diff --git a/ERFormatConstants.py b/ERFormatConstants.py
index a53c47ac44c34440b28ba3897f2d794d2e851fc4..1d214bebb3943b68416cee7cdcdd516e5d7c7a43 100644
--- a/ERFormatConstants.py
+++ b/ERFormatConstants.py
@@ -2,6 +2,7 @@
#Generic fields for all rows in the CSV file
Id = "Id" # The id of the entity
+Name = "Name" # ! Identifier, can be used to distinguish diagram components
textArea1 = "Text Area 1"
textArea2 = "Text Area 2"
@@ -76,3 +77,12 @@ ConsequenceDynamic = "Consequence" # The consequence dynamics
AttackDynamic = "Attack" # The attack dynamics
ERDynamic = "ER" # The ER dynamics
BowtieDynamicType = textArea3 # The bowtie dynamics
+
+# ! Note all metric fields use ID and name as the fields for the metric
+# ! The ID will be used to link them in the dynamic matrix
+
+
+# Lines
+From = "Line Source"
+To = "Line Destination"
+
diff --git a/diagramParser.py b/diagramParser.py
index ca12b16c570683604d74cd1d26f3dcbd9c19d6b6..4b853302b89d322e5db0f6bfe838220fbac84d9d 100644
--- a/diagramParser.py
+++ b/diagramParser.py
@@ -17,12 +17,15 @@ def parseDiagramFile(csvFile):
consequences = {}
#consequences = parseConsequences(df, consequences)
+ # ER components
+ erComponents = {}
+
+
#List containing all metrics
metrics = []
#List containing all attacks
attacks = []
-
parseDynamic(df, metrics)
@@ -67,7 +70,7 @@ def parseAttacks(df, attackDict):
)
attackDict[df.Id[i]] = attack
- return attackDict
+ return attackDict
#Parses metrics components and adds it to list
def parseDynamic(df, metricList):
@@ -81,8 +84,8 @@ def parseDynamic(df, metricList):
)
threatDynamic.metrics = extractMetrics(df, i, 4) # Extracts metrics from the dynamic component
joinMetrcs(threatDynamic.metrics, metricList) # Adds the metrics to the global metric list
-
-
+
+ threatDynamic.associateBowtie(df, threatDynamic.type) # Associate the dynamic with the correct components
elif df[const.textArea3][i] == const.ConsequenceDynamic:
consequenceDynamic = dynamic.BowtieDynamic(
@@ -91,6 +94,7 @@ def parseDynamic(df, metricList):
)
consequenceDynamic.metrics = extractMetrics(df, i, 4) # Extracts metrics from the dynamic component
joinMetrcs(threatDynamic.metrics, metricList) # Adds the metrics to the global metric list
+ consequenceDynamic.associateBowtie(df, consequenceDynamic.type) # Associate the dynamic with the correct components
elif df[const.textArea3][i] == const.AttackDynamic:
@@ -100,15 +104,18 @@ def parseDynamic(df, metricList):
)
attackDynamic.metrics = extractMetrics(df, i, 4)
joinMetrcs(threatDynamic.metrics, metricList) # Adds the metrics to the global metric list
+ attackDynamic.associateBowtie(df,attackDynamic.type) # Associate the dynamic with the correct components
elif df[const.textArea3][i] == const.ERDynamic:
erDynamic = dynamic.ERDynamic(
- df[const.id][i], # Component ID LucidChart
- df.textArea3[i], # Name of dynamic
+ df[const.Id][i], # Component ID LucidChart
+ df[const.textArea3][i], # Component type
+ df[const.textArea5][i] # Description
)
erDynamic.metrics = extractMetrics(df, i, 8)
joinMetrcs(threatDynamic.metrics, metricList) # Adds the metrics to the global metric list
+ #erDynamic.associatedERComponents(df)
def extractMetrics(df, index, startRange):
@@ -117,8 +124,8 @@ def extractMetrics(df, index, startRange):
metricID = "Text Area "+str(j)
metricName = "Text Area "+str(j+1)
- if pd.isnull(df[metric][index]) == False: # If the text area is not empty
- print("Metric: ID", df[metricID][index], "Name: ", df[metricName][index])
+ if pd.isnull(df[metricID][index]) == False: # If the text area is not empty
+ log.info("Metric: ID", df[metricID][index], "Name: ", df[metricName][index])
metric = dynamic.Metric(df[metricID][index], df[metricName][index])
