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The tremendous growth of the Internet of Things (IoT) has gained a lot of attention in the global market. The massive deployment of IoT is also inherent in various security vulnerabilities, which become easy targets for hackers. IoT botnets are one type of critical malware that degrades the performance of the IoT network and is difficult to detect by end-users. Although there are several traditional IoT botnet mitigation techniques such as access control, data encryption, and secured device configuration, these traditional mitigation techniques are difficult to apply due to normal traffic behavior, similar packet transmission, and the repetitive nature of IoT network traffic. Motivated by botnet obfuscation, this article proposes an intelligent mitigation technique for IoT botnets, named IMTIBoT. Using this technique, we harnessed the stacking of ensemble classifiers to build an intelligent system. This stacking classifier technique was tested using an experimental testbed of IoT nodes and sensors. This system achieved an accuracy of 0.984, with low latency. [ABSTRACT FROM AUTHOR]

With a great percentage of farms in NewZealand as pastures, they aremainly important in contributing to the milk and meat industries. Pasture quality is highly affected by weeds.Weeds grow fast and invade pastures by seed pollination. They consume the nutrients,water, and otherminerals, and once they are bitter, cattle do not eat them. Therefore, dairy farmers have to allocate a significant portion of their budget and time to monitor and clean weeds. Unfortunately, most weed management tasks are manual with no consistent technology. Thus, the motivation behind this article was to design an object detection model for weed monitoring and control in pastures. The model was designed and tested on California thistle, a dominant and widespread weed on New Zealand pastures. Our study is one of the major model designs for identifying weeds in an inpasture environment, one of the most complicated environments for any object detection model. A synthetic methodology was used to create three types of datasets: plant-based, leaf-based, and mixed. The trainedmodel based on the leaf-based dataset is one of the major contributions of our work and has not been conducted by any other weed detection models. After models had been trained, tuning experimentation was undertaken to improve the model's performance. This involved studying the model's hyperparameters in various ranges and then recording their values at the optimum points. The improved model showed a 93% mAP accuracy in the detection of training images and over 95% accuracy for testing images. The experimentation showed that the leaf-based model was slightly better than other models. The model can automate highly any weed management system. The use of this model will save farmers time and money and help them reduce the errors of manual work. [ABSTRACT FROM AUTHOR]

Network Function Virtualization (NFV) is an enabling technology that brings together automated network service management and corresponding virtualized network functions that use an NFV Infrastructure (NFVI) framework. The Virtual Network Function Manager (VNFM) placement in a large-scale distributed NFV deployment is therefore a challenging task due to the potential negative impact on performance and operating expense cost. The VNFM assigns Virtual Network Functions (VNFs) and operates efficiently based on network demands with resilient performance through efficient placement techniques. The degradation in performance and a tremendous increase in capital expenditure and operating expenses indicated this chaotic problem. This article proposed a method for VNFM placement using information on the resources of each nodes' Element Manager (EM), which is an efficient method to assign VNFs to each node of element management systems. In addition, this paper proposed an Optimized Element Manager (OEM) method for looking at appropriate EMs for the placement of VNF through periodic information on available resources. It also overcomes challenges such as delays and variations in VNFs workload for edge computing and distributed cloud regions. The performance is measured based on computations performed on various optimization algorithms such as linear programming and tabu search algorithms. The advent of the new service provisioning model of BGP-EVPN for VXLAN is materialized by integrating VTS with OpenStack. The numerical analysis shows that the proposed OEM algorithm gives an optimal solution with an average gap of 8%. [ABSTRACT FROM AUTHOR]

Cervical cancer is one of the common cancers among women and it causes significant mortality in many developing countries. Diagnosis of cervical lesions is done using pap smear test or visual inspection using acetic acid (staining). Digital colposcopy, an inexpensive methodology, provides painless and efficient screening results. Therefore, automating cervical cancer screening using colposcopy images will be highly useful in saving many lives. Nowadays, many automation techniques using computer vision and machine learning in cervical screening gained attention, paving the way for diagnosing cervical cancer. However, most of the methods rely entirely on the annotation of cervical spotting and segmentation. This paper aims to introduce the Faster Small-Object Detection Neural Networks (FSOD-GAN) to address the cervical screening and diagnosis of cervical cancer and the type of cancer using digital colposcopy images. The proposed approach automatically detects the cervical spot using Faster Region-Based Convolutional Neural Network (FR-CNN) and performs the hierarchical multiclass classification of three types of cervical cancer lesions. Experimentation was done with colposcopy data collected from available open sources consisting of 1,993 patients with three cervical categories, and the proposed approach shows 99% accuracy in diagnosing the stages of cervical cancer. [ABSTRACT FROM AUTHOR]

