Detection of economic denial of sustainability (EDoS) threats in self-organizing networks

Smart mirror fashion technology for the retail chain transformation

According to the United Nations, Sustainable Development Goals are framed for improving rural health, hunger, poverty issues, environmental conditions, and illiteracy globally. With the upcoming technology, there have been many advances in the lifestyle of people all around the world. Comparatively, more emphasis has been given to the development of urban areas than rural. The sustainable development of a country depends on the growth of its rural areas. Countless technological and theoretical models, projects, and frameworks have been proposed and implemented to help overcome sundry issues and challenges faced by rural people in quotidian life. New technological methods are deemed to be the future of livability, therefore; a technologically advanced solution for sustainable rural development is called for. Blockchain Technology is the next step for innovation and development and it has far many applications in sustainable rural development that are yet to be discovered. The objective of this paper is to explicitly review research conducted in rural development to fill the undone work in the future with better research ideas, to make rural areas a livable and advanced place while also maintaining their integrity leading to sustainable development. To conduct such a review, a systematic research methodology is applied following regulations in the conduction of standardized but explorative analysis. Within the timeline of 2010–2021, 112 papers are carefully selected to perform the systematic review. This review will provide a comprehensible as well as concise research compendium for all applications proposed, implemented, and possible in the future to realize the concept of smart villages for the development of rural areas using blockchain technology.

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A Systematic Literature Review of Blockchain Technology for Smart Villages.

The recent times have seen unprecedented growth in research and development in the field of Artificial Intelligence and its allied fields, subfields, like Machine Learning, Deep Learning and Natural Language Processing. The new found excitement in these fields of research is the availability of high powered computing devices at a cheaper costs and the wide range of applications of these research fields. It can be safely said that in the present times there are hardly any sectors of businesses which have not been positively impacted by artificial intelligence or its subfields. Machine learning and deep learning, apart from having a positive impact on different sectors of businesses, machine learning and deep learning have also played a very important role in improving the efficiency of other subfields of artificial intelligence, including computer vision and natural language processing. Natural Language Processing is the ability of computer systems to understand human languages, which is a difficult task where these learning techniques has played a very important role in proper analysis. The paper proposes to highlight this important role played by the learning techniques in improving the efficiency of natural language processing.

Impact of Machine Learning in Natural Language Processing: A Review

Cloud computing is currently the most cost-effective means of providing commercial and consumer IT services online. However, it is prone to new flaws. An economic denial of sustainability attack (EDoS) specifically leverages the pay-per-use paradigm in building up resource demands over time, culminating in unanticipated usage charges to the cloud customer. We present an effective approach to mitigating EDoS attacks in cloud computing. To mitigate such distributed attacks, methods for detecting them on different cloud computing smart grids have been suggested. These include hard-threshold, machine, and deep learning, support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF) tree algorithms, namely convolutional neural network (CNN), and long short-term memory (LSTM). These algorithms have greater accuracies and lower false alarm rates and are essential for improving the cloud computing service provider security system. The dataset of nine injection attacks for testing machine and deep learning algorithms was obtained from the Cyber Range Lab at the University of New South Wales (UNSW), Canberra. The experiments were conducted in two categories: binary classification, which included normal and attack datasets, and multi-classification, which included nine classes of attack data. The results of the proposed algorithms showed that the RF approach achieved accuracy of 98% with binary classification, whereas the SVM model achieved accuracy of 97.54% with multi-classification. Moreover, statistical analyses, such as mean square error (MSE), Pearson correlation coefficient (R), and the root mean square error (RMSE), were applied in evaluating the prediction errors between the input data and the prediction values from different machine and deep learning algorithms. The RF tree algorithm achieved a very low prediction level (MSE = 0.01465) and a correlation R2 (R squared) level of 92.02% with the binary classification dataset, whereas the algorithm attained an R2 level of 89.35% with a multi-classification dataset. The findings of the proposed system were compared with different existing EDoS attack detection systems. The proposed attack mitigation algorithms, which were developed based on artificial intelligence, outperformed the few existing systems. The goal of this research is to enable the detection and effective mitigation of EDoS attacks.

