2024 release
This commit is contained in:
Yuheng Yang
parent
b7b3a02bfe
commit
604a544978
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In this lab, students implement the techniques from our group's ISCA 2022 paper `There's Always a Bigger Fish: A Case Study of a Misunderstood Timing Side Channel`. Students will begin by implementing a seemingly familiar cache-based side channel attack in Javascript, and will then be asked to reason about why this attack works. Then, students will remove a core part of the attack, but see that the code still works.
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**Setup**
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Students can complete this lab on their own machines. MacOS, Linux, Windows all should work. Google Chrome is required for Part 4 of this lab.
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Students can complete this lab on their own machines. MacOS, Linux, Windows all should work.
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185
index.html
185
index.html
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@ -1,185 +0,0 @@
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<!DOCTYPE html>
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<html>
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<head>
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<meta charset="utf-8" />
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<title>Website Fingerprinting Lab</title>
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<style>
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* {
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margin: 0;
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padding: 0;
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}
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body {
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font-family: Arial, Helvetica, sans-serif;
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padding: 64px;
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}
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h1 {
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font-size: 32px;
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margin-bottom: 16px;
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}
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p {
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margin-bottom: 8px;
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}
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#buttons {
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margin-bottom: 16px;
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}
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button {
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background: #fff;
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border: 2px solid #3498db;
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border-radius: 4px;
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color: #3498db;
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cursor: pointer;
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display: inline-block;
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font-size: 16px;
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margin-right: 16px;
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padding: 8px 16px;
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}
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button.disabled {
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background: #ccc;
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border-color: #ccc;
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color: #666;
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cursor: default;
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}
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.trace {
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margin-bottom: 16px;
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}
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</style>
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</head>
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<body>
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<h1>Website Fingerprinting Lab</h1>
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<div id="buttons">
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<button id="collect-trace">Collect trace</button>
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<button id="download-traces">Download traces</button>
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</div>
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<div id="traces"></div>
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<script src="https://d3js.org/d3.v6.js"></script>
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<script type="text/javascript">
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const worker = new Worker("worker.js");
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const collectTraceButton = document.getElementById("collect-trace");
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const downloadTracesButton = document.getElementById("download-traces");
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// Default values for when the automation script isn't being used. When
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// the script is in use, these values will get overwritten.
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window.trace_length = 5000;
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window.using_automation_script = false;
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window.recording = false;
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window.traces = [];
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let traceIds = [];
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worker.onmessage = (e) => {
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window.recording = false;
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const trace = JSON.parse(e.data);
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window.traces.push(trace);
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if (window.using_automation_script) {
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// Don't display traces when automation script is in use
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return;
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}
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// Create new trace div
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const parent = document.getElementById("traces");
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const div = document.createElement("div");
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const traceId = "a" + Math.random().toString().substring(2, 10);
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div.setAttribute("id", traceId);
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div.className = "trace";
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parent.appendChild(div);
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traceIds.push(traceId);
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// Trace dimensions
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const width = parent.getBoundingClientRect().width;
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const height = 64;
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// Create div for new trace
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const svg = d3
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.select("#" + traceId)
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.append("svg")
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.attr("width", width)
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.attr("height", height);
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// Find largest value across all traces
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const maxVal = d3.max(window.traces, (d) => d3.max(d));
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for (let i = 0; i < window.traces.length; i++) {
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// Re-visualize all traces each time in case maxVal changes
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const x = d3
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.scaleLinear()
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.domain([0, window.traces[i].length])
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.range([0, width]);
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const color = d3
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.scaleQuantize()
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.range(["#0d0887", "#7e03a8", "#cc4778", "#f89540", "#f0f921"])
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.domain([0, maxVal]);
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svg
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.selectAll()
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.data(window.traces[i].map((x, i) => ({ index: i, value: x })))
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.join("rect")
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.attr("x", (d) => x(d.index))
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.attr("y", 0)
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.attr("width", x(1))
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.attr("height", height)
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.style("fill", (d) => color(d.value));
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}
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// Reset UI
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collectTraceButton.innerText = "Collect trace";
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collectTraceButton.className = "";
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};
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function collectTrace() {
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collectTraceButton.innerText = "Collecting trace...";
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collectTraceButton.className = "disabled";
