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Censored Planet: Global Censorship Observatory

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Censored Planet: Global Censorship Observatory Censored Planet: Global Censorship Observatory Roya Ensafi University of Michigan Dec 27,2018 In my research lab, we ... develop frameworks to detect network interference, apply these frameworks to understand the behavior of network intermediaries, and use this understanding to defend against interference by building tools that safeguard users. Reports suggest Internet censorship practices are at rise! Network Interference Can Happen on Any Layer 1 A user types www.cnn.com into the browser 2 OS sends a DNS query to learn the IP address 3 Browser fetches the website Authoritative 4 Browser loads third-party resources DNS resolver DNS resolver www.cnn.com Server CDN PoP Home ISP User gateway router ISP router Server Transit Client Side Network Server Side Network Interference Can Happen on Any Layer 1 A user types www.cnn.com into the browser 2 OS sends a DNS query to learn the IP address 3 Browser fetches the website 4 Browser loads third-party resources DNS resolver www.cnn.com Server CDN PoP Home ISP User gateway router ISP router Server Transit Client Side Network Server Side Measuring Censorship is a Complex Problem! Internet censorship practices are diverse in their methods, targets, timing, differing by regions, as well as across time. Why Measure Censorship? NETWORK CENSORSHIP IS ON THE RISE ● Information controls harm citizens ● Spreading beyond the large powers ● Frequently opaque in topic & technique WE NEED DATA TO: ● Support transparency & accountability ● Improve technological defenses ● Inform users & public policy Why Measure Censorship? NETWORK CENSORSHIP IS ON THE RISE ● Information controls harm citizens Freedom on the Net 2018: ● Spreading beyond the large powers ● Frequently opaque in topic & technique “...When users become more aware of censorship, WE NEED DATA TO: they often take actions that enhance [I]nternet freedom ● Support transparency & accountability and protect fellow users” ● Improve technological defenses ● Inform users & public policy The Vision “Censorship weather map” Raw to continually monitor Cend Pat Internet censorship around the world Perte rov bok rut y ot Top c dos b ony How Have We Collected Data on Censorship? Common approach: ● Deploy hardware or software in censored region (e.g. RIPE Atlas, OONI probe) ● Ask people on the ground, or use VPNs, or Client research networks (e.g., FreedomHouse, PlanetLab) ? THREE KEY CHALLENGES: Coverage, continuity, and ethics Collecting consistent, continuous, and global Server data requires a different approach. From: mic.com My thought was, “it’s not safe for volunteers and activists to help you to do that.” From: CNN.com Freedom on the Net 2018 “Many governments are enforcing criminal penalties for the publication of what they deem false news” How OONI Deals with Potential Risks? OONI is a global community of volunteers collecting data on Internet censorship To deal with the potential risk: Volunteer “OONI provide as much informed ? choice to the user as possible (to Control minimize potential risk) => being able to choose which websites to Blocked test, whether to upload site measurements or not, what type of data to submit, etc.” How OONI Deals with Potential Risks? To minimize potential risk, OONI: - “Provide as much informed choice to the user as possible => being Volunteer able to choose which websites to test, whether to upload ? measurements or not, what type of data to submit, etc.” Control - Establish relationships with locals & civil society Blocked - Keep the community of volunteer site involved Measuring Internet Censorship Globally… Remotely! REFRAMING THE PROBLEM: How can we detect whether pairs of hosts around the world can talk to each other? Client … without volunteer participation?. ? Chang! Server Leveraging Existing Hosts as Vantage Points These machines speak to the world, and they follow TCP/IP, the basic communication protocol of the Internet. How can we leverage subtle TCP behavior to detect whether two distant hosts can communicate? 