mirror of
https://github.com/logos-blockchain/logos-blockchain-specs.git
synced 2026-01-23 15:33:06 +00:00
289 lines
12 KiB
Python
289 lines
12 KiB
Python
from collections import Counter
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from typing import TYPE_CHECKING
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import numpy as np
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import pandas as pd
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import scipy.stats as stats
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import seaborn
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from matplotlib import pyplot as plt
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from adversary import NodeState
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from config import Config
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from simulation import Simulation
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if TYPE_CHECKING:
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from node import Node
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COL_TIME = "Time"
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COL_NODE_ID = "Node ID"
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COL_MSG_CNT = "Message Count"
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COL_SENDER_CNT = "Sender Count"
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COL_NODE_STATE = "Node State"
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COL_HOPS = "Hops"
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COL_EXPECTED = "Expected"
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COL_MSG_SIZE = "Message Size"
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COL_EGRESS = "Egress"
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COL_INGRESS = "Ingress"
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class Analysis:
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def __init__(self, sim: Simulation, config: Config):
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self.sim = sim
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self.config = config
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def run(self):
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message_size_df = self.message_size_distribution()
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self.bandwidth(message_size_df)
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self.messages_emitted_around_interval()
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self.messages_in_node_over_time()
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# self.node_states()
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median_hops = self.message_hops()
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self.timing_attack(median_hops)
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def bandwidth(self, message_size_df: pd.DataFrame):
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dataframes = []
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nonzero_egresses = []
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nonzero_ingresses = []
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for egress_bandwidths, ingress_bandwidths in zip(self.sim.p2p.measurement.egress_bandwidth_per_time,
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self.sim.p2p.measurement.ingress_bandwidth_per_time):
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rows = []
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for node in self.sim.p2p.nodes:
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egress = egress_bandwidths[node] / 1024.0
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ingress = ingress_bandwidths[node] / 1024.0
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rows.append((node.id, egress, ingress))
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if egress > 0:
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nonzero_egresses.append(egress)
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if ingress > 0:
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nonzero_ingresses.append(ingress)
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df = pd.DataFrame(rows, columns=[COL_NODE_ID, COL_EGRESS, COL_INGRESS])
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dataframes.append(df)
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times = range(len(dataframes))
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df = pd.concat([df.assign(Time=time) for df, time in zip(dataframes, times)], ignore_index=True)
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df = df.pivot(index=COL_TIME, columns=COL_NODE_ID, values=[COL_EGRESS, COL_INGRESS])
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plt.figure(figsize=(12, 6))
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for column in df.columns:
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marker = "x" if column[0] == COL_INGRESS else "o"
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plt.plot(df.index, df[column], marker=marker, label=column[0])
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plt.title("Egress/ingress bandwidth of each node over time")
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plt.xlabel(COL_TIME)
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plt.ylabel("Bandwidth (KiB/s)")
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plt.ylim(bottom=0)
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# Customize the legend to show only 'egress' and 'ingress' regardless of node_id
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handles, labels = plt.gca().get_legend_handles_labels()
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by_label = dict(zip(labels, handles))
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plt.legend(by_label.values(), by_label.keys())
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plt.grid(True)
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# Adding descriptions on the right size of the plot
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egress_series = pd.Series(nonzero_egresses)
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ingress_series = pd.Series(nonzero_ingresses)
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desc = (
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f"message: {message_size_df[COL_MSG_SIZE].mean():.0f} bytes\n"
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f"{self.config.description()}\n\n"
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f"[egress(>0)]\nmean: {egress_series.mean():.2f} KiB/s\nmax: {egress_series.max():.2f} KiB/s\n\n"
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f"[ingress(>0)]\nmean: {ingress_series.mean():.2f} KiB/s\nmax: {ingress_series.max():.2f} KiB/s"
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)
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plt.text(1.02, 0.5, desc, transform=plt.gca().transAxes, verticalalignment="center", fontsize=12)
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plt.subplots_adjust(right=0.8) # Adjust layout to make room for the text
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plt.show()
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def message_size_distribution(self) -> pd.DataFrame:
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df = pd.DataFrame(self.sim.p2p.adversary.message_sizes, columns=[COL_MSG_SIZE])
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print(df.describe())
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return df
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def messages_emitted_around_interval(self):
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# A ground truth that shows how many times each node sent a real message
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truth_df = pd.DataFrame(
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[(node.id, count) for node, count in self.sim.p2p.measurement.original_senders.items()],
