Ransomware recognition in blockchain network using water moth flame optimization-aware DRNN

What is it about?

The emergence of networking systems and quick deployment of applications cause huge increase in cybercrimes which involves various applications like phishing, hacking, and malware propagation. However, the Ransomware techniques utilize certain device which may lead to undesirable properties which might shrink the paying-victim pool. This paper devises a new method, namely Water Moth Flame optimization (WMFO) and deep recurrent neural network (Deep RNN) for determining Ransomware. Here, Deep RNN training is done with WMFO, and is developed by combining Moth Flame optimization (MFO) and Water wave optimization (WWO). Moreover, features are mined with opcodes and by finding term frequency-inverse document frequency (TF-IDF) amongst individual features. Moreover, Probabilistic Principal Component Analysis (PPCA) is adapted to choose significant features. These features are adapted in Deep RNN for classification, wherein the proposed WMFO is employed to produce optimum weights. The WMFO offered enhanced performance with elevated accuracy of 95.025%, sensitivity of 95%, and specificity of 96%.

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Why is it important?

The integration of WMFO and Deep RNN represents an innovative approach to ransomware detection. By combining optimization techniques (MFO and WWO) with deep learning (Deep RNN), the method offers a robust framework for identifying and mitigating ransomware threats. The incorporation of opcodes and TF-IDF features, along with Probabilistic Principal Component Analysis (PPCA) for feature selection, underscores a comprehensive approach to capturing relevant characteristics of ransomware behavior. This ensures that the model is trained on the most discriminative features for accurate classification. The reported performance metrics demonstrate the effectiveness of the proposed method, with an elevated accuracy of 95.025%, sensitivity of 95%, and specificity of 96%. This indicates a high level of accuracy in identifying ransomware instances, which is crucial for timely intervention and mitigation.

Perspectives

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The increasing complexity and rapid deployment of networking systems and applications have led to a surge in cybercrimes, including phishing, hacking, and malware propagation. Ransomware attacks, in particular, pose significant threats to individuals and organizations, highlighting the need for effective detection and prevention strategies. Traditional cybersecurity measures may be insufficient in detecting and mitigating advanced cyber threats like Ransomware. New approaches that leverage advanced technologies such as deep learning and optimization algorithms are necessary to enhance cybersecurity defenses

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