# Opportunities for Early Detection and Prediction of
## 4 Research Direction and Future Work
### Suggestions for Future Works  
Based on the literature reviewed, it is likely that the rise of ransomware targeting SCADA ICS will continue. This is due to the characteristics of SCADA systems that permit remote access and the interaction with diverse components, unique within bespoken environments, making them vulnerable to ransomware attacks. Ransomware can encrypt the operational data and disrupt the SCADA system, but predicting its behavior can aid in the development of proactive measures that enhance ICS security. However, current research efforts either concentrate on the operational side of attacks or the managerial and human aspects, neglecting the context in which the attacks occur. Previous investigations have also overlooked the evolutionary nature that allows ransomware to evolve its attacks with no or less similarity to existing attacks to negatively affect the defense systems to predict upcoming threats. Hence, the defense systems become less protective. This weakness negatively impacts the effectiveness of predictive modeling, as ransomware's behavior varies depending on the situation and context. As a result, there is a need to combine both operational and human factors when evaluating the probability of ransomware attacks. This can be accomplished by gathering data from the running malware process, the underlying operating environment, situational data related to organizational and human factors, and the system's current security posture, through a daunting set of tasks. As deep learning has emerged as a promising technique for predicting ransomware attacks, it can be used to develop deep neural networks that can analyze vast amounts of data from various sources and identify patterns that indicate the likelihood of a ransomware attack. One direction of research is to develop multi-modal deep-learning models that consider multiple data sources, such as network traffic, system logs, and user behavior, to make more accurate predictions. Different modalities, such as network traffic data, system logs, and user behavior, can be used to build a more comprehensive representation of the system state and identify patterns that indicate the likelihood of a ransomware attack. Another direction is to incorporate adversarial examples and defend against malicious attacks in the training process, to increase the robustness of the deep-learning models against real-world threats. Adversarial examples are intentionally crafted inputs that are designed to fool machine-learning models. Research in this direction aims to develop deep-learning models that can defend against such malicious attacks and improve the robustness of the models against real-world threats. Normally, generative adversarial networks (GAN) are used to generate artificial ransomware examples that represent the future behavior of the attacks. Improving the interpretability of the deep-learning models is another research direction so that organizations can understand the reasoning behind the predictions and make informed decisions. The interpretability of deep-learning models refers to the ability to understand the reasoning behind the predictions made by the model. Research in this area aims to improve the interpretability of deep-learning models so that organizations can understand the factors that contribute to the prediction of a ransomware attack and make informed decisions. In addition, research can also focus on developing transfer-learning models that can be trained on one domain and then transferred to another domain to improve the accuracy of predictions in new scenarios [51; 52]. Transfer learning is a machine-learning technique where a model trained on one domain is applied to another domain. In the context of ransomware attack prediction, transfer-learning models can be trained on one set of data and then transferred to another domain to improve the accuracy of predictions in new scenarios. This allows the predictive model to make use of data from similar (or generic) scenarios and applies it in a specific SCADA scenario.