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window.recording = true;
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worker.postMessage({
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type: "start",
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trace_length: window.trace_length,
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});
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}
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collectTraceButton.onclick = () => {
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if (window.recording) return;
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window.recording = true;
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collectTraceButton.innerText = "Starting in 3...";
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collectTraceButton.className = "disabled";
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setTimeout(() => {
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collectTraceButton.innerText = "Starting in 2...";
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setTimeout(() => {
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collectTraceButton.innerText = "Starting in 1...";
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setTimeout(collectTrace, 1000);
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}, 1000);
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}, 1000);
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};
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downloadTracesButton.onclick = () => {
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const blob = new Blob([JSON.stringify({ traces: window.traces })], {
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type: "application/json",
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});
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const url = URL.createObjectURL(blob);
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const elem = document.createElement("a");
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elem.href = url;
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elem.download = "traces.json";
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document.body.appendChild(elem);
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elem.click();
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document.body.removeChild(elem);
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};
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</script>
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</body>
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</html>
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const runs = 10;
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function measureOneLine() {
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const LINE_SIZE = 16; // 64/sizeof(int)
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const LINE_SIZE = 32; // 128/sizeof(int)
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let result = [];
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// Fill with -1 to ensure allocation
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@ -6,22 +6,22 @@ from sklearn.metrics import classification_report
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from sklearn.model_selection import train_test_split
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def eval():
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y_pred_full, y_test_full = [], []
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y_pred_full, y_test_full = [], []
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# Re-train 10 times in order to reduce effects of randomness
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for i in range(10):
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### TODO: Exercise 2-4
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### 1. Load data from traces file
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### 2. Split data into X_train, X_test, y_train, y_test with train_test_split
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### 3. Train classifier with X_train and y_train
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### 4. Use classifier to make predictions on X_test. Save the result to a variable called y_pred
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# Re-train 10 times in order to reduce effects of randomness
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for i in range(10):
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### TODO: Exercise 2-5
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### 1. Load data from traces file
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### 2. Split data into X_train, X_test, y_train, y_test with train_test_split
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### 3. Train classifier with X_train and y_train
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### 4. Use classifier to make predictions on X_test. Save the result to a variable called y_pred
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# Do not modify the next two lines
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y_test_full.extend(y_test)
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y_pred_full.extend(y_pred)
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# Do not modify the next two lines
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y_test_full.extend(y_test)
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y_pred_full.extend(y_pred)
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### TODO: Exercise 2-4 (continued)
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### 5. Print classification report using y_test_full and y_pred_full
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### TODO: Exercise 2-5 (continued)
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### 5. Print classification report using y_test_full and y_pred_full
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if __name__ == "__main__":
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eval()
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eval()
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// Number of sweep counts
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// TODO: Choose an appropriate value!
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let P;
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// TODO (Exercise 2-1): Choose an appropriate value!
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let P = 1000;
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// Number of elements in your trace
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let K = 5 * 1000 / P;
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// Save start timestamp
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start = performance.now();
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// TODO: Record data for 5 seconds and save values to T.
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// TODO (Exercise 2-1): Record data for 5 seconds and save values to T.
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// Once done recording, send result to main thread
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postMessage(JSON.stringify(T));
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from sklearn.model_selection import train_test_split
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def eval():
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y_pred_full, y_test_full = [], []
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y_pred_full, y_test_full = [], []
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# Re-train 10 times in order to reduce effects of randomness
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for i in range(10):
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### TODO: Exercise 5
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### 1. Load data from traces file
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### 2. Split data into X_train, X_test, y_train, y_test with train_test_split
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### 3. Train classifier with X_train and y_train
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### 4. Use classifier to make predictions on X_test. Save the result to a variable called y_pred
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# Re-train 10 times in order to reduce effects of randomness
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for i in range(10):
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### TODO: Exercise 2-5
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### 1. Load data from traces file
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### 2. Split data into X_train, X_test, y_train, y_test with train_test_split
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### 3. Train classifier with X_train and y_train
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### 4. Use classifier to make predictions on X_test. Save the result to a variable called y_pred
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# Do not modify the next two lines
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y_test_full.extend(y_test)
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y_pred_full.extend(y_pred)
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# Do not modify the next two lines
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y_test_full.extend(y_test)
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y_pred_full.extend(y_pred)
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### TODO: Exercise 5 (continued)
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### 5. Print classification report using y_test_full and y_pred_full
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### TODO: Exercise 2-5 (continued)
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### 5. Print classification report using y_test_full and y_pred_full
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if __name__ == "__main__":
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eval()
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eval()
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// Number of sweep counts
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// TODO: Choose an appropriate value!