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Artificial Intelligence Algorithm-Based Economic Denial of Sustainability Attack Detection Systems: Cloud Computing Environments

Sarcasm is a subtle type of irony, which can be widely used in social networks. It is usually used to transmit hidden information to criticize and ridicule a person and to recognize. The sarcastic reorganization system is very helpful for the improvement of automatic sentiment analysis collected from different social networks and microblogging sites. Sentiment analysis refers to internet users of a particular community, expressed attitudes and opinions of identification and aggregation. In this paper, to detect sarcasm, a pattern-based approach is proposed using Twitter data. Four sets of features that include a lot of specific sarcasm is proposed and classify tweets as sarcastic and non-sarcastic. The proposed feature sets are studied and evaluate its additional cost classifications.

Machine Learning based Sarcasm Detection on Twitter Data

Cloud computing is a technology that allows the end-users to access the network through a shared area of resources. As the demand for the cloud computing increases, vulnerabilities in the service p...

FLNL: Fuzzy entropy and lion neural learner for EDoS attack mitigation in cloud computing

Storing data in real time along with keeping it secure is the biggest challenge in industry today Many land issues arise because there is no database which is protected for tracking the real-time changes in data Also, land records currently are registered in paper format This kind of data is thus vulnerable to any changes and maybe destroyed by natural or man-made disasters The emerging blockchain technology is a boon to store any information in real time and is immune to any changes In this paper, we propose a solution in the form of distributed app (DApp) which uses the idea of blockchain as distributed database, smart contracts using ethereum platform and Polyline API from Google to mark the land boundaries Smart contracts allow the performance of credible transactions by using sophisticated cryptography and without interference from third parties These transactions are traceable and irreversible Proponents of smart contracts claim that many kinds of contractual clauses may be made partially or fully self-enforcing In this case, along with the self-verifiable clauses, involving banking parties can perform additional monetary checking A user can sell or transfer a property that he owns or may buy a new land plot open for sale in desired geographical area This solution allows maintaining land records easily and in real time without having a single point of failure for the database system Removal of third-party interventions such as brokers from the process of land title transfer between old and new owners makes the process more transparent and cheaper

Land Record Maintenance Using Blockchain

Several vendors opt for Cloud based website hosting nowadays, as it is very trustworthy, easily scalable and reasonable hosting solution than traditional website hosting in various aspects. The most common attacks carried out currently against web application are SQL injection. Cross Site Scripting (XSS), Distributed Denial of Service (DDoS) and Cross Site Request Forgery (CSRF). Distributed Denial-of-service (DDoS) attack is one of the major web security attacks to the cloud hosted websites. Hence, this paper mainly concentrates upon soft computing techniques used so far against such attack. The most straightforward way to carry out the attack is through botnet. The malicious HTTP traffic generated by an attacker imitates to be genuine and hence go undetected. Nowadays, different soft computing techniques are used as a tool to detect malicious traffic. This paper presents a review of these techniques implemented so far that differentiate between genuine and malicious traffic. Economic Denial of Sustainability (EDoS) is a special sort ο f DDoS attack that exploits cloud's pay-as-you-go model. The attacker generates anomalous HTTP requests that cause up scaling of the architecture and this puts financial burden over website owner. This paper tries to realize whether existing techniques can be used for defending EDoS attack and it also points out research directions in this field.

A survey: Cloud hosted website protection using soft computing techniques

Smart devices are exciting and upcoming advancements in the field of Internet of things (IOT). It is an application of Raspberry Pi, from furniture in the residential and commercial sector to everyday devices like watches and headphones, every object is becoming smarter and hence, making human lives much easier and efficient. Users can actively interact with these devices using voice commands. These smart devices can also assist a user in their personal daily activities as well as in medical use. Currently, various variations of the smart mirror based on software and hardware compatibility as well as based on applications and uses are available in the market. The goal of this paper is to understand the working, various applications, and purposes of the smart mirror according to the varying features in different versions. This paper critically analyses the different versions of the smart mirror and its different features and applications.

Sukhada Bhingarkar

Papers

Machine Learning based Sarcasm Detection on Twitter Data

FLNL: Fuzzy entropy and lion neural learner for EDoS attack mitigation in cloud computing

Land Record Maintenance Using Blockchain

A survey: Cloud hosted website protection using soft computing techniques

A Study Based on Advancements in Smart Mirror Technology