption for security detection. The results of comparative experiments show that the proposed algorithm has a relatively high detection rate and a low false-positive rate, can effectively reduce consumption time, and has good level of adaptability under the condition of dynamic nodes.

Detection of Attacks in Pervasive Computing Using Gated Recurrent Unit Based on Bidirectional Weighted Feature Averaging

In the era of information technology, the new types of cyber-attacks affect the performance of the network, which is very risky and cannot be restored quickly. In pervasive computing, there are more chances for such types of attacks since the personal data of the user is closely connected to the social environment. The research is performed using SNMP-MIB dataset, and feature selection are made by using the Enhanced Salp Swarm Optimization to select the optimal features to identify the attacks by using wrapper techniques. Then, various types of attacks are appropriately distinguished with proposed classifier Gated Recurrent Unit Neural Network based on Bidirectional Weighted Feature Averaging for high detection rate and accuracy. The value of performance metrics obtained from the proposed method outperforms the existing methods in terms of 99.9% accuracy, 99.8% in precision and detection rate is 99% in classifying different types of attacks.

Antibody profiles of avian leukosis virus subgroups A/B and J In layer flocks suspected to have Marek’s disease in Nigeria

Previous reports indicate high seroprevalence of avian leukosis virus (ALV) p72 antigen in layer flocks suspected to have Marek’s disease (MD) in Kaduna and Plateau States. However, the specific subgroups responsible for ALV infection in layers in the States are still unknown, hence the need for this study. Therefore, the objective of this study was to determine the antibody profiles of ALV subgroups A/B and J in layer flocks suspected to have MD in Kaduna and Plateau States. Sera from 7 and 16 layer flocks suspected to have MD in Kaduna and Plateau States respectively, were screened for the presence of antibodies to ALV subgroups A/B and J using IDEXX enzyme linked immunosorbent assay (ELISA) kits. Out of the seven layer flocks screened in Kaduna State, antibodies to ALV subgroup A/B was detected in six of the flocks (85.7%), while antibodies to ALV subgroup J was detected in only one flock (14.3%). Antibodies to both ALV subgroups A/B and J were detected in one flock (14.3%), which suggests co-infection of the two ALV subgroups. Out of the 16 flocks screened in Plateau State, antibodies to ALV subgroup A/B were detected in 15 flocks (93.8%), while antibodies to ALV subgroup J were detected in six flocks (37.5%). Antibodies to both ALV subgroups A/B and J were detected in five flocks (31.3%). The high detection of antibodies to ALV A/B suggests that ALV infection in layers is mostly due to ALV subgroup A or B in the study areas.

Enhanced Network Intrusion Detection System

A reasonably good network intrusion detection system generally requires a high detection rate and a low false alarm rate in order to predict anomalies more accurately. Older datasets cannot capture the schema of a set of modern attacks; therefore, modelling based on these datasets lacked sufficient generalizability. This paper operates on the UNSW-NB15 Dataset, which is currently one of the best representatives of modern attacks and suggests various models. We discuss various models and conclude our discussion with the model that performs the best using various kinds of evaluation metrics. Alongside modelling, a comprehensive data analysis on the features of the dataset itself using our understanding of correlation, variance, and similar factors for a wider picture is done for better modelling. Furthermore, hypothetical ponderings are discussed for potential network intrusion detection systems, including suggestions on prospective modelling and dataset generation as well.

Quasi-Simultaneous Sensitive Detection of Two Gas Species by Cavity-Ringdown Spectroscopy with Two Lasers

We developed a cavity ringdown spectrometer by utilizing a step-scanning and dithering method for matching laser wavelengths to optical resonances of an optical cavity. Our approach is capable of working with two and more lasers for quasi-simultaneous measurements of multiple gas species. The developed system was tested with two lasers operating around 1654 nm and 1658 nm for spectral detections of 12CH4 and its isotope 13CH4 in air, respectively. The ringdown time of the empty cavity was about 340 µs. The achieved high detection sensitivity of a noise-equivalent absorption coefficient was 2.8 × 10−11 cm−1 Hz−1/2 or 1 × 10−11 cm−1 by averaging for 30 s. The uncertainty of the high precision determination of δ13CH4 in air is about 1.3‰. Such a system will be useful for future applications such as environmental monitoring.

Deep-Learning-Based Automatic Monitoring of Pigs’ Physico-Temporal Activities at Different Greenhouse Gas Concentrations

Pig behavior is an integral part of health and welfare management, as pigs usually reflect their inner emotions through behavior change. The livestock environment plays a key role in pigs’ health and wellbeing. A poor farm environment increases the toxic GHGs, which might deteriorate pigs’ health and welfare. In this study a computer-vision-based automatic monitoring and tracking model was proposed to detect pigs’ short-term physical activities in the compromised environment. The ventilators of the livestock barn were closed for an hour, three times in a day (07:00–08:00, 13:00–14:00, and 20:00–21:00) to create a compromised environment, which increases the GHGs level significantly. The corresponding pig activities were observed before, during, and after an hour of the treatment. Two widely used object detection models (YOLOv4 and Faster R-CNN) were trained and compared their performances in terms of pig localization and posture detection. The YOLOv4, which outperformed the Faster R-CNN model, was coupled with a Deep-SORT tracking algorithm to detect and track the pig activities. The results revealed that the pigs became more inactive with the increase in GHG concentration, reducing their standing and walking activities. Moreover, the pigs shortened their sternal-lying posture, increasing the lateral lying posture duration at higher GHG concentration. The high detection accuracy (mAP: 98.67%) and tracking accuracy (MOTA: 93.86% and MOTP: 82.41%) signify the models’ efficacy in the monitoring and tracking of pigs’ physical activities non-invasively.

