def minTree(inputArray, Tree=t.Node("root")):
if len(inputArray) > 0:
midpoint = len(inputArray) / 2
midvalue = inputArray[midpoint]
Tree.name = "Node"
Tree.value = midvalue
Tree.left = t.Node()
Tree.right = t.Node()
minTree(inputArray[0:midpoint], Tree.left)
minTree(inputArray[(midpoint + 1):], Tree.right)
return Tree
return
def __init__(self,grid,tol,mode,partition):
# The partitions are how we divide up the space and using a mixture of partitions
# or a naturally varying partition gives us more robust edge detection
acceptable_partitions= {'quad', 'shift_center', 'golden'}
if partition not in acceptable_partitions:
raise ValueError('Invalid Mode: please choose a valid mode {}'.format(acceptable_partitions.keys()))
#determine how large the image is
height, width, channels = grid.shape
#Channels are equal to depth
# starts with the whole image
self.RootNode = treeNode.Node(None,0,0,height, width, grid, tol, 0, mode, partition)
# save the image for later use
self.Grid = grid
self.Partition = partition
self.Matrix = np.zeros((height,width))
# add in the other three corners
#self.Edges.append([width, 0])
#self.Edges.append([width, -height])
#self.Edges.append([0, -height])
self.Cores = []
self.Edges = [] #starts with the corners included
self.Count = 0
def testModel(model, filename):
resetGlobalValues()
sniffTestPackets(filename)
entropyData = extractDNSQueryEntropy(entropyData, False)
buildTree(root, False)
subdomainCountData = calculateSubdomainCount(subdomainCountData, root,
False)
qnamesLen = calculateQnamesLen(qnamesLen, False)
testData = modifyData(entropyData, subdomainCountData, qnamesLen, False)
preds = model.predict(testData)
pktdump = scapy.utils.PcapWriter("results.pcap", append=True, sync=True)
print("Analysis Completed.")
print("Saving suspicious packets below to pcap file...(results.pcap)")
resultsTree = treeNode.Node('', '')
i = 0
while i < len(preds):
if preds[i] == -1:
pktdump.write(testPackets[i])
treeNode.populateTree(resultsTree, testPackets[i])
i += 1
print("Within the results, the breakdown of the domains are as such:")
print("Ignoring any domains with only 1 subdomain...")
resultsTree.printNodes()
current = root
s = []
done = 0
print('\n')
while not done:
#print("here:")
if current is not None:
s.append(current)
current = current.left
else:
if s:
current = s.pop()
print(current.val, end='')
current = current.right
else:
done = 1
if __name__ == '__main__':
ROOT = treeNode.Node(1)
ROOT.left = treeNode.Node(2)
ROOT.right = treeNode.Node(3)
ROOT.left.left = treeNode.Node(4)
ROOT.left.right = treeNode.Node(5)
ROOT.right.left = treeNode.Node(6)
ROOT.right.right = treeNode.Node(7)
treeNode.printTree(ROOT)
inorder(ROOT)
def resetGlobalValues():
root = treeNode.Node('', '')
entropyData = []
subdomainCountData = []
qnamesLen = []