Localization and Detection of Multiple Attacks in Wireless Sensor Networks Using Artificial Neural Network

Abstract

Security enhancement in wireless sensor networks (WSNs) is significant in different applications. The advancement of routing attack localization is a crucial security research scenario. Various routing attacks degrade the network performance by injecting malicious nodes into wireless sensor networks. Sybil attacks are the most prominent ones generating false nodes similar to the station node. This paper proposed detection and localization against multiple attacks using security localization based on an optimized multilayer perceptron artificial neural network (MLPANN). The proposed scheme has two major part localization techniques and machine learning techniques for detection and localization WSN DoS attacks. The proposed system is implemented using MATLAB simulation and processed with the IBM SPSS toolbox and Python. The dataset is classified into training and testing using the multilayer perceptron artificial neural network to detect ten classes of attacks, including denial-of-service (DoS) attacks. Using the UNSW-NB, WSN-DS, NSL-KDD, and CICIDS2018 benchmark datasets, the results reveal that the suggested system improved with an average detection accuracy of 100%, 99.65%, 98.95%, and 99.83% for various DoS attacks. In terms of localization precision, recall, accuracy, and f-score, the suggested system outperforms state-of-the-art alternatives. Finally, simulations are done to assess how well the suggested method for detecting and localizing harmful nodes performs in terms of security. This method provides a close approximation of the unknown node position with low localization error. The simulation findings show that the proposed system is effective for the detection and secure localization of malicious attacks for scalable and hierarchically distributed wireless sensor networks. This achieved a maximum localization error of 0.49% and average localization accuracy of 99.51% using a secure and scalable design and planning approach.

Blockchain-Based Secure Localization against Malicious Nodes in IoT-Based Wireless Sensor Networks Using Federated Learning

“…The sensor nodes are assumed to be randomly deployed and are unaware of their location and position. At the same time, the beacon nodes and sink nodes are aware of their locations and positions, and they assist the unknown sensor nodes in locating the sensor nodes and identifying the malicious nodes by computing the distance between each node and its position, with each node having its own distinctive identifier [29]. The network model incorporates challenging design requirements and assumptions for efficient performance.…”

“…For one-bit data transmission, we get where E TX is the energy being sent, E f is the energy being received, and E e is the power being lost somewhere along the line [29]. Signal propagation, filtering, modulation, and channel coding all play a role in the amount of energy lost in the process.…”

“…Finding the nodes' locations and strengths of connections, as well as evaluating their positions, is tasks for which specific algorithms are required. The system is broken down into two types of categories, range-based and range-free localization methods [29]. The second option is more practical financially, but it needs specialized hardware.…”

“…Geometric calculations like triangulation, the polygon technique, and trilateration are frequently employed as the underlying basis for determining the locations of unknown nodes [29]. There are a number of methods that can be used to calculate the separation of two nodes; they include synchronization, radio signal intensity, and the physical features of the carrying wave [37].…”

“…The average detection rate, accuracy, precision, and recall are also used to evaluate the effectiveness the proposed system. In order to calculate the ALE, add up the LE of all the unknown nodes and divide by the total number of unknown nodes [29].The LE represents the discrepancy between the estimated and true locations of any undiscovered nodes.…”