A deep learning based feed forward artificial neural network to predict the K-barriers for intrusion detection using a wireless sensor network

Abstract

Wireless Sensor Networks (WSN) has a wide range of opportunities and can be applied to almost every part of our life. Intrusion detection and monitoring across disputed areas and on army bases are two of the most important uses of WSN. An innovative method that precisely calculates many barriers involved in the process for quick identification and prevention of intrusions. The relevance and risk level of the chosen characteristics were assessed in this paper using feature importance and feature sensitivity metrics. This paper develops a deep learning-based feed-forward artificial neural network model that enables precise estimates of the k-barrier count for effective intrusion detection and mitigation. The area of the region of interest, sensor sensing area, sensing transmission area, and many sensors are the four potential characteristics that were used to learn and assess the feed-forward ANN model. The various attributes are extracted via Monte Carlo simulation. This research found that the method correctly forecasts the number of barriers with Root Mean Square Error and correlation coefficient. The proposed methodology is more efficient than the other standard methods.

Keywords

• Wireless sensor networks
• Intrusion detection
• K barriers
• Feed-forward artificial neural network