commit 5940decb63c63e7b6fafa9bc37a3b8e2785cdd11
parent 41d189bbeed25afa32cdd153b9bd4728f1bec7d4
Author: Cody Lewis <codymlewis@protonmail.com>
Date: Tue, 16 Apr 2019 14:38:36 +1000
Added digraphing of recommendations and improve file neatness
Diffstat:
10 files changed, 74 insertions(+), 131 deletions(-)
diff --git a/.gitignore b/.gitignore
@@ -393,3 +393,6 @@ tags.lock
Session.vim
*.csv
*.pkl
+*.pdf
+*.gv
+*.h5
diff --git a/ANN.h5 b/ANN.h5
Binary files differ.
diff --git a/BadMouther/__init__.py b/BadMouther/__init__.py
@@ -13,6 +13,9 @@ class BadMouther(Node.Node):
A bad mouthing malicious node.
'''
def take_note(self, proxy, service_target, capability_target):
+ '''
+ Give a bad mouthed note.
+ '''
if proxy.is_malicious(): # say that other malicious nodes are good
return 1
return -1
diff --git a/Node/__init__.py b/Node/__init__.py
@@ -15,7 +15,7 @@ class Node:
'''
A node in the trust managed network.
'''
- def __init__(self, service=100, capability=100, is_malicious=False):
+ def __init__(self, service, capability, is_malicious=False):
self.__service = service
self.__capability = capability
self.__is_malicious = is_malicious
@@ -38,6 +38,9 @@ class Node:
return Report.Report(service_target, capability_target, note, time)
def take_note(self, proxy, service_target, capability_target):
+ '''
+ Take note of the service that the proxy provided.
+ '''
if proxy.is_malicious():
note = -1
elif proxy.get_service() >= service_target and \
diff --git a/TrustManager/ANN.py b/TrustManager/ANN.py
@@ -11,6 +11,9 @@ Date: 2019-04-12
def create_and_train_ann(train_data, train_labels, test_data, test_labels):
+ '''
+ Create a neural network and train it on the given data.
+ '''
model = keras.models.Sequential()
model.add(keras.layers.Dense(64, input_shape=(4,)))
model.add(keras.layers.Activation('relu'))
@@ -34,6 +37,9 @@ def create_and_train_ann(train_data, train_labels, test_data, test_labels):
def get_trusted_list(ann, client_id, service_target, capability_target, no_of_nodes):
+ '''
+ Get the list of nodes that client trusts for a given target service, and capability.
+ '''
trusted_list = dict()
for i in range(no_of_nodes):
@@ -43,5 +49,8 @@ def get_trusted_list(ann, client_id, service_target, capability_target, no_of_no
def unbinarize(arr):
+ '''
+ Convert a binarized array into the respective classes.
+ '''
value = list(arr)
return [-1, 0, 1][value.index(max(value))]
diff --git a/TrustManager/SVM.py b/TrustManager/SVM.py
@@ -32,6 +32,9 @@ def find_accuracy(svm, data, labels):
def get_trusted_list(svm, service_target, capability_target, no_of_nodes):
+ '''
+ Get the list of nodes that client trusts for a given target service, and capability.
+ '''
trusted_list = dict()
for i in range(no_of_nodes):
@@ -41,6 +44,9 @@ def get_trusted_list(svm, service_target, capability_target, no_of_nodes):
def evolve(train_inputs, train_labels, test_inputs, test_labels):
+ '''
+ Perform an evolutionary algorithm to optimize SVM parameters.
+ '''
genome = hill_climb(
train_inputs, train_labels, test_inputs, test_labels
)
@@ -48,10 +54,12 @@ def evolve(train_inputs, train_labels, test_inputs, test_labels):
def normalise_genome(genome):
+ '''
+ Make sure the genome does not have invalid input.
+ '''
for index_gene in enumerate(genome):
while genome[index_gene[0]] <= 0:
- genome[index_gene[0]] = \
- float(np.random.normal(10, 8, 1))
+ genome[index_gene[0]] = float(np.random.normal(10, 8, 1))
def generate_genome():
@@ -79,7 +87,7 @@ def mutate_genome(genome):
def hill_climb(train_inputs, train_labels, test_inputs, test_labels, acc_goal=99):
'''
- Evolutionary algorithm to find the optimal number of neurons for the ANN.
+ Evolutionary algorithm to find the optimal parameters for the SVM.
'''
counter = 0
n_epochs = 10_000
diff --git a/TrustManager/__init__.py b/TrustManager/__init__.py
@@ -1,3 +1,4 @@
+import os
import csv
import numpy as np
@@ -25,7 +26,7 @@ class TrustManager:
self.__train_filename = train_filename
self.__test_filename = test_filename
self.__use_svm = use_svm
- self.__predicter = None
+ self.__predictor = None
# A real trust model would not be aware of these lists
# these are for the training
constrained_list = Functions.get_conditioned_ids(
@@ -72,14 +73,15 @@ class TrustManager:
def get_no_of_nodes(self):
return len(self.__network)
- def reset_predicter(self):
- self.__predicter = None
+ def reset_predictor(self):
+ self.__predictor = None
def save(self):
- if self.__predicter:
- del self.__predicter
-
- joblib.dump(self, "trust_manager.pkl")
+ if self.__predictor:
+ del self.__predictor
+ if not os.path.exists("data"):
+ os.makedirs("data")
+ joblib.dump(self, "data/trust_manager.pkl")
def bootstrap(self, epochs=100, filewrite=True):
'''
@@ -128,7 +130,7 @@ class TrustManager:
def train(self):
'''
- Train the predicter.