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let P;
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// TODO (Exercise 3-1): Choose an appropriate value!
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let P = 1000;
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// Number of elements in your trace
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let K = 5 * 1000 / P;
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// Save start timestamp
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start = performance.now();
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// TODO: Record data for 5 seconds and save values to T.
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// TODO (Exercise 3-1): Record data for 5 seconds and save values to T.
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// Once done recording, send result to main thread
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postMessage(JSON.stringify(T));
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@ -0,0 +1,89 @@
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## Optional
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**Report your browser version, CPU type, cache size, RAM amount, and OS. We use this information to learn about the attack’s behavior on different machines.**
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- Browser:
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- CPU:
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- Cache sizes:
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- RAM:
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- OS:
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## 1-2
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**Use the values printed on the webpage to find the median access time and report your results as follows.**
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| Number of Cache Lines | Median Access Latency (ms) |
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| --------------------- | -------------------------- |
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| 1 | |
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| 10 | |
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| 100 | |
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| 1,000 | |
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| 10,000 | |
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| 100,000 | |
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| 1,000,000 | |
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| 10,000,000 | |
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## 1-3
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**According to your measurement results, what is the resolution of your `performance.now()`? In order to measure differences in time with `performance.now()``, approximately how many cache accesses need to be performed?**
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## 2-2
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**Report important parameters used in your attack. For each sweep operation, you access N addresses, and you count the number of sweep operations within a time interval P ms. What values of N and P do you use? How do you choose N? Why do not you choose P to be larger or smaller?**
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## 2-3
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**Take screenshots of the three traces generated by your attack code and include them in the lab report.**
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## 2-4
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**Use the Python code we provided in Part 2.1 to analyze simple statistics (mean, median, etc.) on the traces from google.com and nytimes.com. Report the statistic numbers.**
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## 2-6
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**Include your classification results in your report.**
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```
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```
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## 3-2
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**Include your new accuracy results for the modified attack code in your report.**
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```
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```
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## 3-3
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**Compare your accuracy numbers between Part 2 and 3. Does the accuracy decrease in Part 3? Do you think that our “cache-occupancy” attack actually exploits a cache side channel? If not, take a guess as to possible root causes of the modified attack.**
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48
update.sh
48
update.sh
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#!/bin/bash
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# Updates repository to latest starter code
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#
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# Adapted from Oliver Beckstein's ASU-CompMethodsPhysics-PHY494 course 2016-2020 placed into the public domain
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# With GitHub template repositories one needs to use --allow-unrelated-histories
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# at least once. https://help.github.com/en/github/creating-cloning-and-archiving-repositories/creating-a-repository-from-a-template
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progname="$0"
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REMOTE_NAME="startercode"
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REMOTE_URL="https://github.com/CSAIL-Arch-Sec/SHD-WebsiteFingerprintingLab.git"
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# progname, from top dir
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UPDATESH="./deploy/$(basename $progname)"
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CONTACT_MESSAGE="Contact the instructor and TA with a screen shot of ALL output from running $0."
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function die () {
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local msg="$1" err=${2:-1}
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echo "ERROR: ${msg}."
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exit $err
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}
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# ensure everything relative to top dir
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topdir="$(git rev-parse --show-toplevel)" || die "Failed to get rootdir"
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cd "${topdir}" || die "Failed to get to the git root dir ${rootdir}"
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# first time
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# 1. set remote repo
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# 2. merge histories between student (template) and remote skeleton
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if ! git remote get-url ${REMOTE_NAME} >/dev/null 2>&1; then
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echo "Adding remote repository '${REMOTE_NAME}'."
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git remote add ${REMOTE_NAME} ${REMOTE_URL}
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echo "Merging histories for the first time..."
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set -x
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git pull --allow-unrelated-histories -s recursive -X theirs --no-edit ${REMOTE_NAME} main || \
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{ git rev-list -1 MERGE_HEAD >/dev/null 2>&1 && git merge --abort ; \
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git remote rm ${REMOTE_NAME}; \
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die "Failed to merge histories. ${CONTACT_MESSAGE}" $?; }
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set +x
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fi
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echo "updating repository... git pull from ${REMOTE_NAME}"
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git pull --no-edit ${REMOTE_NAME} main || die "Failed to pull from ${REMOTE_NAME}. ${CONTACT_MESSAGE}"
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