# Enhancing Cybersecurity Through Comprehensive Investigation of Data Flow-Based Attack Scenarios
## 3 Proposed Approach
### Methodology
#### 3.2.3 Impact Enumeration
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multicolumn{1}{c}{**Property**} & \multicolumn{1}{c}{**Number of Scenarios**} & \multicolumn{1}{c}{**Most Frequent Initial Component**} & \multicolumn{1}{c}{**Most Frequent Final Component**} & \multicolumn{1}{c}{**Most Frequent Attack types(s)**} \\
**Scenarios** & & & **(\#)** & **(\#)** & **(\#)** \\ \hline Scenarios with 2 components & 64 & FleetController (25) & RemoteController (26) & Impersonate \\ Scenarios with 3 components & 540 & HX01 (168) & HX01 (180) & Impersonate \\ Scenarios with 4 components & 2208 & HX01 (1728) & SiteServer (2160) & Impersonate \\ Scenarios with 5 components & 288 & HX0 endoOperator (288) & SiteServer (288) & Impersonate \\ \hline Total & 3100 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Frequency and characteristics of attack scenarios. \begin{table}
\begin{tabular}{l l l l l l l l l l l l l l} \hline \hline \multicolumn{1}{c}{**Authors Types**} & \multicolumn{1}{c}{**Information**} & \multicolumn{1}{c}{**Authors Types**} & \multicolumn{1}{c}{**Usunmodified**} & \multicolumn{1}{c}{**Data Flow**} & \multicolumn{1}{c}{**Know of**} & \multicolumn{1}{c}{**Knowing**} & \multicolumn{1}{c}{**Knowing**} & \multicolumn{1}{c}{**Knowing**} & \multicolumn{1}{c}{**Knowing**} & \multicolumn{1}{c}{**Data Repe-**} & \multicolumn{1}{c}{**Data Repe-**} & \multicolumn{1}{c}{**Data Repe-**} \\
**Scenarios** & & & & & & & & & & & & & & \\ \hline Scenarios with 2 components & 5 & 6 & 5 & 5 & 5 & 5 & 8 & 5 & 5 & 5 & 5 & 5 & 64 \\ Scenarios with 3 components & 81 & 105 & 81 & 81 & 81 & 81 & 165 & 81 & 81 & 81 & 81 & 1000 \\ Scenarios with 4 components & 508 & 678 & 508 & 508 & 508 & 508 & 508 & 508 & 508 & 508 & 508 & 508 & 6424 \\ Scenarios with 5 components & 48 & 172 & 48 & 48 & 48 & 48 & 48 & 48 & 48 & 48 & 48 & 1132 \\ \hline Total & 642 & 909 & 642 & 642 & 642 & 642 & 1321 & 642 & 642 & 642 & 642 & 642 & 642 & 642 & 642 & 642 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Distribution of attack types based on scenario complexity. ### Comparison with Existing Methods

To contextualize our results within the existing literature, we compared our approach to several prominent cybersecurity methodologies, including both traditional and more recent approaches: _STRIDE_, _MITRE ATT&CK_[17], _PASTA_ (_Process for Attack Simulation and Threat Analysis_) [18], _OCTAVE Allegro_[19], _TARA_ (_Threat Assessment and Remediation Analysis_) [20], _FAIR_ (_Factor Analysis of Information Risk_) [21], and _NIST Cybersecurity Framework (CSF_) [22]. Table 4 provides a comparative analysis of these methodologies alongside our approach. Our approach has several advantages over existing methodologies. It provides comprehensive threat identification, similar to MITRE ATT&CK, PASTA, and TARA, surpassing the limitations of traditional STRIDE. A unique feature of our methodology is the full integration of data flow analysis, an aspect that is not fully addressed by most other approaches, including recent ones such as FAIR and NIST CSF. In terms of attack scenario mapping, our approach provides detailed insights comparable to MITRE ATT&CK and TARA, offering more depth than traditional methods and some newer frameworks such as FAIR. Our methodology is specifically tailored for IoT and industrial systems, another aspect that is not fully addressed by most other approaches, with TARA being a notable exception. We incorporate threat intelligence, a feature shared with newer methods such as MITER ATT&CK, PASTA, and NIST CSF. Our approach combines a risk-based methodology with impact quantification, similar to PASTA, OCTAVE Allegro, and FAIR but with a specific focus on IoT and industrial systems. Finally, similar to MITRE ATT&CK, TARA, and NIST CSF, our approach is designed to be continuously updated to ensure that it remains relevant in the face of evolving threats. These comprehensive features make our methodology a robust and adaptable solution to the unique challenges of IoT and industrial cybersecurity.