# A Deep Learning Framework for Enhanced Detection of Polymorphic Ransomware
## 5 Conclusions  
This work explores early and accurate ransomware detection, which is crucial for mitigating its impact on individuals and organizations. Traditional detection methods often fail due to attackers' avoidance techniques and high false positive rates. To address these challenges, this framework uses a three-phase methodology: data augmentation, feature selection, and model training and validation. During the first phase, the framework develops a dynamic ransomware early detection model capable of predicting behavioral changes. By using GANs, the GBM-GAN model generates realistic attack patterns that address the lack of diverse ransomware data and enhance the capabilities of detection. Also, by leveraging the adversarial nature of GANs, the model adapts to polymorphic and metamorphic ransomware features, which improve early detection. The experimental results show a peak detection accuracy of 98%, a recall rate of 96%, and a false positive rate of 14%. This framework supports cybersecurity by predicting ransomware threats and mitigating them before causing damage. During the second phase, the framework employs incremental learning to update relevant features as new data arrives dynamically. To address ransomware's evolving nature, this work introduces the IMIS technique, which selects non-duplicative features early in an attack. This reduces computational load and improves adaptability. The results show that the IMIS-DBN model surpasses traditional methods by achieving 97.9% accuracy with 25 features and a low false positive rate of 0.104. Therefore, this highlights the importance of adaptive feature selection in ransomware detection. Future research will explore IMIS's application in real-time and wider malware detection. During the third phase, the proposed model employs a dynamic training regimen to mitigate overfitting and underfitting, thereby enhancing detection accuracy and reducing false positives. This phase introduces a UA-DES technique, which leverages uncertainty estimation and dropout simulations to optimize training. By integrating uncertainty and calibration quality metrics, UA-DES ensures heightened accuracy and reliability in ransomware detection. Empirical results demonstrate that, altogether, the UA-DES-DBN model achieves a 98.6% accuracy, surpassing traditional models. Consequently, this research advances DBN-based detection, and future work will explore alternative deep learning architectures and refine uncertainty calibration to address evolving threat landscapes. While the proposed framework demonstrates strong performance, several limitations should be acknowledged. First, the dataset is limited to portable executable files on Windows, potentially affecting generalizability to other platforms such as Linux or MacOS. Second, although the GAN  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Motivation** & **Component** & **Contribution** & **Limitation** \\ \hline \multirow{3}{*}{ \begin{tabular}{c} Lack of data during \\ pre-encryption phase \\ \end{tabular}  } & \multirow{3}{*}{BGM-GAN} & Generates synthetic & Effectiveness depends \\  & & ransomware attack & on the quality and \\  & & patterns to augment & diversity of initial real \\  & & early-phase data. & samples. \\ \multirow{3}{*}{ \begin{tabular}{c} Static feature \\ selection struggles to \\ adapt to evolving \\ threats \\ \end{tabular}  } & \multirow{3}{*}{IMIS} & \multirow{3}{*}{ \begin{tabular}{c} Dynamically selects and \\ updates relevant features in \\ real-time. \\ \end{tabular}  } & May struggle with \\  & & & & noisy or redundant \\  & & & real-time. & features. \\ \multirow{3}{*}{ \begin{tabular}{c} Risk of overfitting in \\ deep detection \\ models \\ \end{tabular}  } & \multirow{3}{*}{ \begin{tabular}{c} DBN + \\ UA-DES \\ \end{tabular}  } & Learns hierarchical & \multirow{3}{*}{\ \begin{tabular}{c} Requires tuning and \\ high computational \\ resources. \\ \end{tabular}  } \\  & & & \\  & & & \\ \cline{1-1}  & Integrated & \multirow{3}{*}{