# A Digital Twin-Based Approach for Detecting Cyber-Physical Attacks in ICS Using Knowledge Discovery
## 7 Conclusions and Future Work  
In this paper, we have addressed the cybersecurity challenges introduced by the rapid digitalization and interconnection of industrial processes. While recent works have focused on various aspects of cybersecurity in cyber-physical systems, including advances in intrusion detection and response mechanisms, there remains a need to continuously update and refine our approaches to address the evolving threat landscape. Despite industry standards and frameworks advocating for a multilayered strategy, current cybersecurity techniques often fall short in adapting to new and emerging threats. Notable advancements include the integration of machine learning algorithms for predictive threat modeling [50, 51] and innovations in anomaly detection methodologies [52]. Incorporating these recent developments into our approach could provide additional layers of security and improve detection accuracy. We proposed a novel methodology leveraging digital twins and process mining to monitor ICS event logs and detect anomalies even when attacks are masked by normal operational data. Through a realistic case study, we have demonstrated how our solution can effectively detect and analyze potential attacks. Moreover, we have presented a case study that replicates a realistic industrial process and shown how malicious activities can be detected using our approach. Our experimental assessment confirms the effectiveness of the proposed method, which is able to maintain a high detection rate despite attempts to mask attacks with benign data. The obtained results validate our method's robustness and effectiveness in a realistic industrial setting. Our current work focuses primarily on specific types of cyber-physical attacks; however, as cyber threats continue to evolve, it is crucial to consider a broader range of attack vectors. Future research should explore additional types of cyberattacks, including but not limited to the following: (i) Advanced Persistent Threats [53] (APTs) are long-term targeted attacks that can be challenging to detect and may require more sophisticated \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Event \%**} & \multicolumn{2}{c}{**Attack 1**} & \multicolumn{2}{c}{**Attack 2**} \\  & **Log Size** & **Fitness** & **Log Size** & **Fitness** \\ \hline Initial Log (0) & 406 & 0.549 & 375 & 0.0 \\ +50 (benign events) & 609 & 0.563 & 562 & 0.369 \\ +100 (benign events) & 811 & 0.579 & 750 & 0.385 \\ +150 (benign events) & 1014 & 0.608 & 937 & 0.386 \\ +200 (benign events) & 1218 & 0.614 & 1125 & 0.396 \\ \hline Avg & 811.6 & 0.582 & 749.8 & 0.307 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Fitness scores obtained by DGMining for different percentages of events for both cases. monitoring and detection techniques; (ii) insider threats consist of attacks or sabotage initiated by individuals with authorized access, and as such can be particularly insidious, requiring strategies tailored to monitor and manage internal risks; (iii) zero-day exploits are unpatched vulnerabilities that attackers can exploit before a fix is available, requiring real-time detection mechanisms to counter; finally (iv) ransomware and malware targeting ICS systems are becoming increasingly sophisticated, necessitating enhanced detection and response strategies. Examples of such attacks include those that study the physical evolution of the system and subtly manipulate it for malicious purposes as well as those that conceal their presence by executing man-in-the-middle attacks and replaying outdated data to present a healthy system state to the human-machine interface (HMI) while covertly damaging the physical process [3]. Additionally, we aim to explore the integration of advanced machine learning techniques and game theory frameworks [54,55] to further enhance the detection and prevention capabilities of our proposed solution. Conceptualization, A.P. and M.L. ; methodology, A.P. and G.S. ; software, M.L. ; validation, M.L. ; formal analysis, A.P. ; investigation, G.S. and M.L.