Pervasive growth of Internet of Things (IoT) is visible across the globe. The 2016 Dyn cyberattack exposed the critical fault-lines among smart networks. Security of IoT has become a critical concern. The danger exposed by infested Internet-connected Things not only affects the security of IoT but also threatens the complete Internet eco-system which can possibly exploit the vulnerable Things (smart devices) deployed as botnets. Mirai malware compromised the video surveillance devices and paralyzed Internet via distributed denial of service attacks. In the recent past, security attack vectors have evolved bothways, in terms of complexity and diversity. Hence, to identify and prevent or detect novel attacks, it is important to analyze techniques in IoT context. This survey classifies the IoT security threats and challenges for IoT networks by evaluating existing defense techniques. Our main focus is on network intrusion detection systems (NIDSs); hence, this paper reviews existing NIDS implementation tools and datasets as well as free and open-source network sniffing software. Then, it surveys, analyzes, and compares state-of-the-art NIDS proposals in the IoT context in terms of architecture, detection methodologies, validation strategies, treated threats, and algorithm deployments. The review deals with both traditional and machine learning (ML) NIDS techniques and discusses future directions. In this survey, our focus is on IoT NIDS deployed via ML since learning algorithms have a good success rate in security and privacy. The survey provides a comprehensive review of NIDSs deploying different aspects of learning techniques for IoT, unlike other top surveys targeting the traditional systems. We believe that, this paper will be useful for academia and industry research, first, to identify IoT threats and challenges, second, to implement their own NIDS and finally to propose new smart techniques in IoT context considering IoT limitations. Moreover, the survey will enable security individuals differentiate IoT NIDS from traditional ones.

Network Intrusion Detection for IoT Security Based on Learning Techniques

Industry 5.0: A survey on enabling technologies and potential applications

Emerging applications such as Internet of Everything, Holographic Telepresence, collaborative robots, and space and deep-sea tourism are already highlighting the limitations of existing fifth-generation (5G) mobile networks. These limitations are in terms of data-rate, latency, reliability, availability, processing, connection density and global coverage, spanning over ground, underwater and space. The sixth-generation (6G) of mobile networks are expected to burgeon in the coming decade to address these limitations. The development of 6G vision, applications, technologies and standards has already become a popular research theme in academia and the industry. In this paper, we provide a comprehensive survey of the current developments towards 6G. We highlight the societal and technological trends that initiate the drive towards 6G. Emerging applications to realize the demands raised by 6G driving trends are discussed subsequently. We also elaborate the requirements that are necessary to realize the 6G applications. Then we present the key enabling technologies in detail. We also outline current research projects and activities including standardization efforts towards the development of 6G. Finally, we summarize lessons learned from state-of-the-art research and discuss technical challenges that would shed a new light on future research directions towards 6G.

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Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research

Distributed denial of service (DDoS) resilience in cloud

Machine learning technology in biodiesel research: A review

Mahalanobis taguchi system (MTS) is a multi-variate statistical method extensively used for feature selection and binary classification problems. The calculation of orthogonal array and signal-to-noise ratio in MTS makes the algorithm complicated when more number of factors are involved in the classification problem. Also the decision is based on the accuracy of normal and abnormal observations of the dataset. In this paper, a multiclass model using Improved Mahalanobis Taguchi System (IMTS) is proposed based on normal observations and Mahalanobis distance for agriculture development. Twenty-six input factors relevant to crop cultivation have been identified and clustered into six main factors for the development of the model. The multiclass model is developed with the consideration of the relative importance of the factors. An objective function is defined for the classification of three crops, namely paddy, sugarcane and groundnut. The classification results are verified against the results obtained from the agriculture experts working in the field. The proposed classifier provides 100% accuracy, recall, precision and 0% error rate when compared with other traditional classifier models.

Multiclass Model for Agriculture Development Using Multivariate Statistical Method

Security in this world of digital computing plays a typical role, since all the operations are automated and large volumes of data are being maintained in the servers. Cloud computing is one of the evolving technologies where a huge volume of storage is made on-line, data and services are also distributed. Because of its distributed nature, they have become easy targets for the intruders to exploit the information. The well known Distributed Denial of Service (DDoS) attack is the most prominent attacks in this area of computing. DDoS is the single largest threat to internet and internet of things. This paper provides a wide survey on various DDoS attacks, their vulnerabilities and countermeasures proposed against them. Also this paper provides an in-depth analysis on effects of DDoS attacks in the Cloud environment. Through the analysis done it will be useful for designing a secured cloud infrastructure which will abide the DDoS attacks.

Distributed Denial of service attacks and its effects on Cloud environment- a survey

Since its emergence in December 2019, there have been numerous posts and news regarding the COVID-19 pandemic in social media, traditional print, and electronic media. These sources have information from both trusted and non-trusted medical sources. Furthermore, the news from these media are spread rapidly. Spreading a piece of deceptive information may lead to anxiety, unwanted exposure to medical remedies, tricks for digital marketing, and may lead to deadly factors. Therefore, a model for detecting fake news from the news pool is essential. In this work, the dataset which is a fusion of news related to COVID-19 that has been sourced from data from several social media and news sources is used for classification. In the first step, preprocessing is performed on the dataset to remove unwanted text, then tokenization is carried out to extract the tokens from the raw text data collected from various sources. Later, feature selection is performed to avoid the computational overhead incurred in processing all the features in the dataset. The linguistic and sentiment features are extracted for further processing. Finally, several state-of-the-art machine learning algorithms are trained to classify the COVID-19-related dataset. These algorithms are then evaluated using various metrics. The results show that the random forest classifier outperforms the other classifiers with an accuracy of 88.50%.

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Detecting COVID-19-Related Fake News Using Feature Extraction

Blockchain is gaining momentum as a promising technology for many application domains, one of them being the Edge-of-Things (EoT) that is enabled by the integration of edge computing and the Internet-of-Things (IoT). Particularly, the amalgamation of blockchain and EoT leads to a new paradigm, called blockchain enabled EoT (BEoT) that is crucial for enabling future low-latency and high-security services and applications. This article envisions a novel BEoT architecture for supporting industrial applications under the management of blockchain at the network edge in a wide range of IoT use cases such as smart home, smart healthcare, smart grid, and smart transportation. The potentials of BEoT in providing security services are also explored, including access authentication, data privacy preservation, attack detection, and trust management. Finally, we point out some key research challenges and future directions in this emerging area.

Toward Blockchain for Edge-of-Things: A New Paradigm, Opportunities, and Future Directions

In recent years, there has been a rapid increase in the applications generating sensitive and personal information based on the Internet of Things (IoT). Due to the sensitive nature of the data there is a huge surge in intruders stealing the data from these applications. Hence a strong intrusion detection systems which can detect the intruders is the need of the hour to build a strong defence systems against the intruders. In this work, a Crow-Search based ensemble classifier is used to classify IoT- based UNSW-NB15 dataset. Firstly, the most significant features are selected from the dataset using Crow-Search algorithm, later these features are fed to the ensemble classifier based on Linear Regression, Random Forest and XGBoost algorithms for training. The performance of the proposed model is then evaluated against the state-of-the-art models to check for its effectiveness. The experimental results prove that the proposed model performs better than the other considered models.

B. Prabadevi

Papers

Multiclass Model for Agriculture Development Using Multivariate Statistical Method

Distributed Denial of service attacks and its effects on Cloud environment- a survey

Detecting COVID-19-Related Fake News Using Feature Extraction

Toward Blockchain for Edge-of-Things: A New Paradigm, Opportunities, and Future Directions

An ensemble model for intrusion detection in the Internet of Softwarized Things