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columns=[COL_NODE_ID, COL_MSG_CNT])
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# A result of observing nodes who have sent messages around the promised message interval
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suspected_df = pd.DataFrame(
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[(node.id, self.sim.p2p.adversary.senders_around_interval[node]) for node in
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self.sim.p2p.measurement.original_senders.keys()],
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columns=[COL_NODE_ID, COL_MSG_CNT]
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)
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width = 0.4
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fig, ax = plt.subplots(figsize=(12, 8))
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ax.bar(truth_df[COL_NODE_ID] - width / 2, truth_df[COL_MSG_CNT], width, label="Ground Truth", color="b")
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ax.bar(truth_df[COL_NODE_ID] + width / 2, suspected_df[COL_MSG_CNT], width, label="Adversary's Inference",
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color="r")
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ax.set_title("Nodes who generated real messages")
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ax.set_xlabel(COL_NODE_ID)
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ax.set_ylabel(COL_MSG_CNT)
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ax.set_xlim(-1, len(truth_df[COL_NODE_ID]))
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ax.legend()
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plt.tight_layout()
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plt.show()
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def messages_in_node_over_time(self):
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dataframes = []
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for window, msg_pools in enumerate(self.sim.p2p.adversary.msg_pools_per_window):
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time = window * self.config.adversary.window_size
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data = []
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for receiver, msg_pool in msg_pools.items():
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senders = self.sim.p2p.adversary.msgs_received_per_window[window][receiver]
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data.append((time, receiver.id, len(msg_pool), len(senders)))
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df = pd.DataFrame(data, columns=[COL_TIME, COL_NODE_ID, COL_MSG_CNT, COL_SENDER_CNT])
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if not df.empty:
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dataframes.append(df)
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df = pd.concat(dataframes, ignore_index=True)
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msg_cnt_df = df.pivot(index=COL_TIME, columns=COL_NODE_ID, values=COL_MSG_CNT)
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plt.figure(figsize=(12, 6))
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for column in msg_cnt_df.columns:
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plt.plot(msg_cnt_df.index, msg_cnt_df[column], marker=None, label=column)
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plt.title("Messages within each node over time")
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plt.xlabel(COL_TIME)
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plt.ylabel(COL_MSG_CNT)
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plt.ylim(bottom=0)
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plt.grid(True)
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plt.tight_layout()
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plt.show()
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sender_cnt_df = df.pivot(index=COL_TIME, columns=COL_NODE_ID, values=COL_SENDER_CNT)
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plt.figure(figsize=(12, 6))
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for column in sender_cnt_df.columns:
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plt.plot(sender_cnt_df.index, sender_cnt_df[column], marker=None, label=column)
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plt.title("Diversity of senders of messages received by each node over time")
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plt.xlabel(COL_TIME)
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plt.ylabel("# of senders of messages received by each node")
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plt.ylim(bottom=0)
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plt.grid(True)
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plt.tight_layout()
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plt.show()
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plt.figure(figsize=(12, 6))
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df.boxplot(column=COL_SENDER_CNT, by=COL_TIME, medianprops={"color": "red", "linewidth": 2.5})
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plt.title("Diversity of senders of messages received by each node over time")
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plt.suptitle("")
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plt.xticks([])
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plt.xlabel(COL_TIME)
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plt.ylabel("# of senders of messages received by each node")
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plt.ylim(bottom=0)
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plt.grid(axis="x")
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plt.tight_layout()
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plt.show()
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def node_states(self):
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rows = []
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for time, node_states in self.sim.p2p.adversary.node_states.items():
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for node, state in node_states.items():
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rows.append((time, node.id, state))
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df = pd.DataFrame(rows, columns=[COL_TIME, COL_NODE_ID, COL_NODE_STATE])
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plt.figure(figsize=(10, 6))
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seaborn.scatterplot(data=df, x=COL_TIME, y=COL_NODE_ID, hue=COL_NODE_STATE,
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palette={NodeState.SENDING: "red", NodeState.RECEIVING: "blue"})
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plt.title("Node states over time")
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plt.xlabel(COL_TIME)
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plt.ylabel(COL_NODE_ID)
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plt.legend(title=COL_NODE_STATE)
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plt.show()
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def message_hops(self) -> int:
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df = pd.DataFrame(self.sim.p2p.measurement.message_hops.values(), columns=[COL_HOPS])
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print(df.describe())
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plt.figure(figsize=(6, 6))
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seaborn.boxplot(data=df, y=COL_HOPS, medianprops={"color": "red", "linewidth": 2.5})
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plt.ylim(bottom=0)
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plt.title("Message hops distribution")