listOfMetrics.append(metric)
else:
@@ -133,8 +140,8 @@ def extractMetrics(df, index, startRange):
"""
def joinMetrcs(localMetrics, globalMetrics):
duplicateMetrics = 0 # Counter for duplicate metrics per function run
- for i in range(globalMetrics):
- for j in range(localMetrics):
+ for i in range(len(globalMetrics)):
+ for j in range(len(localMetrics)):
if globalMetrics[i].name == localMetrics[j].name: # Local metric already exists in the global metric list
duplicateMetrics += 1
break # Check next entry
diff --git a/documentation/requirements.txt b/documentation/requirements.txt
index 2e7582422e7b85c961cfd87ef17665225848c56c..1c782dcbaa11eb9a14613c59c1a8586c5a753ccb 100644
--- a/documentation/requirements.txt
+++ b/documentation/requirements.txt
@@ -1,7 +1,7 @@
Har komponenter
Lagt til lokale metrics i hver komponent
-1. Må nå lage en global metric liste
-2. Må lage ER dynamikk liste
+1. Må nå lage en global metric liste FERDIG
+2. Må lage ER dynamikk liste
3. Må koble dynamic komponenter til trusselkomponent
diff --git a/dynamics.py b/dynamics.py
index dcaf9313c96fc20ea9a952d7927d41d2b3d61e2f..b77cc346ad4dd0ec115c87f7fff653d3afbbac12 100644
--- a/dynamics.py
+++ b/dynamics.py
@@ -1,3 +1,4 @@
+import ERFormatConstants as const
import logging as log
# Dynamics class parent class for both the bowtie dynamics and the architecture dynamics
@@ -13,30 +14,6 @@ class DynamicComponent:
def __str__(self) -> str:
return f"Dynamic Component: {self.componentID}, {len(self.metrics)}"
- #Simple contains function to check if metric already exists
- def containsMetric(metricList, metric):
- if len(metricList)==0:
- return False
- for i in metricList:
- if i.name == metric:
- return True
- return False
-
- # Function used to add a metric to the dynamics list
- # The function will check if the metric already exists in the global list, if it does it will not be added to the global list
- def addMetrics(self, globalMetricList , newMetric, description, metricType ):
- metric = Metric(newMetric, description, metricType)
-
- #Check if metric is already in the global metrics list if it is the metric will not be added to the global list
- if DynamicComponent.containsMetric(globalMetricList, newMetric) == False:
- globalMetricList = globalMetricList.append(metric)
- log.info(f"Added metric {newMetric} to GLOBAL dynamics list with ID: {self.id}")
- else:
- log.info(f"Metric {newMetric} already exists in GLOBAL dynamics list with ID: {self.id}")
- self.metrics.append(metric)
- log.info(f"Added metric {newMetric} to LOCAL dynamics list with ID: {self.id}")
-
-
#Metric class, will be used for all dynamics
class Metric:
def __init__(self, ID, name) -> None:
@@ -45,7 +22,7 @@ class Metric:
self.ID = ID # ID of the metric used to locate in dynamics matrics
self.name = name # Name of the metric
-
+
# Metrics found in the metric table
@@ -57,17 +34,112 @@ class BowtieDynamic(DynamicComponent):
def __init__(self, componentID, type) -> None:
super().__init__(componentID)
- self.type = type # Type of dynamicM
- self.associatedThreat = None # List of threats associated with the dynamic
- self.associatedAttack = None # List of attacks associated with the dynamic
+ self.type = type # Type of dynamicM
+ self.associatedThreat = None # Threat associated with the dynamic
+ self.associatedAttack = None # Attack associated with the dynamic
+ self.associatedConsequence = None # Consequence associated with the dynamic
+
+ """_summary_
+ Handles the associated dynamics for the bowtie model as they are different from the ER model
+ ER model has one type of dynamic component with metrics however, the bowtie model has three dynamic types and needs more parsing
+ than the ER model du to the modeling annotation.
