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Low-speed internet can negatively impact incident response by causing delayed detection, ineffective response, poor collaboration, inaccurate analysis, and increased risk. Slow internet speeds can delay the receipt and analysis of data, making it difficult for security teams to access the relevant information and take action, leading to a fragmented and inadequate response. All of these factors can increase the risk of data breaches and other security incidents and their impact on IoT-enabled communication. This study combines virtual network function (VNF) technology with software -defined networking (SDN) called virtual network function software-defined networking (VNFSDN). The adoption of the VNFSDN approach has the potential to enhance network security and efficiency while reducing the risk of cyberattacks. This approach supports IoT devices that can analyze large volumes of data in real time. The proposed VNFSDN can dynamically adapt to changing security requirements and network conditions for IoT devices. VNFSDN uses threat filtration and threat-capturing and decision-driven algorithms to minimize cyber risks for IoT devices and enhance network performance. Additionally, the integrity of IoT devices is safeguarded by addressing the three risk categories of data manipulation, insertion, and deletion. Furthermore, the prioritized delegated proof of stake (PDPoS) consensus variant is integrated with VNFSDN to combat attacks. This variant addresses the scalability issue of blockchain technology by providing a safe and adaptable environment for IoT devices that can quickly be scaled up and down to pull together the changing demands of the organization, allowing IoT devices to efficiently utilize resources. The PDPoS variant provides flexibility to IoT devices to proactively respond to potential security threats, preventing or mitigating the impact of cyberattacks. The proposed VNFSDN dynamically adapts to the changing security requirements and network conditions, improving network resiliency and enabling proactive threat detection. Finally, we compare the proposed VNFSDN to existing state-of-the-art approaches. According to the results, the proposed VNFSDN has a 0.08 ms minimum response time, a 2% packet loss rate, 99.5% network availability, a 99.36% threat detection rate, and a 99.77% detection accuracy with 1% malicious nodes. [ABSTRACT FROM AUTHOR]

Deep learning algorithms have a wide range of applications, including cancer diagnosis, face and speech recognition, object recognition, etc. It is critical to protect these models since any changes to them can result in serious losses in a variety of ways. This article proposes the consortium blockchain-enabled conventional neural network (CBCNN), a four-layered paradigm for detecting malicious vehicles. Layer-1 is a convolutional neural network-enabled Internet-of-Things (IoT) model for the vehicle; Layer-2 is a spatial pyramid polling layer for the vehicle; Layer-3 is a fully connected layer for the vehicle; and Layer-4 is a consortium blockchain for the vehicle. The first three layers accurately identify the vehicles, while the final layer prevents any malicious attempts. The primary goal of the four-layered paradigm is to successfully identify malicious vehicles and mitigate the potential risks they pose using multi-label classification. Furthermore, the proposed CBCNN approach is employed to ensure tamper-proof protection against a parameter manipulation attack. The consortium blockchain employs a proof-of-luck mechanism, allowing vehicles to save energy while delivering accurate information about the vehicle's nature to the "vehicle management system." C++ coding is employed to implement the approach, and the ns-3.34 platform is used for simulation. The ns3-ai module is specifically utilized to detect anomalies in the Internet of Vehicles (IoVs). Finally, a comparative analysis is conducted between the proposed CBCNN approach and state-of-the-art methods. The results confirm that the proposed CBCNN approach outperforms competing methods in terms of malicious label detection, average accuracy, loss ratio, and cost reduction. [ABSTRACT FROM AUTHOR]

Finding reliable and efficient routes is a persistent problem in megacities. To address this problem, several algorithms have been proposed. However, there are still areas of research that require attention. Many traffic-related problems can be resolved with the help of smart cities that incorporate the Internet of Vehicles (IoV). On the other hand, due to rapid increases in the population and automobiles, traffic congestion has become a serious concern. This paper presents a heterogeneous algorithm called ant-colony optimization with pheromone termite (ACO-PT), which combines two state-of-the-art algorithms, pheromone termite (PT) and ant-colony optimization (ACO), to address efficient routing to improve energy efficiency, increase throughput, and shorten end-to-end latency. The ACO-PT algorithm seeks to provide an effective shortest path from a source to a destination for drivers in urban areas. Vehicle congestion is a severe issue in urban areas. To address this issue, a congestion-avoidance module is added to handle potential overcrowding. Automatic vehicle detection has also been a challenging issue in vehicle management. To address this issue, an automatic-vehicle-detection (AVD) module is employed with ACO-PT. The effectiveness of the proposed ACO-PT algorithm is demonstrated experimentally using network simulator-3 (NS-3) and Simulation of Urban Mobility (SUMO). Our proposed algorithm is compared with three cutting-edge algorithms. The results demonstrate that the proposed ACO-PT algorithm is superior to earlier algorithms in terms of energy usage, end-to-end delay, and throughput. [ABSTRACT FROM AUTHOR]

