A Secure Scheme for Detecting Provenance Forgery and Masquerade Attack in Wireless Sensor Networks

Abstract

The sensor networks are popularly increasing and it used as a effective way in decision making infrastructures such as Data Acquisition systems, Supervisory Control industrial monitoring, battlefield monitoring systems. It is difficult for the process to maintain the trustworthiness of the data. To address this problem, we propose a systematic technique for evaluating the trustworthiness of data. This approach uses the data provenance and it provides quantitative measures for trustworthiness. The encoded method is used in the provenance approach as sensor data travels through intermediate sensor nodes. Then it is decoded and verified at the base station. The is also able to protect from The harm from malicious attacks such as packet dropping is also be protected by the provenance technique and the responsible node for packet drops is also detected. As such it makes possible to modify the route to avoid nodes that could be compromised or malfunctioning. Another major issue is a Masquerade attack. The masquerade attack, where an attacker takes on the identity of a confirming the user to maliciously utilize that user’s privileges, obtain a serious threat to the security of information systems. Therefore a large number of attempts has been made at detecting these attacks; yet achieving high levels of accuracy remains an open challenge. In this work, Data Driven Semi-Global Alignment (DDSGA) approach is used to detect the masquerade attack.