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remove magic strings

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Youngjoon Lee 2024-06-12 12:28:14 +09:00
parent 4cb76eac78
commit a2af026488
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1 changed files with 58 additions and 39 deletions
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97
mixnet/v2/sim/bulk_attack.py
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@ -8,6 +8,21 @@ from analysis import Analysis
from config import Config, P2PConfig
from simulation import Simulation
COL_P2P_TYPE = "p2p_type"
COL_NUM_MIX_LAYERS = "num_mix_layers"
COL_COVER_MESSAGE_PROB = "cover_message_prob"
COL_MIX_DELAY = "mix_delay"
COL_GLOBAL_PRECISION = "global_precision"
COL_GLOBAL_RECALL = "global_recall"
COL_GLOBAL_F1_SCORE = "global_f1_score"
COL_TARGET_ACCURACY_MEDIAN = "target_accuracy_median"
COL_TARGET_ACCURACY_STD = "target_accuracy_std"
COL_TARGET_ACCURACY_MIN = "target_accuracy_min"
COL_TARGET_ACCURACY_25p = "target_accuracy_25p"
COL_TARGET_ACCURACY_MEAN = "target_accuracy_mean"
COL_TARGET_ACCURACY_75p = "target_accuracy_75p"
COL_TARGET_ACCURACY_MAX = "target_accuracy_max"
def bulk_attack():
parser = argparse.ArgumentParser(description="Run multiple passive adversary attack simulations",
@ -33,10 +48,10 @@ def bulk_attack():
for p2p_type in [P2PConfig.TYPE_ONE_TO_ALL, P2PConfig.TYPE_GOSSIP]:
config.p2p.type = p2p_type
for num_mix_layers in [0, 1, 2, 3, 4]:
for num_mix_layers in [0, 1, 2, 3]:
config.mixnet.num_mix_layers = num_mix_layers
for cover_message_prob in [0.0, 0.1, 0.2, 0.3, 0.4]:
for cover_message_prob in [0.0, 0.1, 0.2, 0.3]:
config.mixnet.cover_message_prob = cover_message_prob
for mix_delay in [0]:
@ -49,81 +64,85 @@ def bulk_attack():
analysis = Analysis(sim, config, show_plots=False)
precision, recall, f1_score = analysis.messages_emitted_around_interval()
print(
f"ANALYZING TIMING ATTACK: p2p_type:{p2p_type}, {num_mix_layers} layers, {cover_message_prob} cover, {mix_delay} delay")
f"STARTING TIMING ATTACK: p2p_type:{p2p_type}, {num_mix_layers} layers, {cover_message_prob} cover, {mix_delay} delay")
timing_attack_df = analysis.timing_attack(analysis.message_hops())
results.append({
"p2p_type": p2p_type,
"num_mix_layers": num_mix_layers,
"cover_message_prob": cover_message_prob,
"mix_delay": mix_delay,
"global_precision": precision,
"global_recall": recall,
"global_f1_score": f1_score,
"target_median": float(timing_attack_df.median().iloc[0]),
"target_std": float(timing_attack_df.std().iloc[0]),
"target_min": float(timing_attack_df.min().iloc[0]),
"target_25%": float(timing_attack_df.quantile(0.25).iloc[0]),
"target_mean": float(timing_attack_df.mean().iloc[0]),
"target_75%": float(timing_attack_df.quantile(0.75).iloc[0]),
"target_max": float(timing_attack_df.max().iloc[0]),
COL_P2P_TYPE: p2p_type,
COL_NUM_MIX_LAYERS: num_mix_layers,
COL_COVER_MESSAGE_PROB: cover_message_prob,
COL_MIX_DELAY: mix_delay,
COL_GLOBAL_PRECISION: precision,
COL_GLOBAL_RECALL: recall,
COL_GLOBAL_F1_SCORE: f1_score,
COL_TARGET_ACCURACY_MEDIAN: float(timing_attack_df.median().iloc[0]),
COL_TARGET_ACCURACY_STD: float(timing_attack_df.std().iloc[0]),
COL_TARGET_ACCURACY_MIN: float(timing_attack_df.min().iloc[0]),
COL_TARGET_ACCURACY_25p: float(timing_attack_df.quantile(0.25).iloc[0]),
COL_TARGET_ACCURACY_MEAN: float(timing_attack_df.mean().iloc[0]),
COL_TARGET_ACCURACY_75p: float(timing_attack_df.quantile(0.75).iloc[0]),
COL_TARGET_ACCURACY_MAX: float(timing_attack_df.max().iloc[0]),
})
df = pd.DataFrame(results)
df.to_csv(f"bulk-attack-{datetime.now().replace(microsecond=0).isoformat()}.csv", index=False)