INTRODUCTION: It is very important to find a a non invasive, less time consuming and relatively easy modality, non operator dependent and available even remote areas for the common bile duct stones (choledocolithiasis) in symptomatic patients. Instant diagnosis will lead to prompt decision making for treatment strategies that will ultimately decrease the morbidity. OBJECTIVE: To determine the diagnostic accuracy of ultrasonography in detection of Choledocholithiasis in suspected patients taking intraoperative findings as gold standard. STUDY DESIGN: Descriptive Cross sectional Study SETTING: Department of Radiology, Jinnah postgraduate Medical Center, Karachi DURATION: Six months study from Feb 2020 onwards. SUBJECTS AND METHODS: All patients with age 30-60 years of either gender having clinically suspected with duration of symptoms of more than or equal to 7 days were enrolled. The ultrasound was performed and presence of CBD diameter of > 7 mm on US was taken as CBD stones. Presence of CBD stones intra operatively was noted. RESULTS: Mean age of the patients was 51.29 ±7.03 years. There were 75 (62%) females and 46 (38%) males. Overall diagnostic accuracy of ultrasonography was found to be 89.25%. Sensitivity was found to be 80.48%, specificity 93.75%, PPV 86.84% and NPV 90.36%. CONCLUSION: The diagnostic accuracy of ultrasonography was found satisfactory in detection of Choledocholithiasis in suspected patients taking intraoperative findings as gold standard. [ABSTRACT FROM AUTHOR]

The rapid development of the emerging Internet of things (IoT) regime and of smart applications has given rise to many new wireless security vulnerabilities in consumer electronic environment networks, which compromises the integrity of the whole environment. Attacks often encompass the abuse of vulnerable wireless IoT consumer electronics devices, compromise information security, and leak sensitive and private data. This paper proposes a decentralized blockchain-enabled framework network infrastructure to improve wireless security and mitigate new wireless attacks on connected consumer electronics as a proof of concept. The system model of the proposed framework was verified mathematically and was then implemented and tested in case studies. The experimental results show that the proposed decentralized blockchain technology-featured air-cracking tool (BTFAT) provides a robust detection of vulnerabilities in the security of wireless-based IoT consumer electronics, i.e., 97% in terms of the effectiveness of packet chapters and 85% in terms of the maximum capture packet reliability, compared with the state of the art. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are considered as the appealing research area. WSNs require highly robust medium access control (MAC) protocol to enhance the performance in several application areas, such as intrusion detection, target detection, industrial automation, environmental monitoring, surveillance system, medical diagnosing system, and tactical system. On the other hand, there are several factors that affect the performance of these applications particularly selection of weak MAC protocol. In this paper, we provide performance impairing drivers for MAC protocols, which affect the efficiency and robustness of MAC protocols in WSN applications. We classify MAC protocols into six categories, as compared with previous MAC surveys that only focused on classifying the MAC protocols into two, three, or four major categories. In addition, we show the link of each category with another based on their existing features. Furthermore, this survey provides a detailed nomenclature in which protocols are categorized based on synchronous and asynchronous communication. This survey also discusses the possible threats and some existing solutions at the MAC layer from 2000 to 2014. Finally, we identify the future research challenges and raise directions for controlling these challenges. [ABSTRACT FROM PUBLISHER]

