Optimizing Blockchain Communication Systems Security With Deep Logic Sparse Autoencoder and Kookaburra Search–Based Intrusion Detection

Recently, the rapid expansion of blockchain technology has sparked a transformative wave across various sectors, prominently impacting cybersecurity. This paper introduces a pioneering intrusion detection model, the deep logic sparse autoencoder–based kookaburra search (DLSA-KS) algorithm. This innovative approach amalgamates advanced deep learning capabilities with an efficient initial search strategy, significantly enhancing the identification and mitigation of malicious activities within digital environments. The initial phase involves gathering input data from diverse datasets, including the Malware Executable Detection dataset, KDD Cup 1999 dataset, NSL-KDD dataset, Bot-IoT dataset, and UNSW-NB15 dataset. These datasets serve as foundational resources for training and evaluating the DLSA-KS model, ensuring its efficacy across varied cyber threat scenarios. This integration not only bolsters security but also enhances scalability and real-time detection capabilities, crucial for managing the voluminous data dynamics inherent in blockchain ecosystems. Moreover, the DLSA-KS model exhibits remarkable flexibility and optimization abilities, adapting proficiently to diverse network conditions. This adaptability contributes significantly to its overall performance, enabling robust intrusion detection across a spectrum of operational environments. In addition to this, the proposed DLSA-KS approach is evaluated across multiple performance metrics, including accuracy rate, detection rate, error rate, precision, and F-measure. The findings unequivocally demonstrate the model's superiority over existing methodologies, achieving exceptional metrics such as an accuracy rate of 98.7%, detection rate of 99.2%, error rate of 3%, precision of 97.8%, and F-measure of 98.7%. Thus, the results underscore the efficacy of the DLSA-KS algorithm in effectively detecting and mitigating intrusions, thereby affirming its potential as a pivotal advancement in cybersecurity defenses.