Blockchain technology has become an active area of research and a technological option for many businesses and industrial communities. With its distributed, decentralized, and trustless nature, blockchain can provide businesses with new opportunities and benefits through increased efficiency, reduced costs, enhanced integrity and transparency, better security, and improved traceability. Although blockchain's largest applications have been in the finance and banking sector, we now see experiments and proposed applications in different fields. This paper provides an overview of blockchain technology; it brings together all the key design features, characteristics, and benefits of blockchain that make it a superior and unique technology, and it presents the popular consensus protocols and taxonomy of blockchain systems. Additionally, the paper surveys blockchain-based applications across multiple domains such as in finance, insurance, supply chain management, energy, advertising and media, real estate and healthcare. It aims at examining the industries' key issues, blockchain solutions and use cases. The paper highlights three broad limitations that blockchain technology presents: scalability, security, and regulation, and shows how these challenges could impact blockchain application and adoption. [ABSTRACT FROM AUTHOR]

Power-laws are ubiquitous in the Internet and its applications. This tutorial presents a review of power-laws with emphasis on observations from Internet measurements. First, we introduce power-laws and describe two commonly observed power-law distributions, the Pareto and Zipf distributions. Two frequently occurring terms associated with these distributions, specifically heavy tails and long tails, are also discussed. Second, the preferential attachment model, which is a widely used model for generating power-law graph structures, is reviewed. Subsequently, we present several examples of Internet workload properties that exhibit power-law behavior. Finally, we explore several implications of power-laws in computer networks. Using examples from past and present, we review how researchers have studied and exploited power-law properties. We observe that despite the challenges posed, power-laws have been effectively leveraged by researchers to improve the design and performance of Internet-based systems. [ABSTRACT FROM PUBLISHER]

There is a continuous struggle for control of resources at every organization that is connected to the Internet. The local organization wishes to use its resources to achieve strategic goals. Some external entities seek direct control of these resources, for purposes such as spamming or launching denial-of-service attacks. Other external entities seek indirect control of assets (e.g., users, finances), but provide services in exchange for them. Using a year-long trace from an edge network, we examine what various external organizations know about one organization. We compare the types of information exposed by or to external organizations using either active (reconnaissance) or passive (surveillance) techniques. We also explore the direct and indirect control external entities have on local IT resources. [ABSTRACT FROM AUTHOR]

Agile methods are a departure from plan-driven traditional approaches, where the focus is on generating early releases of working software using collaborative techniques, code refactoring, and on-site customer involvement. Research and surveys have shown that agile methodologies are an efficient way of producing software with significant advantages in production costs, time-to-market, complexity, and quality improvement over heavy-weight traditional methodologies. Even with such apparent advantages the information technology industry has not seen large-scale adoption of agile methods. In this survey paper, the major challenges in adopting agile practices by enterprises are addressed. Drawing information from the literature issues like framework for agile organizational change and adoption strategies are examined. Inputs from the industry suggest that most organizations are best suited in adopting a combination of traditional and agile methods. There is no agile methodology that can be universally applied and they all have to be tailored to integrate into existing processes. [ABSTRACT FROM AUTHOR]