plot_global_metrics(df)
plot_target_metrics(df)
plot_target_accuracy(df)
def plot_global_metrics(df: pd.DataFrame):
for p2p_type in df["p2p_type"].unique():
for p2p_type in df[COL_P2P_TYPE].unique():
# Plotting global precision, recall, and f1 score against different parameters
fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(10, 15))
# Precision plot
for cover_message_prob in df["cover_message_prob"].unique():
subset = df[(df["cover_message_prob"] == cover_message_prob) & (df["p2p_type"] == p2p_type)]
axes[0].plot(subset["num_mix_layers"], subset["global_precision"], label=f"{cover_message_prob} cover rate")
for cover_message_prob in df[COL_COVER_MESSAGE_PROB].unique():
subset = df[(df[COL_COVER_MESSAGE_PROB] == cover_message_prob) & (df[COL_P2P_TYPE] == p2p_type)]
axes[0].plot(subset[COL_NUM_MIX_LAYERS], subset[COL_GLOBAL_PRECISION],
label=f"{cover_message_prob} cover rate")
axes[0].set_title(f"Global Precision ({p2p_type})")
axes[0].set_xlabel("# of Mix Layers")
axes[0].set_ylabel("Global Precision (%)")
axes[0].set_ylim(0, 100)
axes[0].set_ylim(0, 105)
axes[0].legend()
# Recall plot
for cover_message_prob in df["cover_message_prob"].unique():
subset = df[(df["cover_message_prob"] == cover_message_prob) & (df["p2p_type"] == p2p_type)]
axes[1].plot(subset["num_mix_layers"], subset["global_recall"], label=f"{cover_message_prob} cover rate")
for cover_message_prob in df[COL_COVER_MESSAGE_PROB].unique():
subset = df[(df[COL_COVER_MESSAGE_PROB] == cover_message_prob) & (df[COL_P2P_TYPE] == p2p_type)]
axes[1].plot(subset[COL_NUM_MIX_LAYERS], subset[COL_GLOBAL_RECALL],
label=f"{cover_message_prob} cover rate")
axes[1].set_title(f"Global Recall ({p2p_type})")
axes[1].set_xlabel("# of Mix Layers")
axes[1].set_ylabel("Global Recall (%)")
axes[1].set_ylim(0, 100)
axes[1].set_ylim(0, 105)
axes[1].legend()
# F1 Score plot
for cover_message_prob in df["cover_message_prob"].unique():
subset = df[(df["cover_message_prob"] == cover_message_prob) & (df["p2p_type"] == p2p_type)]
axes[2].plot(subset["num_mix_layers"], subset["global_f1_score"], label=f"{cover_message_prob} cover rate")
for cover_message_prob in df[COL_COVER_MESSAGE_PROB].unique():
subset = df[(df[COL_COVER_MESSAGE_PROB] == cover_message_prob) & (df[COL_P2P_TYPE] == p2p_type)]
axes[2].plot(subset[COL_NUM_MIX_LAYERS], subset[COL_GLOBAL_F1_SCORE],
label=f"{cover_message_prob} cover rate")
axes[2].set_title(f"Global F1 Score ({p2p_type})")
axes[2].set_xlabel("# of Mix Layers")
axes[2].set_ylabel("Global F1 Score (%)")
axes[2].set_ylim(0, 100)
axes[2].set_ylim(0, 105)
axes[2].legend()
plt.tight_layout()
plt.show()
def plot_target_metrics(df: pd.DataFrame):
for p2p_type in df["p2p_type"].unique():
def plot_target_accuracy(df: pd.DataFrame):
for p2p_type in df[COL_P2P_TYPE].unique():
plt.figure(figsize=(12, 6))
for cover_message_prob in df["cover_message_prob"].unique():
subset = df[(df["cover_message_prob"] == cover_message_prob) & (df["p2p_type"] == p2p_type)]
plt.plot(subset["num_mix_layers"], subset["target_median"], label=f"{cover_message_prob} cover rate")
plt.title(f"Timing Attack Success Rate ({p2p_type})")
for cover_message_prob in df[COL_COVER_MESSAGE_PROB].unique():
subset = df[(df[COL_COVER_MESSAGE_PROB] == cover_message_prob) & (df[COL_P2P_TYPE] == p2p_type)]
plt.plot(subset[COL_NUM_MIX_LAYERS], subset[COL_TARGET_ACCURACY_MEDIAN],
label=f"{cover_message_prob} cover rate")
plt.title(f"Timing Attack Accuracy ({p2p_type})")
plt.xlabel("# of Mix Layers")
plt.ylabel("Median of Success Rates (%)")
plt.ylim(0, 100)
plt.ylabel("Median of Accuracy (%)")
plt.ylim(0, 105)
plt.legend()
plt.tight_layout()
plt.show()