Robust paradigms are a necessity, particularly for emerging wireless sensor network (WSN) applications. The lack of robust and efficient paradigms causes a reduction in the provision of quality of service (QoS) and additional energy consumption. In this paper, we introduce modular energy-efficient and robust paradigms that involve two archetypes: (1) the operational medium access control (O-MAC) hybrid protocol and (2) the pheromone termite (PT) model. The O-MAC protocol controls overhearing and congestion and increases the throughput, reduces the latency and extends the network lifetime. O-MAC uses an optimized data frame format that reduces the channel access time and provides faster data delivery over the medium. Furthermore, O-MAC uses a novel randomization function that avoids channel collisions. The PT model provides robust routing for single and multiple links and includes two new significant features: (1) determining the packet generation rate to avoid congestion and (2) pheromone sensitivity to determine the link capacity prior to sending the packets on each link. The state-of-the-art research in this work is based on improving both the QoS and energy efficiency. To determine the strength of O-MAC with the PT model; we have generated and simulated a disaster recovery scenario using a network simulator (ns-3.10) that monitors the activities of disaster recovery staff; hospital staff and disaster victims brought into the hospital. Moreover; the proposed paradigm can be used for general purpose applications. Finally; the QoS metrics of the O-MAC and PT paradigms are evaluated and compared with other known hybrid protocols involving the MAC and routing features. The simulation results indicate that O-MAC with PT produced better outcomes. [ABSTRACT FROM AUTHOR]

This paper introduces the design, implementation, and performance analysis of the scalable and mobility-aware hybrid protocol named boarder node medium access control (BN-MAC) for wireless sensor networks (WSNs), which leverages the characteristics of scheduled and contention-based MAC protocols. Like contention-based MAC protocols, BN-MAC achieves high channel utilization, network adaptability under heavy traffic and mobility, and low latency and overhead. Like schedule-based MAC protocols, BN-MAC reduces idle listening time, emissions, and collision handling at low cost at one-hop neighbor nodes and achieves high channel utilization under heavy network loads. BN-MAC is particularly designed for region-wise WSNs. Each region is controlled by a boarder node (BN), which is of paramount importance. The BN coordinates with the remaining nodes within and beyond the region. Unlike other hybrid MAC protocols, BN-MAC incorporates three promising models that further reduce the energy consumption, idle listening time, overhearing, and congestion to improve the throughput and reduce the latency. One of the models used with BN-MAC is automatic active and sleep (AAS), which reduces the ideal listening time. When nodes finish their monitoring process, AAS lets them automatically go into the sleep state to avoid the idle listening state. Another model used in BN-MAC is the intelligent decision-making (IDM) model, which helps the nodes sense the nature of the environment. Based on the nature of the environment, the nodes decide whether to use the active or passive mode. This decision power of the nodes further reduces energy consumption because the nodes turn off the radio of the transceiver in the passive mode. The third model is the least-distance smart neighboring search (LDSNS), which determines the shortest efficient path to the one-hop neighbor and also provides cross-layering support to handle the mobility of the nodes. The BN-MAC also incorporates a semi-synchronous feature with a low duty cycle, which is advantageous for reducing the latency and energy consumption for several WSN application areas to improve the throughput. BN-MAC uses a unique window slot size to enhance the contention resolution issue for improved throughput. BN-MAC also prefers to communicate within a one-hop destination using Anycast, which maintains load balancing to maintain network reliability. BN-MAC is introduced with the goal of supporting four major application areas: monitoring and behavioral areas, controlling natural disasters, human-centric applications, and tracking mobility and static home automation devices from remote places. These application areas require a congestion-free mobility-supported MAC protocol to guarantee reliable data delivery. BN-MAC was evaluated using network simulator-2 (ns2) and compared with other hybrid MAC protocols, such as Zebra medium access control (Z-MAC), advertisement-based MAC (A-MAC), Speck-MAC, adaptive duty cycle SMAC (ADC-SMAC), and low-power real-time medium access control (LPR-MAC). The simulation results indicate that BN-MAC is a robust and energy-efficient protocol that outperforms other hybrid MAC protocols in the context of quality of service (QoS) parameters, such as energy consumption, latency, throughput, channel access time, successful delivery rate, coverage efficiency, and average duty cycle. [ABSTRACT FROM AUTHOR]

