Water Pipeline Leakage Detection Based on Machine Learning and Wireless Sensor Networks

The detection of water pipeline leakage is important to ensure that water supply networks can operate safely and conserve water resources. To address the lack of intelligent and the low efficiency of conventional leakage detection methods, this paper designs a leakage detection method based on machine learning and wireless sensor networks (WSNs). The system employs wireless sensors installed on pipelines to collect data and utilizes the 4G network to perform remote data transmission. A leakage triggered networking method is proposed to reduce the wireless sensor network’s energy consumption and prolong the system life cycle effectively. To enhance the precision and intelligence of leakage detection, we propose a leakage identification method that employs the intrinsic mode function, approximate entropy, and principal component analysis to construct a signal feature set and that uses a support vector machine (SVM) as a classifier to perform leakage detection. Simulation analysis and experimental results indicate that the proposed leakage identification method can effectively identify the water pipeline leakage and has lower energy consumption than the networking methods used in conventional wireless sensor networks

Privacy Preservation in data leakage detection of dynamically changing sensitive data

The number of data leakage instances is increasing day by day. Human mistakes are main cause of data leakage. Data leakage caused by human mistakes has some solutions. These solutions provide an alert when there is a data leakage. One of the common approach is to give the contents in the databases and transmitted information over the network to the data leakage detection service provider for getting information about the leakage. But this is an open invitation for an outsider to gain the knowledge about sensitive information about the company or an organization; because the detection is based on third party and contents are available with the data leakage detection service provider. In this report, author has proposed technique for data leakage detection using Rabin fingerprint and RSA algorithm. Using this technique, data owners can perform data leakage detection by preserving the privacy of their sensitive information

Review on Privacy Preservation in Data Leakage Detection

The number of data leak instances are increasing day by day. Human mistakes are main cause of data leakage . Data leakage caused by human mistakes have some solutions. These solutions provide an alert when there is a data leakage. One of the common approach is to give the contents in the databases and transmitted information over the network to the data leakage detection service provider for getting information about the leakage ,but this is an open invitation for an outsider to gain the sensitive information about the company or an organization ;because the detection is based on third party. In this paper, we have done the analysis on various techniques of data leakage detection along with their privacy issu

Data Leakage Detection using Cloud Computing

Today the present world mostly depends on exchange of information i.e. transfer of data from one person to another person which is also known as distributary system. The data is sent from the distributor to the user are confidential so the data is distributed only between the distributor and the trusted third parties. The data sent by the distributor must be secured, confidential and must not be reproduced as the data shared with the trusted third parties are confidential and highly important. In some occasions the data distributed by the distributor are copied by different agents who cause a huge damage to the institute and this process of losing the data is known as data leakage. The data leakage must be detected in the early stage in order to protect the data form being open source. This project deals with protecting the data from being out sourcing by giving a special inscription to the sensitive data so that it cannot be reproduced

A Survey: Data Leakage Detection Techniques

Data is an important property of various organizations and it is intellectual property of organization. Every organization includes sensitive data as customer information, financial data, data of patient, personal credit card data and other information based on the kinds of management, institute or industry. For the areas like this, leakage of information is the crucial problem that the organization has to face, that poses high cost if information leakage is done. All the more definitely, information leakage is characterize as the intentional exposure of individual or any sort of information to unapproved outsiders. When the important information is goes to unapproved hands or moves towards unauthorized destination. This will prompts the direct and indirect loss of particular industry in terms of cost and time. The information leakage is outcomes in vulnerability or its modification. So information can be protected by the outsider leakages. To solve this issue there must be an efficient and effective system to avoid and protect authorized information. From not so long many methods have been implemented to solve same type of problems that are analyzed here in this survey.  This paper analyzes little latest techniques and proposed novel Sampling algorithm based data leakage detection techniques

Water Pipeline Leakage Detection Based on Machine Learning and Wireless Sensor Networks

The detection of water pipeline leakage is important to ensure that water supply networks can operate safely and conserve water resources. To address the lack of intelligent and the low efficiency of conventional leakage detection methods, this paper designs a leakage detection method based on machine learning and wireless sensor networks (WSNs). The system employs wireless sensors installed on pipelines to collect data and utilizes the 4G network to perform remote data transmission. A leakage triggered networking method is proposed to reduce the wireless sensor network’s energy consumption and prolong the system life cycle effectively. To enhance the precision and intelligence of leakage detection, we propose a leakage identification method that employs the intrinsic mode function, approximate entropy, and principal component analysis to construct a signal feature set and that uses a support vector machine (SVM) as a classifier to perform leakage detection. Simulation analysis and experimental results indicate that the proposed leakage identification method can effectively identify the water pipeline leakage and has lower energy consumption than the networking methods used in conventional wireless sensor networks