Privacy Preserving of Data Files & Audio / Video Encryption –Decryption Using AES Algorithm

Recently in many areas like facebook , watsapp and many more social networking site many users upload their personal data, video ,voice recording. This paper proposed and idea of encryption – decryption of any file which user s going to upload on site. The specific site which are providing such kind of functionality needs to adopts this method to secure user data for privacy preserving.So that any hackers or indruder can not directly hike your data. If in exceptional cases someone even hacks the data they will not get the actual file they will only get the encrypted file withoud having a decrypt key for the data. So they never see an original file.This will improve the data security over internet uses. The proposed system wiil used a special Advanced Encryption Standard, also known by its original name Rijndael for secure encryption decryption of audio ,video as well as data files

Outlier Detection Techniques For Wireless Sensor Networks: A Survey

In the field of wireless sensor networks, measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the multivariate nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a decision tree to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier degree

Outlier detection techniques for wireless sensor networks: A survey

In the field of wireless sensor networks, those measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a comparative table to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier identity, and outlier degree

model checking for data anomaly detection

Abstract Data tipically evolve according to specific processes, with the consequent possibility to identify a profile of evolution: the values it may assume, the frequencies at which it changes, the temporal variation in relation to other data, or other constraints that are directly connected to the reference domain. A violation of these conditions could be the signal of different menaces that threat the system, as well as: attempts of a tampering or a cyber attack, a failure in the system operation, a bug in the applications which manage the life cycle of data. To detect such violations is not straightforward as processes could be unknown or hard to extract. In this paper we propose an approach to detect data anomalies. We represent data user behaviours in terms of labelled transition systems and through the model checking techniques we demonstrate the proposed modeling can be exploited to successfully detect data anomalies

Outlier-Aware Data Aggregation in Sensor Networks

Abstract- In this paper we discuss a robust aggregation framework that can detect spurious measurements and refrain from incorporating them in the computed aggregate values. Our framework can consider different definitions of an outlier node, based on a specified minimum support. Our experimental evaluation demonstrates the benefits of our approach. I