Exploring the confluence of IoT and metaverse: Future opportunities and challenges

The Internet of Things (IoT) and the metaverse are two rapidly evolving technologies that have the potential to shape the future of our digital world. IoT refers to the network of physical devices, vehicles, buildings, and other objects that are connected to the internet and capable of collecting and sharing data. The metaverse, on the other hand, is a virtual world where users can interact with each other and digital objects in real time. In this research paper, we aim to explore the intersection of the IoT and metaverse and the opportunities and challenges that arise from their convergence. We will examine how IoT devices can be integrated into the metaverse to create new and immersive experiences for users. We will also analyse the potential use cases and applications of this technology in various industries such as healthcare, education, and entertainment. Additionally, we will discuss the privacy, security, and ethical concerns that arise from the use of IoT devices in the metaverse. A survey is conducted through a combination of a literature review and a case study analysis. This review will provide insights into the potential impact of IoT and metaverse on society and inform the development of future technologies in this field

Shaping the future of Ethereum: Exploring energy consumption in Proof-of-Work and Proof-of-Stake consensus

Ethereum (ETH) is a popular Layer-1 blockchain platform that has been used to create decentralized applications (dApps) and smart contracts. Ethereum 2.0, or Serenity, is a significant update to the network that intends to address numerous issues with scalability, security, and energy efficiency. The Proof-of-Stake (PoS) consensus method will replace the Proof-of-Work (PoW) mechanism, which is one of the major new features of Ethereum 2.0. Given that PoS doesn’t require miners to do intensive mathematical calculations in order to validate transactions, it has the potential to be more energy-efficient than PoW. Additionally, this Ethereum upgrade will also be more secure due to the introduction of a new mechanism called “Casper” that will ensure that validators are always in agreement on the state of the blockchain. The paper begins by discussing the current issues facing Ethereum, including the limitations of the Proof of Work (PoW) consensus mechanism and the need for more efficient and scalable solutions. In this study, we peered at the major changes introduced by Ethereum 2.0, such as the new consensus method (Proof-of-Stake) and the addition of shard chains (Ethereum 2.0), as well as the associated development timelines, benefits and the community criticism on this upgrade

Detection of Distributed Attacks in Hybrid & Public Cloud Networks

International audienceIn this paper early detection of distributed attacks are discussed that are launched from multiple sites of the hybrid & public cloud networks. A prototype of Cloud Distributed Intrusion Detection System (CDIDS) is discussed with some basic experiments. The summation of security alerts has been applied which helps to detect distributed attacks while keeping the false positive at the minimum. Using the summation of security alerts mechanism the attacks that have slow iteration rate are detected at an early stage. The objective of our work is to propose a Security Management System (SMS) that can detect malicious activities as early as possible and camouflaging of attacks under the conditions when other security management systems become unstable due to intense events of attacks

Blood image analysis to detect malaria using filtering image edges and classification

Malaria is a most dangerous mosquito borne disease and its infection spread through the infected mosquito. It especially affects the pregnant females and Children less than 5 years age. Malarial species commonly occur in five different shapes, Therefore, to avoid this crucial disease the contemporary researchers have proposed image analysis based solutions to mitigate this death causing disease. In this work, we propose diagnosis algorithm for malaria which is implemented for testing and evaluation in Matlab. We use Filtering and classification along with median filter and SVM classifier. Our proposed method identifies the infected cells from rest of blood images. The Median filtering smoothing technique is used to remove the noise. The feature vectors have been proposed to find out the abnormalities in blood cells. Feature vectors include (Form factor, measurement of roundness, shape, count total number of red cells and parasites). Primary aim of this research is to diagnose malaria by finding out infected cells. However, many techniques and algorithm have been implemented in this field using image processing but accuracy is not up to the point. Our proposed algorithm got more efficient results along with high accuracy as compared to NCC and Fuzzy classifier used by the researchers recently

Energy Performance Analysis of a Multi-Story Building Using Building Information Modeling (BIM)

There is an enormous rise in building construction to meet serious demands of population increase. Besides its benefits, certain negative impacts on climate change and environments are associated with the built environment due to substantial energy requirements during operational phase. The current work aims to assess the energy consumption pattern of a residential facility based upon solar path analysis using simulation technique. A multi-story conventional building has been developed in a virtual 3D parametric environment using building information modeling. The BIM model was converted into the energy model using cloud computing. The energy model, at the proposed current orientation, was analyzed using insight 360 and solar energy analysis performed accordingly. Based upon the solar path analysis, the study observed that, at the present trajectory of solar path, provision of solar panels arrangements on 106,221 ft2 Photovoltaic panel area can produce the energy of 2,163,417 kwh/year with a payback period of 0.8 years.Keyword: Building Information Modelling, Energy analysis, Energy Optimization, Architecture 2030 Challeng