Technological advancements have led to increased confidence in the design of large-scale wireless networks that comprise small energy constraint devices. Despite the boost in technological advancements, energy dissipation and fault tolerance are amongst the key deciding factors while designing and deploying wireless sensor networks. This paper proposes a Fault-tolerant Energy-efficient Hierarchical Clustering Algorithm (FEHCA) for wireless sensor networks (WSNs), which demonstrates energy-efficient clustering and fault-tolerant operation of cluster heads (CHs). It treats CHs as no special node but equally prone to faults as normal sensing nodes of the cluster. The proposed scheme addresses some of the limitations of prominent hierarchical clustering algorithms, such as the randomized election of the cluster heads after each round, which results in significant energy dissipation; non-consideration of the residual energy of the sensing nodes while selecting cluster heads, etc. It utilizes the capability of vector quantization to partition the deployed sensors into an optimal number of clusters and ensures that almost the entire area to be monitored is alive for most of the network's lifetime. This supports better decision-making compared to decisions made on the basis of limited area sensing data after a few rounds of communication. The scheme is implemented for both friendly as well as hostile deployments. The simulation results are encouraging and validate the proposed algorithm. [ABSTRACT FROM AUTHOR]

Fog computing is an emerging computing paradigm that has come into consideration for the deployment of Internet of Things (IoT) applications amongst researchers and technology industries over the last few years. Fog is highly distributed and consists of a wide number of autonomous end devices, which contribute to the processing. However, the variety of devices offered across different users are not audited. Hence, the security of Fog devices is a major concern that should come into consideration. Therefore, to provide the necessary security for Fog devices, there is a need to understand what the security concerns are with regards to Fog. All aspects of Fog security, which have not been covered by other literature works, need to be identified and aggregated. On the other hand, privacy preservation for user's data in Fog devices and application data processed in Fog devices is another concern. To provide the appropriate level of trust and privacy, there is a need to focus on authentication, threats and access control mechanisms as well as privacy protection techniques in Fog computing. In this paper, a survey along with a taxonomy is proposed, which presents an overview of existing security concerns in the context of the Fog computing paradigm. Moreover, the Blockchain-based solutions towards a secure Fog computing environment is presented and various research challenges and directions for future research are discussed. [ABSTRACT FROM AUTHOR]

In cloud storage systems, users must be able to shut down the application when not in use and restart it from the last consistent state when required. BlobSeer is a data storage application, specially designed for distributed systems, that was built as an alternative solution for the existing popular open-source storage system-Hadoop Distributed File System (HDFS). In a cloud model, all the components need to stop and restart from a consistent state when the user requires it. One of the limitations of BlobSeer DFS is the possibility of data loss when the system restarts. As such, it is important to provide a consistent start and stop state to BlobSeer components when used in a Cloud environment to prevent any data loss. In this paper, we investigate the possibility of BlobSeer providing a consistent state distributed data storage system with the integration of checkpointing restart functionality. To demonstrate the availability of a consistent state, we set up a cluster with multiple machines and deploy BlobSeer entities with checkpointing functionality on various machines. We consider uncoordinated checkpoint algorithms for their associated benefits over other alternatives while integrating the functionality to various BlobSeer components such as the Version Manager (VM) and the Data Provider. The experimental results show that with the integration of the checkpointing functionality, a consistent state can be ensured for a distributed storage system even when the system restarts, preventing any possible data loss after the system has encountered various system errors and failures. [ABSTRACT FROM AUTHOR]

Over the last decade, the field of bioinformatics has been increasing rapidly. Robust bioinformatics tools are going to play a vital role in future progress. Scientists working in the field of bioinformatics conduct a large number of researches to extract knowledge from the biological data available. Several bioinformatics issues have evolved as a result of the creation of massive amounts of unbalanced data. The classification of precursor microRNA (pre miRNA) from the imbalanced RNA genome data is one such problem. The examinations proved that pre miRNAs (precursor microRNAs) could serve as oncogene or tumor suppressors in various cancer types. This paper introduces a Hybrid Deep Neural Network framework (H-DNN) for the classification of pre miRNA in imbalanced data. The proposed H-DNN framework is an integration of Deep Artificial Neural Networks (Deep ANN) and Deep Decision Tree Classifiers. The Deep ANN in the proposed H-DNN helps to extract the meaningful features and the Deep Decision Tree Classifier helps to classify the pre miRNA accurately. Experimentation of H-DNN was done with genomes of animals, plants, humans, and Arabidopsis with an imbalance ratio up to 1:5000 and virus with a ratio of 1:400. Experimental results showed an accuracy of more than 99% in all the cases and the time complexity of the proposed H-DNN is also very less when compared with the other existing approaches., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 R., Jain, Kotecha, Pandya, Reddy, E., Varadarajan, Mahanti and V.)