http://home.engineering.iastate.edu/~morris/papers/14/crn_survey_deanna.pdf

A layered approach to cognitive radio network security

Stock market data is a time-series data in which stock value varies depends on time. Prediction of the stock market is an endeavor to assess the future value of a company’s stock rate which will increase the investor’s profit. The accurate prediction of stock market analysis is still a challenging task. The proposed system predicts stock price of any company mentioned by the user for the next few days. Using the predicted stock price and datasets collected from various sources regarding a certain equity, the overall sentiment of the stock is predicted. The prediction of stock price is done by regression and candlestick pattern detection. The proposed system generates signals on the candlestick graph which allows to predict market movement to a sufficient level of accuracy so that the user is able to judge whether a stock is a ‘Buy/Sell’ and whether to short the stock or go long by delivery. The prediction accuracy of the stock exchange has analyzed and improved to 85% using machine learning algorithms.

Stock market analysis using candlestick regression and market trend prediction (CKRM)

Anomaly detection and trust authority in artificial intelligence and cloud computing

With the information and communication technologies (ICT) and Internet of Things (IoT) gradually advancing, smart homes have been able to provide home services to users. The user can enjoy a high level of comfort and improve his quality of life by using home services provided by smart devices. However, the smart home has security and privacy problems, since the user and smart devices communicate through an insecure channel. Therefore, a secure authentication protocol should be established between the user and smart devices. In 2020, Xiang and Zheng presented a situation-aware protocol for device authentication in smart grid-enabled smart home environments. However, we demonstrate that their protocol can suffer from stolen smart device, impersonation, and session key disclosure attacks and fails to provide secure mutual authentication. Therefore, we propose a secure and lightweight authentication protocol for IoT-based smart homes to resolve the security flaws of Xiang and Zheng’s protocol. We proved the security of the proposed protocol by performing informal and formal security analyses, using the real or random (ROR) model, Burrows–Abadi–Needham (BAN) logic, and the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. Moreover, we provide a comparison of performance and security properties between the proposed protocol and related existing protocols. We demonstrate that the proposed protocol ensures better security and lower computational costs than related protocols, and is suitable for practical IoT-based smart home environments.

A Secure and Lightweight Authentication Protocol for IoT-Based Smart Homes

Cognitive radio networks (CRNs) are an emerging paradigm for next generation wireless communication systems allowing for more efficient radio spectrum utilization. In order to harness the full potential that CRNs may offer, many challenges and problems need to be overcome and addressed. One of the critical questions is the performance of secondary networks under primary user activity constraints. In this respect, queueing assumes a primary role in characterizing the delay, throughput and other performance metrics for secondary users, which in turn has implications for resource allocation, medium access control and quality of service provisioning. This survey presents an overview and classification of the various queueing models and techniques which have been proposed in the literature in the context of CRNs. Furthermore, open problems, future research directions and further potential applications related to queueing for CRNs are identified.

Queueing Models for Cognitive Radio Networks: A Survey

In Stock Market Prediction, the aim is to predict the future value of the financial stocks of a company. The recent trend in stock market prediction technologies is the use of machine learning which makes predictions based on the values of current stock market indices by training on their previous values. Machine learning itself employs different models to make prediction easier and authentic. The paper focuses on the use of Regression and LSTM based Machine learning to predict stock values. Factors considered are open, close, low, high and volume.

Stock Market Prediction Using Machine Learning

The edge-based Internet of Things (IoT) computing provides a new value for the consumer where the smart devices, objects, and appliances connected over the internet. The data generated from the smart IoT devices need to be securely processed. With the increasing rate of smart IoT devices, the existing addressing schemes and security protocols do not guaranty to perform well in all situations. This paper proposed a smart card based secure addressing and authentication (SCSAA) scheme by modifying the standard IPv6 protocol to mitigate the security threats in the IoT network. The proposed scheme has two folds; firstly, this scheme provides a unique way of addressing by assigning unique 64-bit interface identifier (IID) to smart devices/appliances and uniquely authenticates them in IoT network. Secondly, this scheme uses the secret session key to prevent the network from unauthorized access. Additionally, this work also evaluates the informal security analysis, formal security analysis using ROR model and AVISPA tool. The overall security analysis proves that proposed scheme protect the smart home IoT network from various vulnerabilities and attacks.

A secure authentication scheme for IoT application in smart home

A privacy and session key based authentication scheme for medical IoT networks

This paper is presents a Comprehensive Survey on Tabu Search Algorithms (TSA). TSA is a meta heuristics kind of algorithm which works on global optimal solution for a given problem such as vehicle routing problem (VRP), open vehicle routing problem (OVRP), multi-trip vehicle routing and scheduling problem (MTVRSP), container loading problem (CLP) and job shop problem, etc. in this paper focuses on main characteristics of TSA and its behaviour.

Tabu Search Algorithm (TSA): A Comprehensive Survey

Crime is one of the dominant and alarming aspect of our society. Everyday huge number of crimes are committed, these frequent crimes have made the lives of common citizens restless. So, preventing the crime from occurring is a vital task. In the recent time, it is seen that artificial intelligence has shown its importance in almost all the field and crime prediction is one of them.However, it is needed to maintain a proper database of the crime that has occurred as this information can be used for future reference. The ability to predict the crime which can occur in future can help the law enforcement agencies in preventing the crime before it occurs. The capability to predict any crime on the basis of time, location and so on can help in providing useful information to law enforcement from strategical perspective. However, predicting the crime accurately is a challenging task because crimes are increasing at an alarming rate. Thus, the crime prediction and analysis methods are very important to detect the future crimes and reduce them. In Recent time, many researchers have conducted experiments to predict the crimes using various machine learning methods and particular inputs. For crime prediction, KNN, Decision trees and some other algorithms are used. The main purpose is to highlight the worth and effectiveness of machine learning in predicting violent crimes occurring in a particular region in such a way that it can be used by police to reduce crime rates in the society.

Lokesh Chouhan

Papers

Stock Market Prediction Using Machine Learning

A secure authentication scheme for IoT application in smart home

A privacy and session key based authentication scheme for medical IoT networks

Tabu Search Algorithm (TSA): A Comprehensive Survey

Crime Prediction and Analysis