Deep Learning-based Automatic Monitoring of Pigs Physico-temporal Activities at Different Greenhouse Gas Concentrations

Pig behavior is an integral part of health and welfare management, as pigs usually reflect their inner emotions through behavior change. The livestock environment plays a key role in pigs' health and wellbeing. A poor farm environment increases the toxic GHGs, which might deteriorate pigs' health and welfare. In this study a computer-vision-based automatic monitoring and tracking model was proposed to detect short-term pigs' physical activities in a compromised environment. The ventilators of the livestock barn were closed for an hour, three times in a day (07:00-08:00, 13:00-14:00, and 20:00-21:00) to create a compromised environment, which increases the GHGs level significantly. The corresponding pig activities were observed before, during, and after an hour of the treatment. Two widely used object detection models (YOLOv4 and Fast-er R-CNN) were trained and compared their performances in terms of pig localization and posture detection. The YOLOv4, which outperformed the Faster R-CNN model, coupled with a Deep-SORT tracking algorithm to detect and track the pig activities. The results showed that the pigs became more inactive with the increase in GHG concentration, reducing their standing and walking activities. Moreover, the pigs also shortened their sternal-lying posture increasing the lateral lying posture duration at higher GHG concentration. The high detection accuracy (mAP: 98.67%) and tracking accuracy (MOTA: 93.86% and MOTP: 82.41%) signify the models’ efficacy in monitoring and tracking pigs' physical activities non-invasively.

Extra criterial antiphospholipid antibodies in patients with antiphospholipid syndrome and systemic lupus erythematosus (preliminary data)

The role of antiphospholipid antibodies (aPL), which are not included in the classification criteria, in antiphospholipid syndrome (APS) andsystemic lupus erythematosus (SLE) is not well understood.Objective: to determine the frequency of detection of IgG antibodies to domain 1 of β2-glycoprotein 1 (IgG-aβ2GP1-D1), IgA antibodiesto cardiolipin (aCL) and IgA antibodies to β2-glycoprotein 1 (IgA-a β2GP1) in patients with primary APS and APS in combination withSLE.Patients and methods. The study included 63 patients in whom IgG/IgM-aCL and IgG/IgM-aβ2GP1 were detected by enzyme-linkedimmunosorbent assay (ELISA) and IgG/IgM/IgA-aCL, IgG/IgM/IgA-aβ2GP1 and IgG-aβ2GP1-D1 by chemiluminescence analysis (CLA).Results and discussion. The detection rate of IgG-aβ2GP1-D1 was 76%, IgA-aCL – 56%, IgA-aβ2GP1 – 48%. Isolated positivity for IgA-aCL,IgA-aβ2GP1, IgG-aβ2GP1-D1 was not observed. The presence of IgA-aCL, IgA-aβ2GP1, IgG-aβ2GP1-D1 was associated with high positivity forIgG/IgM-aCL and IgG/IgM-aβ2GP1. There was a statistically significant relationship between IgA-aCL/IgA-aβ2GP1 and the standard aPLprofile, as well as IgG-aβ2GP1-D1, IgG-aCL and IgG-aβ2GP1.Conclusion. A high detection rate of IgG-aβ2GP1-D1, IgA-aCL, IgA-aβ2GP1 was found in patients with APS. A statistically significant relationshipwas found between IgA-aCL/IgA-aβ2GP1 and the standard aPL profile, as well as IgG-aβ2GP1-D1 with IgG-aCL and IgG-aβ2GP1.

Detection of unilateral and bilateral cleft alveolus on panoramic radiographs using a deep learning system

The purpose of this study was to create an effective deep learning (DL) model for detecting cleft alveolus (CA), including bilateral CA (BCA), on panoramic radiographs by comparing the detection performance of a DL model based on unilateral CA (UCA) and normal data with a model developed by combining UCA, BCA and normal data. We created two models using DetectNet. Model A was created using only UCA and normal images, and Model B was created using UCA, BCA and normal images. The performance of Models A and B was evaluated with the same testing data, and compared with two human observers. The total detection sensitivities were 0.55, 0.85, and 0.86 for Model A, Model B, and human observers, respectively. The ratios of detected and undetected CAs were significantly different among these three evaluators (p < 0.001). Regarding the UCA group, no significant differences were found in the ratio of those between Models A and B (p = 0.248). However, in the BCA group, the ratios were significantly different between the models (p < 0.001). The DL model created with the data including the BCA (Model B) achieved high detection performance for the testing data of both the UCA and BCA.