Wireless Sensor Networks (WSNs) were exposed to several distinct safety issues and attacks regarding gathering and sending data. In this scenario, one of the most prevalent WSN assaults that may target any tier of the protocol stack is the Denial of Service (DoS) attack. The current research suggested various strategies to find the attack in the network. However, it has classification challenges. An effective ensemble deep learning-based intrusion detection system to identify the assault in the WSN network was, therefore, suggested in this research to address this issue. The data pre-processing involves converting qualitative data into numeric data using the One-Hot Encoding technique. Following that, Normalization Process was carried out. Then Manta-Ray Foraging Optimization is suggested to choose the best subset of features. Then Synthetic Minority Oversampling Technique (SMOTE) oversampling creates a new minority sample to balance the processed dataset. Finally, CNN–SVM classifier is proposed to classify the attack kinds. The Accuracy, F-Measure, Precision, and Recall metrics were used to assess the outcomes of 99.75%, 99.21%, 100%, and 99.6%, respectively. Compared to existing approaches, the proposed method has shown to be extremely effective in detecting DoS attacks in WSNs.
DDoS attacks are a widespread method of making network information systems out of service. Furthermore, the malefactors combine multiple types of attacks in order to increase the intrusion efficiency. This paper considers the network traffic parameters enabling system state monitoring and invasion tracking. There are defined thresholds and conditions that allow linking the parameters’ behavior to the type of attacks the system is exposed to.
The paper considers an analysis of a protection mechanism against infrastructure attacks based on the bio-inspired approach ―nervous network system‖. We propose to use a network packet-level simulation to investigate the protection mechanism ―nervous network system‖. The paper presents the structure of the protection mechanism, the algorithms of its functioning, and the results of the experiments. Basing on the experimental data, we analyze the effectiveness of the proposed protection mechanism.
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