+ Abstracting this saves time
+ """
+ def associateBowtie(self, df, componentType ):
+ componentId = self.componentID # Define the ID of the component
+ #Find the lines associated with the component
+ lines = df.loc[df['Name']=='Line'] # Find all lines in the diagram
+
+ lineSource = lines.loc[lines[const.From] == componentId] # Find the line where the component is the source
+ lineDestination = lines.loc[lines[const.To] == componentId] # ! The component SHOULD be source however, to error handle we need to check for destination as well
+
+ if lineSource.isnull().values.any() == True:
+ lineOne = lineSource
+ else:
+ lineOne = lineDestination
+
+ if lineOne[const.From].item() == componentId: # If the component is the source
+ lineTwo = df.loc[df[const.Id] == lineOne[const.To].item()] # Find the component associated with the line
+ else:
+ lineTwo = df.loc[df[const.Id] == lineOne[const.From].item()] # Find the component associated with the line
+
+ # ! Edge case, if the second line is in fact not a second line it is an attack meaning that the dynamic component is an attack
+ if componentType == const.Attack: # If the associated component is an attack
+ self.associatedAttack = lineTwo # ! The "line" is an attack, not a line, we add it to the associated attack field and move on
+ return # The attack is the only component associated with the dynamic
+
+
+ sourceComponent = df.loc[df[const.Id]==lineTwo[const.From].item()]
+ destinationComponent = df.loc[df[const.Id]==lineTwo[const.To].item()]
+
+
+
+ if componentType == const.ThreatDynamic: # If the component is a threat
+ if sourceComponent[const.textArea1].item() == const.Threat: # Checks if source or destination is the threat
+ self.associatedThreat = sourceComponent
+ self.associatedAttack = destinationComponent
+ else:
+ self.associatedThreat = destinationComponent
+ self.associatedAttack = sourceComponent
+
+ elif componentType == const.ConsequenceDynamic: # If the component is a consequence
+ if sourceComponent[const.textArea1].item() == const.Consequence:
+ self.associatedConsequence = sourceComponent
+ self.associatedAttack = destinationComponent
+ else:
+ self.associatedConsequence = destinationComponent
+ self.associatedAttack = sourceComponent
+
+
+
+
+
+
+
+
+
+
class ERDynamic(DynamicComponent):
def __init__(self, componentID, type, description) -> None:
- super().__init__(componentID, type)
+ super().__init__(componentID)
self.description = description # Description of the dynamic
- self.associatedComponent = None # Associated ER component
+ self.associatedERComponents = [] # Associated ER component
+ self.type = type
-
\ No newline at end of file
+ """_summary_
+ Function will use the dynamic component ID to find linked threats, ER components, attacks and consequences
+ This will be needed to associate the dynamics with the correct components and analyze relationships
+ """
+ def associatER(self, df):
+ componentId = self.componentID # Define the ID of the component
+
+ #Find the lines associated with the component
+ lines = df.loc[df['Name']=='Line'] # Find all lines in the diagram
+ relevantLines = [] # List of lines relevant to the component
+ for i in range(len(lines)):
+
+ line = lines.iloc[i]
+
+ if line[const.From] == componentId or line[const.To] == componentId: # If the line is associated with the component
+ relevantLines.append(lines.iloc[i]) # Add the line to the relevant lines list
+ log.info("Amount of relevant lines found:", len(relevantLines))
+
+ for i in range(len(relevantLines)):
+ line = relevantLines[i]
+ lineSource = line[const.From] # The source of the line
+ lineDestination = line[const.To]
+
+ if lineSource == componentId: # If the line is associated with the component
+ associatedComponentID = lineDestination
+ else:
+ associatedComponentID = lineSource
+ associatedComponent = df.iloc[df.index[df[const.Id] == associatedComponentID]] # Find the component associated with the line
+
+ self.associatedERComponents.append(associatedComponent) # Add the associated component to the list of associated components
+ log.info("Associated components found: ", self.associatedERComponents)
+ return
+
\ No newline at end of file