+ Train the predictor.
'''
if self.__use_svm:
self.evolve_svm()
@@ -157,25 +159,32 @@ class TrustManager:
progress += 1
Functions.print_progress(progress, total_reporters)
- joblib.dump(svms, "SVMs.pkl")
+ if not os.path.exists("data"):
+ os.makedirs("data")
+ joblib.dump(svms, "data/SVMs.pkl")
print()
def train_ann(self):
+ '''
+ Train the artificial neural network.
+ '''
train_data, train_notes = read_data(self.__train_filename)
test_data, test_notes = read_data(self.__test_filename)
- ANN.create_and_train_ann(train_data, train_notes, test_data, test_notes).save("ANN.h5")
+ if not os.path.exists("data"):
+ os.makedirs("data")
+ ANN.create_and_train_ann(train_data, train_notes, test_data, test_notes).save("data/ANN.h5")
def load_svms(self):
'''
Load the kernel machine classifiers for each node in the network.
'''
- self.__predicter = joblib.load("SVMs.pkl")
+ self.__predictor = joblib.load("data/SVMs.pkl")
def load_ann(self):
'''
Load the neural network classifier.
'''
- self.__predicter = keras.models.load_model("ANN.h5")
+ self.__predictor = keras.models.load_model("data/ANN.h5")
def get_all_recommendations(self, service_target, capability_target):
trusted_lists = dict()
@@ -183,21 +192,24 @@ class TrustManager:
for client_id in range(no_of_nodes):
if self.__use_svm:
- if not self.__predicter:
+ if not self.__predictor:
self.load_svms()
trusted_lists[client_id] = SVM.get_trusted_list(
- self.__predicter[client_id], service_target, capability_target, no_of_nodes
+ self.__predictor[client_id], service_target, capability_target, no_of_nodes
)
else:
- if not self.__predicter:
+ if not self.__predictor:
self.load_ann()
trusted_lists[client_id] = ANN.get_trusted_list(
- self.__predicter, client_id, service_target, capability_target, no_of_nodes
+ self.__predictor, client_id, service_target, capability_target, no_of_nodes
)
return trusted_lists
def graph_recommendations(self, client_id, service_target, capability_target):
+ '''
+ Create a DiGraph of the recommendations for the client at the target service and capability.
+ '''
graph = graphviz.Digraph(comment="Recommendations DiGraph")
trusted_lists = self.get_all_recommendations(service_target, capability_target)
for node_id in range(self.get_no_of_nodes()):
@@ -214,31 +226,39 @@ class TrustManager:
f"{other_node_id}",
color="red" if trust_val == -1 else "purple" if trust_val == 0 else "blue"
)
- graph.render("recommendations.gv", view=False)
-
+ if not os.path.exists("graphs"):
+ os.makedirs("graphs")
+ predictor_name = "SVM" if self.__use_svm else "ANN"
+ graph.render(f"graphs/id{client_id}_s{service_target}_c{capability_target}_{predictor_name}_recommendations.gv", view=False)
def find_best_servers(self, client_id, service_target, capability_target):
+ '''
+ Give a list of trusted nodes for the client at the target service and capability.
+ '''
if self.__use_svm:
- if not self.__predicter:
+ if not self.__predictor:
self.load_svms()
trusted_list = SVM.get_trusted_list(
- self.__predicter[client_id], service_target, capability_target, len(self.__network)
+ self.__predictor[client_id], service_target, capability_target, len(self.__network)
)
else:
- if not self.__predicter:
+ if not self.__predictor:
self.load_ann()
trusted_list = ANN.get_trusted_list(
- self.__predicter, client_id, service_target, capability_target, len(self.__network)
+ self.__predictor, client_id, service_target, capability_target, len(self.__network)
)
return trusted_list
def load(train_filename, test_filename, use_svm):
- trust_manager = joblib.load("trust_manager.pkl")
+ '''
+ Load a previously saved trust manager.
