Optimized deep stacked autoencoder for ransomware detection using blockchain network

What is it about?

The innovation of technologies has become ubiquitous and imperative in day-to-day lives. Malware is the major threat to the network, and Ransomware is a special and harmful type of malware. Ransomware led to huge data losses and induced huge economic costs. Moreover, Ransomware detection is a crucial task to minimize analyst’s workloads. This paper devises a novel deep learning method for detecting Ransomware using the blockchain network. Here, the sequence-based statistical feature extraction is performed, wherein the features are extracted using 2-gram and 3-gram opcodes. Also, the term frequency-inverse document frequency (TF-IDF) is discovered for each feature. Then the Box-Cox transformation is applied to transformation to the data for improved analysis. Also, the feature fusion is progressed using a fractional concept. Finally, the classification of Ransomware is done using Deep stacked Auto-encoder (Deep SAE), wherein the proposed Water wave-based Moth Flame optimization (WMFO) is adapted for generating the optimal weights. The WMFO is designed by integrating Water wave optimization (WWO) and Moth Flame optimization (MFO). The proposed WMFO-Deep SAE outperformed other methods with maximal accuracy of 96.925%, sensitivity of 96.900%, and specificity of 97.920%.

Featured Image

Why is it important?

Ransomware is identified as a major threat to network security, causing substantial data losses and economic costs. By focusing on ransomware detection, the paper addresses a critical cybersecurity challenge that has serious implications for individuals, organizations, and society as a whole. The paper introduces a novel deep learning method for ransomware detection, leveraging advancements in technology to combat evolving threats. This demonstrates a proactive approach to staying ahead of cybercriminal tactics and adapting detection techniques to new challenges.

Perspectives

“

The statement emphasizes the pervasive nature of technological innovations in daily life and the accompanying necessity for addressing security threats. It specifically highlights malware, with ransomware being identified as a particularly harmful type, causing substantial data loss and economic damage. The statement underscores the importance of detecting ransomware, emphasizing its criticality in minimizing the workload of analysts who are tasked with mitigating such threats.

”