# Enhancing Cybersecurity Through Comprehensive Investigation of Data Flow-Based Attack Scenarios
## 3 Proposed Approach
### Methodology
#### 3.2.3 Impact Enumeration
With _sdList_ in hand, we proceed to identify the source and destination using the description of each threat by going through the _sdList_ category-by-category, starting with _Spoofing_ and moving on to _Denial of Service_, _Elevation of Privilege_, _Tampering_, _Repudiation_, and _Information Disclosure_. In the case of a redundant interaction, the source and destination are identified from the description and _sdList_ is used for a unique interaction, as shown in Algorithm 2, lines 7-20. ```
1:\(aList\)
2:\(dfd\)
3:Create \(sdList\)\(\triangleright\) Source, destination list for interactions
4:\(sdList=\{Intr,Src,Dst,IFlag\}\)
5:\(a[\text{IFlag}]\leftarrow\text{groupby}(a[\text{Int}])\)\(\triangleright\) Count redundancy of Interactions
6:repeat
7:for each\(a\in aList\)do
8:if\(a[IFlag]\geq 2\)then
9:\(a[aSrc]\gets extractfrom(a[tDes])\)
10:\(a[aDst]\gets extractfrom(a[tDes])\)
11:else
12:repeat
13:for each\(sd\in sdList\)do
14:if\(a[\text{tint}]==sd[\text{Intr}]\)then
15:\(a[aSrc]\gets extractfrom(sd[Src])\)
16:\(a[aDst]\gets extractfrom(a[Dst])\)
17:break
18:endif
19:endfor
20:until\(EoF\)
21:endif
22:endfor
23:until\(EoF\)
```

**Algorithm 2** Identify the source and destination of each threat

At this point, the attack profile in _aList_ is ready to be used in calculating the paths and scenarios. The longer the path (steps), the greater the depth. #### 3.2.4 Attack Surface Detection

Given that an attack surface encompasses all potential entry points exploitable for malicious activities in a system, our approach leverages the _aList_ to comprehensively identify these entry points. By aggregating all sources \(a[aSrc]\) within _aList_, we obtain a holistic view of the entire attack surface inherent in the designed system. This enables a detailed analysis of system vulnerabilities associated with each threat. For instance, examination of _aList_ reveals that the _FelectController_ component is susceptible to various threats, including _Elevationof Privilege, Denial of Service, Spoofing, and Information Disclosure. Consequently, the system is rendered vulnerable to potential adversities such as _Impersonation_, _Crashes_, _Unauthorized Access_, and _Data Flow Sniffing_, illustrating the multifaceted nature of the identified threats. #### 3.2.5 Attack Scenario API

The Attack Scenario API is created based on the attack profile, and displays all potential attack scenarios from each attack surface. This provides valuable information on the potential depth of impact and the number of actors within the system that could be affected in the event of a successful attack. The idea is to take the sources as a starting point and proceed with their corresponding destinations. The destination in the next step becomes the source in the next step; thus, its destination is the source for the next step, and so forth. After the \(aList\) has been computed, we propose an automated approach to compute each path. Each path is composed of one or more components in succession. A threat generated from a source is a provision to reach the other node directly connected to it. Here, the scenario is a combination of multiple attack paths, where each path is a combination of three components, i.e., source, interaction, and destination. The prerequisite for using the API is Python (Python 3 or later), which is usually installed in many operating systems, and can be installed easily. The API requires three arguments: the threat list, DFD, and our customized tool for parsing the graphical DFD, such that:

python STRIDEThreatAttack.py inputFile SourceDestinationFilename outputFile

Here, \(inputFile\) and \(SourceDestinationFilename\) should be provided with the extension and full path if they are not in the current folder. In addition, \(outputFile\) should be provided without extension but with the full path if it is not present in the current folder. The source code of the API can be viewed at [[https://t.ly/Rdfvr](https://t.ly/Rdfvr)]([https://t.ly/Rdfvr](https://t.ly/Rdfvr)) (accessed on 30 September 2024).