Remote sensing technologies have been widely used in the contexts of land cover and land use. The image classification algorithms used in remote sensing are of paramount importance since the reliability of the result from remote sensing depends heavily on the classification accuracy. Parametric classifiers based on traditional statistics have successfully been used in remote sensing classification, but the accuracy is greatly impacted and rather constrained by the statistical distribution of the sensing data. To eliminate those constraints, new variants of support vector machine (SVM) are introduced. In this paper, we propose and implement land use classification based on improved SVM-enabled radial basis function (RBF) and SVM-Linear for image sensing. The proposed variants are applied for the cross-validation to determine how the optimization of parameters can affect the accuracy. The accuracy assessment includes both training and test sets, addressing the problems of overfitting and underfitting. Furthermore, it is not trivial to determine the generalization problem merely based on a training dataset. Thus, the improved SVM-RBF and SVM-Linear also demonstrate the outstanding generalization performance. The proposed SVM-RBF and SVM-Linear variants have been compared with the traditional algorithms (Maximum Likelihood Classifier (MLC) and Minimum Distance Classifier (MDC)), which are highly compatible with remote sensing images. Furthermore, the MLC and MDC are mathematically modeled and characterized with new features. Also, we compared the proposed improved SVM-RBF and SVM-Linear with the current state-of-the-art algorithms. Based on the results, it is confirmed that proposed variants have higher overall accuracy, reliability, and fault-tolerance than traditional as well as latest state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Fractional dual solutions to the Maxwell equations for a planar dielectric-chiral interface are determined using the fractional curl operator. Both ⊥ and ∥ modes are treated for this purpose. Behavior of absolute value of the reflection coefficients, their phases and GHS are depicted and analyzed taking specific values of the fractional parameter describing order of the fractional curl operator. Derived results are verified by comparing with published work and observing the canonical cases for the fractional duality. [ABSTRACT FROM AUTHOR]

Significant research has been conducted for maintaining a high standard of communication and good coverage in wireless sensor networks (WSNs), but extra power consumption and mobility issues are not yet fully resolved. This paper introduces a memory-less location mobility-aware Lattice Mobility Model (LMM) for WSNs. LMM is capable of concurrently determining the node and sink mobility. LMM has a lower pause time, fewer control packets, and less node dependency (e.g., the energy consumed by each node in each cycle that is independent of the data traffic). LMM accurately determines a node's moving location, the distance from its previous location to its current location, and the distance from its existing location to its destination. Many existing mobility models only provide a model how nodes move (e.g., to mimic pedestrian behavior), but do not actually control the next position based on properties of the underlying network topology. To determine the strength of LMM, OMNet++ was used to generate the realistic scenario to safeguard the affected area. The operation in affected area comprises searching for, detecting, and saving survivors. Currently, this process involves a time-consuming, manual search of the disaster area. This contribution aims to identify an energy efficient mobility model for a walking pattern in this particular scenario. LMM outperforms other mobility models, including the geographic-based circular mobility model (CMM), the random waypoint mobility model (RWMM) and the wind mobility model (WMM), The simulation results also demonstrate that the LMM requires the least time to change the location, has a lower drop rate, and has more residual energy savings than do the WMM, RWMM, and CMM. [ABSTRACT FROM AUTHOR]

Worldwide, tuberculosis (TB) is one of the top 10 causes of death, and the leading cause from a single infectious agent. Pakistan has an overwhelming burden of TB and it is a major health hazard for the majority of the rural population. The lung continues to be the most common site of involvement and even after completion of treatment residual changes remain which may affect quality of life.Complications of TB after treatment completion can often be misinterpreted for other active diseases so it is important to recognize and understand the radiologic manifestations of the thoracic sequelae. Post TB sequelae can be categorized into parenchymal, airway disease, pleural/chest wall, vascular and mediastinal. These residual changes can be minor however, some can be debilitating and even fatal.The purpose of this pictorial review is to show the spectrum of residual changes seen on chest radiography and/or computed tomography that persist after treatment completion and bacteriological cure. [ABSTRACT FROM AUTHOR]

Worldwide, tuberculosis (TB) is one of the top 10 causes of death, and the leading cause from a single infectious agent. Pakistan has an overwhelming burden of TB and it is a major health hazard for the majority of the rural population. The lung continues to be the most common site of involvement and even after completion of treatment residual changes remain which may affect quality of life.Complications of TB after treatment completion can often be misinterpreted for other active diseases so it is important to recognize and understand the radiologic manifestations of the thoracic sequelae. Post TB sequelae can be categorized into parenchymal, airway disease, pleural/chest wall, vascular and mediastinal. These residual changes can be minor however, some can be debilitating and even fatal.The purpose of this pictorial review is to show the spectrum of residual changes seen on chest radiography and/or computed tomography that persist after treatment completion and bacteriological cure. [ABSTRACT FROM AUTHOR]