Security analysis of network anomalies mitigation schemes in IoT networks

The Internet of Things (IoT) is on the rise and it is giving a new shape to several fields such as smart cities, smart homes, smart health, etc. as it facilitates the connection of physical objects to the internet. However, this advancement comes along with new challenges in terms of security of the devices in the IoT networks. Some of these challenges come as network anomalies. Hence, this has prompted the use of network anomaly mitigation schemes as an integral part of the defense mechanisms of IoT networks in order to protect the devices from malicious users. Thus, several schemes have been proposed to mitigate network anomalies. This paper covers a review of different network anomaly mitigation schemes in IoT networks. The schemes' objectives, operational procedures, and strengths are discussed. A comparison table of the reviewed schemes, as well as a taxonomy based on the detection methodology, is provided. In contrast to other surveys that presented qualitative evaluations, our survey provides both qualitative and quantitative evaluations. The UNSW-NB15 dataset was used to conduct a performance evaluation of some classification algorithms used for network anomaly mitigation schemes in IoT. Finally, challenges and open issues in the development of network anomaly mitigation schemes in IoT are discussed

A DDoS attack mitigation framework for IoT networks using fog computing

The advent of 5G which strives to connect more devices with high speed and low latencies has aided the growth IoT network. Despite the benefits of IoT, its applications in several facets of our lives such as smart health, smart homes, smart cities, etc. have raised several security concerns such as Distributed Denial of Service (DDoS) attacks. In this paper, we propose a DDoS mitigation framework for IoT using fog computing to ensure fast and accurate attack detection. The fog provides resources for effective deployment of the mitigation framework, this solves the deficits in resources of the resource-constrained IoT devices. The mitigation framework uses an anomaly-based intrusion detection method and a database. The database stores signatures of previously detected attacks while the anomaly-based detection scheme utilizes k-NN classification algorithm for detecting the DDoS attacks. By using a database containing the attack signatures, attacks can be detected faster when the same type of attack is executed again. The evaluations using a DDoS based dataset show that the k-NN classification algorithm proposed for our framework achieves a satisfactory accuracy in detecting DDoS attacks

A hybrid dual-mode trust management scheme for vehicular networks

Vehicular ad-hoc networks allow vehicles to exchange messages pertaining to safety and road efficiency. Building trust between nodes can, therefore, protect vehicular ad-hoc networks from malicious nodes and eliminate fake messages. Although there are several trust models already exist, many schemes suffer from varied limitations. For example, many schemes rely on information provided by other peers or central authorities, for example, roadside units and reputation management centers to ensure message reliability and build nodes’ reputation. Also, none of the proposed schemes operate in different environments, for example, urban and rural. To overcome these limitations, we propose a novel trust management scheme for self-organized vehicular ad-hoc networks. The scheme is based on a crediting technique and does not rely on other peers or central authorities which distinguishes it as an economical solution. Moreover, it is hybrid, in the sense it is data-based and entity-based which makes it capable of revoking malicious nodes and discarding fake messages. Furthermore, it operates in a dual-mode (urban and rural). The simulation has been performed utilizing Veins, an open-source framework along with OMNeT++, a network simulator, and SUMO, a traffic simulator. The scheme has been tested with two trust models (urban and rural). The simulation results prove the performance and security efficacy of the proposed scheme

TQ-Model: A New Evaluation Model for Knowledge-Based Authentication Schemes

Many user authentication schemes are developed to resolve security issues of traditional textual password scheme. However, only Android unlock scheme gets wide acceptance among users in the domain of smartphones. Although Android unlock scheme has many security issues, it is widely used due to usability advantages. Different models and frameworks are developed for evaluating the performance of user authentication schemes. However, most of the existing frameworks provide ambiguous process of evaluation, and their results do not reflect how much an authentication scheme is strong or weak with respect to traditional textual password scheme. In this research paper, an evaluation model called textual passwords-based quantification model (TQ-Model) is proposed for knowledge-based authentication schemes. In the TQ-Model, evaluation is done on the basis of different features, which are related to security, usability and memorability. An evaluator needs to assign a score to each of the feature based on some criteria defined in the model. From the evaluation result, the performance difference between a knowledge-based authentication scheme and textual password scheme can be measured. Furthermore, evaluation results of Android unlock scheme, picture gesture authentication scheme and Passface scheme are presented in the paper using the TQ-Model

An anomaly mitigation framework for IoT using fog computing

The advancement in IoT has prompted its application in areas such as smart homes, smart cities, etc., and this has aided its exponential growth. However, alongside this development, IoT networks are experiencing a rise in security challenges such as botnet attacks, which often appear as network anomalies. Similarly, providing security solutions has been challenging due to the low resources that characterize the devices in IoT networks. To overcome these challenges, the fog computing paradigm has provided an enabling environment that offers additional resources for deploying security solutions such as anomaly mitigation schemes. In this paper, we propose a hybrid anomaly mitigation framework for IoT using fog computing to ensure faster and accurate anomaly detection. The framework employs signature- and anomaly-based detection methodologies for its two modules, respectively. The signature-based module utilizes a database of attack sources (blacklisted IP addresses) to ensure faster detection when attacks are executed from the blacklisted IP address, while the anomaly-based module uses an extreme gradient boosting algorithm for accurate classification of network traffic flow into normal or abnormal. We evaluated the performance of both modules using an IoT-based dataset in terms response time for the signature-based module and accuracy in binary and multiclass classification for the anomaly-based module. The results show that the signature-based module achieves a fast attack detection of at least six times faster than the anomaly-based module in each number of instances evaluated. The anomaly-based module using the XGBoost classifier detects attacks with an accuracy of 99% and at least 97% for average recall, average precision, and average F1 score for binary and multiclass classification. Additionally, it recorded 0.05 in terms of false-positive rates