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plt.show()
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return int(df.median().iloc[0])
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def timing_attack(self, hops_between_layers: int):
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hops_to_observe = hops_between_layers * (self.config.mixnet.num_mix_layers + 1)
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suspected_senders = Counter()
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for receiver, windows_and_senders in self.sim.p2p.adversary.final_msgs_received.items():
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for window, sender in windows_and_senders.items():
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suspected_senders.update(self.timing_attack_with(receiver, window, hops_to_observe, sender))
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suspected_senders = ({node.id: count for node, count in suspected_senders.items()})
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print(f"suspected nodes count: {len(suspected_senders)}")
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# Create the bar plot for original sender counts
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original_senders = ({node.id: count for node, count in self.sim.p2p.measurement.original_senders.items()})
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plt.figure(figsize=(12, 8))
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plt.bar(list(original_senders.keys()), list(original_senders.values()))
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plt.xlabel("Node ID")
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plt.ylabel("Counts")
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plt.title("Original Sender Counts")
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plt.xlim(-1, self.config.mixnet.num_nodes)
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plt.show()
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# Create the bar plot for suspected sender counts
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keys = list(suspected_senders.keys())
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values = list(suspected_senders.values())
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# Create the bar plot
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plt.figure(figsize=(12, 8))
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plt.bar(keys, values)
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plt.xlabel("Node ID")
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plt.ylabel("Counts")
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plt.title("Suspected Sender Counts")
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plt.xlim(-1, self.config.mixnet.num_nodes)
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plt.show()
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# Create the bar plot for broadcasters
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broadcasters = ({node.id: count for node, count in self.sim.p2p.broadcasters.items()})
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plt.figure(figsize=(12, 8))
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plt.bar(list(broadcasters.keys()), list(broadcasters.values()))
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plt.xlabel("Node ID")
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plt.ylabel("Counts")
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plt.title("Broadcasters")
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plt.xlim(-1, self.config.mixnet.num_nodes)
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plt.show()
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# Calculate the mean and standard deviation of the counts
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mean = np.mean(values)
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std_dev = np.std(values)
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# Plot the histogram of the values
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plt.figure(figsize=(12, 8))
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plt.hist(values, bins=30, density=True, alpha=0.6, color="g", label="Counts Histogram")
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# Plot the normal distribution curve
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xmin, xmax = plt.xlim()
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x = np.linspace(xmin, xmax, 100)
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p = stats.norm.pdf(x, mean, std_dev)
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plt.plot(x, p, "k", linewidth=2, label="Normal Distribution")
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title = "Fit results: mean = %.2f, std_dev = %.2f" % (mean, std_dev)
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plt.title(title)
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plt.xlabel("Counts")
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plt.ylabel("Density")
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plt.legend()
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plt.show()
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def timing_attack_with(self, receiver: "Node", window: int, remaining_hops: int, sender: "Node" = None) -> Counter:
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assert remaining_hops >= 1
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# Start inspecting senders who sent messages that were arrived in the receiver at the given window
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if sender is not None:
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senders = {sender}
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else:
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senders = self.sim.p2p.adversary.msgs_received_per_window[window][receiver]
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# If the remaining_hops is 1, return the senders as suspected senders
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if remaining_hops == 1:
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return Counter(senders)
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# A result to be returned after inspecting all senders who sent messages to the receiver
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suspected_senders = Counter()
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# Inspect each sender who sent messages to the receiver
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for sender in senders:
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# Track back to each window where that sender might have received any messages.
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time_range = self.config.mixnet.max_mix_delay + self.config.p2p.max_network_latency
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window_range = int(time_range / self.config.adversary.window_size)
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for prev_window in range(window - 1, window - 1 - window_range, -1):
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if prev_window < 0:
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break
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suspected_senders.update(self.timing_attack_with(sender, prev_window, remaining_hops - 1))
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return suspected_senders
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@staticmethod
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def print_nodes_per_hop(nodes_per_hop, starting_window: int):
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for hop, nodes in enumerate(nodes_per_hop):
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print(f"hop-{hop} from w-{starting_window}: {len(nodes)} nodes: {sorted([node.id for node in nodes])}")
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