In today's world, the use of computer networks is everywhere, and to access the home network we use the Internet. IoT networks are the new range of these networks in which we try to connect different home appliances and try to give commands from a remote place. Access to any device over an insecure network invites various types of attacks. User authentication can be performed using some password or biometric technique. However, when it comes to authenticating a device, it becomes challenging to maintain data security over a secure network such as the Internet. Many encryptions and decryption algorithms assert confidentiality, and hash code or message authentication code MAC is used for authentication. Traditional cryptographic security methods are expensive in terms of computational resources such as memory, processing capacity, and power consumption. They are incompatible with the Internet of Things devices that have limited resources. Although automatic Device-to-Device communication enables new potential applications, the limited resources of the networks' machines and devices impose various constraints. This paper proposes a home device authentication scheme when these are accessed from a remote place. An authentication device is used for the home network and controller device to control home appliances. Our scheme can prevent various attacks such as replay attacks, server spoofing, and man-in-the-middle attack. The proposed scheme maintains the confidentiality and authenticity of the user and devices in the network. At the same time, we check the system in a simulated environment, and the results show that the network's performance does not degrade much in terms of delay, throughput, and energy consumed. [ABSTRACT FROM AUTHOR]

Cloud-native computing is getting more and more popular in recent years where containerized microservices architectural designs play a central role in building a distributed systems and services. On one hand, they bring convenience and simplicity to build massively scalable distributed cloud-native applications and enable continuous development and delivery for their services. On the other hand, they widen the surface of malicious intrusions, which, in turn, without proper defense mechanisms, lessens their benefits to a certain degree. Among the biggest threats of malicious intrusions are those that belong to the Distributed Denial of Service (DDoS) family. Such type of attacks are challenging because DDoS attacks are elevated hard-to-absorbed threats and have a high degree of variability in types, design, and complexity. In this work, resilient backpropagation neural network was used to build an intelligent network intrusion detection model against the most modernistic DDoS attacks in the cloud-native computing environment. We evaluated our proposed model using the benchmarking Canadian Institute for Cybersecurity evaluation CICDDoS 2019 dataset. Our proposed detection model has achieved high reflective DDoS attack detection. Therefore, it is appropriate to defend against reflective DDoS attacks in containerized cloud-native platforms. Experimental results indicate that the DDoS attack detection accuracy of the proposed resilient neural network model is as high as 97.07% which outperforms most of the well-known learning models mentioned in the most related work. Moreover, the proposed model has achieved a competitive run time performance that highly meets the delay requirements of containerized cloud computing. [ABSTRACT FROM AUTHOR]

As a results of the large scale development of the Internet of Things (IoT), cloud computing capabilities including networking, data storage, management, and analytics are brought very close to the edge of networks forming Fog computing and enhancing transferring and processing of tremendous amount of data. As the Internet becomes more deeply integrated into our business operations through IoT platform, the desire for reliable and efficient connections increases as well. Fog and Cloud security is a topical issue associated with every data storage, managing or processing paradigm. Attacks once occurred, have ineradicable and disastrous effects on the development of IoT, Fog, Cloud computing. Therefore, many security systems/models have been proposed and/or implemented for the sake of Fog security. Intrusion detection systems are one of the premier choices especially ones that designed using artificial intelligence. In our paper, we presented an artificially full-automated intrusion detection system for Fog security against cyber-attacks. The proposed model uses multi-layered of recurrent neural networks designed to be implemented for Fog computing security that is very close to the end-users and IoT devices. We demonstrated our proposed model using a balanced version of the challenging dataset: NSL-KDD. The performance of our model was measured using a variety of typical metrics, and we add two additional metrics: Mathew correlation and Cohen's Kappa coefficients for deeper insight. where the experimental results and simulations proved the stability and robustness of the proposed model in terms of a variety of performance metrics. [ABSTRACT FROM AUTHOR]