+ '''
+ trust_manager = joblib.load("data/trust_manager.pkl")
trust_manager.set_filenames(train_filename, test_filename)
trust_manager.set_use_svm_flag(use_svm)
- trust_manager.reset_predicter()
+ trust_manager.reset_predictor()
return trust_manager
diff --git a/data/ANN.h5 b/data/ANN.h5
Binary files differ.
diff --git a/recommendations.gv b/recommendations.gv
@@ -1,103 +0,0 @@
-// Recommendations DiGraph
-digraph {
- 0 [label=0 color=blue fontcolor=white style=filled]
- 1 [label=1 color=blue fontcolor=white style=filled]
- 2 [label=2 color=blue fontcolor=white style=filled]
- 3 [label=3 color=blue fontcolor=white style=filled]
- 4 [label=4 color=blue fontcolor=white style=filled]
- 5 [label=5 color=blue fontcolor=white style=filled]
- 6 [label=6 color=blue fontcolor=white style=filled]
- 7 [label=7 color=red fontcolor=white style=filled]
- 8 [label=8 color=blue fontcolor=white style=filled]
- 9 [label=9 color=blue fontcolor=white style=filled]
- 10 [label=10 color=blue fontcolor=white style=filled]
- 11 [label=11 color=blue fontcolor=white style=filled]
- 12 [label=12 color=blue fontcolor=white style=filled]
- 13 [label=13 color=blue fontcolor=white style=filled]
- 14 [label=14 color=blue fontcolor=white style=filled]
- 15 [label=15 color=blue fontcolor=white style=filled]
- 16 [label=16 color=blue fontcolor=white style=filled]
- 17 [label=17 color=blue fontcolor=white style=filled]
- 18 [label=18 color=red fontcolor=white style=filled]
- 19 [label=19 color=blue fontcolor=white style=filled]
- 20 [label=20 color=blue fontcolor=white style=filled]
- 21 [label=21 color=red fontcolor=white style=filled]
- 22 [label=22 color=red fontcolor=white style=filled]
- 23 [label=23 color=blue fontcolor=white style=filled]
- 24 [label=24 color=blue fontcolor=white style=filled]
- 25 [label=25 color=blue fontcolor=white style=filled]
- 26 [label=26 color=blue fontcolor=white style=filled]
- 27 [label=27 color=blue fontcolor=white style=filled]
- 28 [label=28 color=blue fontcolor=white style=filled]
- 29 [label=29 color=blue fontcolor=white style=filled]
- 30 [label=30 color=blue fontcolor=white style=filled]
- 31 [label=31 color=blue fontcolor=white style=filled]
- 32 [label=32 color=blue fontcolor=white style=filled]
- 33 [label=33 color=blue fontcolor=white style=filled]
- 34 [label=34 color=blue fontcolor=white style=filled]
- 35 [label=35 color=blue fontcolor=white style=filled]
- 36 [label=36 color=blue fontcolor=white style=filled]
- 37 [label=37 color=blue fontcolor=white style=filled]
- 38 [label=38 color=blue fontcolor=white style=filled]
- 39 [label=39 color=blue fontcolor=white style=filled]
- 40 [label=40 color=blue fontcolor=white style=filled]
- 41 [label=41 color=blue fontcolor=white style=filled]
- 42 [label=42 color=blue fontcolor=white style=filled]
- 43 [label=43 color=blue fontcolor=white style=filled]
- 44 [label=44 color=blue fontcolor=white style=filled]
- 45 [label=45 color=blue fontcolor=white style=filled]
- 46 [label=46 color=red fontcolor=white style=filled]
- 47 [label=47 color=blue fontcolor=white style=filled]
- 48 [label=48 color=blue fontcolor=white style=filled]
- 49 [label=49 color=blue fontcolor=white style=filled]
- 2 -> 0 [color=red]
- 2 -> 1 [color=blue]
- 2 -> 2 [color=blue]
- 2 -> 3 [color=blue]
- 2 -> 4 [color=blue]
- 2 -> 5 [color=blue]
- 2 -> 6 [color=blue]
- 2 -> 7 [color=blue]
- 2 -> 8 [color=red]
- 2 -> 9 [color=purple]
- 2 -> 10 [color=red]
- 2 -> 11 [color=purple]
- 2 -> 12 [color=blue]
- 2 -> 13 [color=purple]
- 2 -> 14 [color=blue]
- 2 -> 15 [color=red]
- 2 -> 16 [color=red]
- 2 -> 17 [color=blue]
- 2 -> 18 [color=purple]
- 2 -> 19 [color=purple]
- 2 -> 20 [color=blue]
- 2 -> 21 [color=blue]
- 2 -> 22 [color=red]
- 2 -> 23 [color=blue]
- 2 -> 24 [color=red]
- 2 -> 25 [color=blue]
- 2 -> 26 [color=red]
- 2 -> 27 [color=blue]
- 2 -> 28 [color=red]
- 2 -> 29 [color=blue]
- 2 -> 30 [color=blue]
- 2 -> 31 [color=blue]
- 2 -> 32 [color=red]
- 2 -> 33 [color=red]
- 2 -> 34 [color=blue]
- 2 -> 35 [color=blue]
- 2 -> 36 [color=red]
- 2 -> 37 [color=red]
- 2 -> 38 [color=purple]
- 2 -> 39 [color=blue]
- 2 -> 40 [color=purple]
- 2 -> 41 [color=blue]
- 2 -> 42 [color=blue]
- 2 -> 43 [color=blue]
- 2 -> 44 [color=blue]
- 2 -> 45 [color=red]
- 2 -> 46 [color=blue]
- 2 -> 47 [color=purple]
- 2 -> 48 [color=purple]
- 2 -> 49 [color=blue]
-}
diff --git a/recommendations.gv.pdf b/recommendations.gv.pdf
Binary files differ.
