Evolutionary Algorithms in AI-Driven Cybersecurity Solutions for Adaptive Threat Mitigation

Abstract

The rapid evolution of ransomware attacks has posed significant challenges to traditional cybersecurity measures, necessitating innovative approaches for effective identification and mitigation. This study presents a multi-modal approach leveraging artificial intelligence (AI) to enhance the detection, analysis, and prevention of ransomware attacks. By integrating machine learning (ML), deep learning (DL), and natural language processing (NLP) techniques, this approach aims to provide a comprehensive defense mechanism against the sophisticated tactics employed by ransomware actors. Our methodology involves the utilization of supervised and unsupervised ML algorithms to identify ransomware signatures and anomalous behaviors indicative of potential attacks. Specifically, convolutional neural networks (CNNs) are employed to detect patterns in file structures and network traffic associated with ransomware. Recurrent neural networks (RNNs), particularly long short-term memory (LSTM) networks, are used to analyze temporal sequences of system activities, identifying deviations that suggest ransomware execution. NLP techniques are integrated to analyze threat intelligence from unstructured text data, such as dark web forums and phishing emails, to extract relevant indicators of compromise (IOCs) and understand the evolving threat landscape. Sentiment analysis and topic modeling further enhance the predictive capabilities by identifying emerging ransomware trends and actor motivations. The study also addresses adversarial robustness by implementing adversarial training and defensive distillation, ensuring that AI models remain resilient against evasion techniques employed by ransomware developers. Evaluation metrics such as accuracy, precision, recall, F1- score, and ROC-AUC demonstrate the efficacy of the proposed approach. Results indicate that the multi-modal AI approach significantly improves the early detection and mitigation of ransomware attacks, reducing the time to response and minimizing the impact on affected systems. This research highlights the potential of AI-driven solutions to provide a robust, adaptive defense mechanism against the rapidly evolving threat of ransomware, contributing to the development of more resilient cybersecurity infrastructures. Future work will focus on enhancing model interpretability, improving real-time response capabilities, and ensuring compliance with ethical standards and data privacy regulations.