Abstract
A distributed system comprises several independent units, each planned to track its tasks without interconnecting with the rest of them, excluding messaging services. This indicates that a solitary point of failure can reduce a method incapable without caution since no single point can achieve all essential processes. Malicious node recognition is a crucial feature of safeguarding the safety and reliability of distributed methods. Numerous models, ranging from anomaly recognition techniques to machine learning (ML) methods, are used to examine node behaviour and recognize deviances from usual patterns that may designate malicious intent. Advanced cryptographic protocols and intrusion detection devices are often combined to improve the flexibility of these methods against attacks. Moreover, real-time observing and adaptive plans are vital in quickly identifying and answering emerging attacks, contributing to the complete sturdiness of safe distributed methods. This study designs a Hybrid Bald Eagle-Crow Search Algorithm and Deep Learning for Enhanced Malicious Node Detection (HBECSA-DLMND) technique in Secure Distributed Systems. The HBECSA-DLMND technique follows the concept of metaheuristic feature selection with DL-based detection of malicious nodes in distributed systems. To accomplish this, the HBECSA-DLMND technique performs data normalization using the linear scaling normalization (LSN) approach, and the ADASYN approach is employed to handle class imbalance data. Besides, the HBECSA-DLMND method utilizes the HBECSA technique to choose a better subset of features. Meanwhile, the convolutional sparse autoencoder (CSAE) model detects malicious nodes. Finally, the dung beetle optimization (DBO) method is employed for the parameter range of the CSAE method. The experimental evaluation of the HBECSA-DLMND methodology is examined on a benchmark WSN-DS database. The performance validation of the HBECSA-DLMND methodology illustrated a superior accuracy value of 98.99% over existing approaches.