140 million IPv4 hosts that respond to TCP SYNs Spooky Scan Spooky Scan uses TCP/IP side-channels to detect whether a client and server can communicate (and in which direction packets are blocked) Client ? Goal: Detect blocking from off-path ? Server * Detecting Intentional Packet Drops on the Internet via TCP/IP Side Channels Roya Ensafi, Knockel, Alexander, and Crandall (PAM ’14) * Idle Port Scanning and Non-interference Analysis of Network Protocol Stacks Using Model Checking Roya Ensafi, Park, Kapur, and Crandall (Usenix Security 2010) * TCP Idle Scan Antirez (Bugtraq 1998) Background: TCP/IP Protocol TCP Handshake: SYN-ACK RST SYN [IP_ID:X] Port status is open/closed SYN-ACK [IP_ID: Y] ACK [IP_ID: X+1] SYN SYN-ACK SYN-ACK SYN-ACK Port status is open Spooky Scan Requirements Client Server Must maintain a Open port and global value for IP_ID retransmitting SYN-ACKs Measurement Machine Must be able to spoof packets Spooky Scan Client IP_ID Measurement Client machine Server Spooky Scan No direction blocked 1 SYN-ACK Client IP_ID: 7000 Measurement Client machine Server Spooky Scan No direction blocked 1 SYN-ACK Client IP_ID: 2 RST [IP_ID: 7000] 7000 Measurement Client machine Server Spooky Scan Scan No direction blocked 1 SYN-ACK Client IP_ID: 2 RST [IP_ID: 7000] 7000 Measurement Client machine 3 Spoofed SYN [src: Client IP] Server Spooky Scan No direction blocked 1 SYN-ACK Client IP_ID: 2 RST [IP_ID: 7000] 7000 Measurement Client machine 3 Spoofed SYN [src: Client IP] SYN-ACK 4 Server Spooky Scan No direction blocked 1 SYN-ACK Client IP_ID: 2 RST [IP_ID: 7000] 7000 7001 Measurement Client machine 3 Spoofed SYN [src: Client IP] SYN-ACK 4 5 RST [IP_ID: 7001] Server 6 SYN-ACK Spooky Scan 7 RST [IP_ID: 7002] No direction blocked 1 SYN-ACK Client IP_ID: 2 RST [IP_ID: 7000] 7000 7001 7002 Measurement Client machine 3 Spoofed SYN [src: Client IP] SYN-ACK 4 5 RST [IP_ID: 7001] Server Probe [IP_ID: 7003] 6 SYN-ACK Spooky Scan 7 RST [IP_ID: 7002] No direction blocked 1 SYN-ACK Client IP_ID: 2 RST [IP_ID: 7000] 7000 7001 7002 Measurement Client 7003 machine 3 Spoofed SYN [src: Client IP] SYN-ACK 4 5 RST [IP_ID: 7001] Server Probe [IP_ID: 7002] 5 SYN-ACK Spooky Scan 6 RST [IP_ID: 7001] Server-to-Client 1 SYN-ACK Client IP_ID: 2 RST [IP_ID: 7000] 7000 blocked 7001 7002 Measurement Client machine 3 Spoofed SYN [src: ClientIP] SYN-ACK 4 Server 6 SYN-ACK Spooky Scan 7 RST [IP_ID: 7002] Client-to-Server 1 SYN-ACK Client IP_ID: 2 RST [IP_ID: 7000] 7000 blocked 7001 7002 Measurement machine 3 5 RST Spoofed SYN [src: ClientIP] SYN-ACK 4 Server Probe [IP_ID: 7004] 6 SYN-ACK Spooky Scan 7 RST [IP_ID: 7002] Client-to-Server 1 SYN-ACK Client IP_ID: 2 RST [IP_ID: 7000] 7000 blocked 7001 7002 Measurement 7003 machine 7004 3 5 RST Spoofed SYN [src: ClientIP] SYN-ACK 4 Server Spooky Scan Server-to-Client Blocked No Direction Blocked Client-to-Server Blocked 횫 IP_ID1 = 1 횫 IP_ID1 = 2 횫 IP_ID1 = 2 횫 IP_ID2 = 1 횫 IP_ID2 = 1 횫 IP_ID2 = 2 Client IP_ID Noise Client Coping with Client IP_ID Noise Amplifying the signal Client Effect of sending N spoofed SYNs: Server-to-Client Blocked No Direction Blocked Client-to-Server Blocked 횫 IP_ID1 = (1 + noise) 횫 IP_ID1 = (1 + N + noise) 횫 IP_ID1 = (1 + N + noise) 횫 IP_ID2 = noise 횫 IP_ID2 = noise 횫 IP_ID2 = (1 + N + noise) Coping with Client IP_ID Noise Amplifying the signal Client Effect of sending N spoofed SYNs: Server-to-Client Blocked No Direction Blocked Client-to-Server Blocked 횫 IP_ID1 = (1 + noise) 횫 IP_ID1 = (1 + N + noise) 횫 IP_ID1 = (1 + N + noise) 횫 IP_ID2 = noise 횫 IP_ID2 = noise 횫 IP_ID2 = (1 + N + noise) Repeating the experiment To eliminate the effects of packet loss, sudden bursts of packets, ... Spooky Scan with Noise: Visualization Probing method Send 5 spoofed SYNs per second For first 30s, query IP_IDs. Then, for another 30s Query IP_ID once per second Server-to-Client Blocked No Direction Blocked Client-to-Server Blocked Tor relay (SE) to client (CN) blocked No block btw client (US) and Tor relay (SE) Client (AZ) to Tor relay (SE) blocked 35 Augur: Spooky for Continuous Scanning Problem: Want to optimize Spooky to probe many hosts, all the time. Insight: Some measurements are much noisier than others. * Internet-Wide Detection of Connectivity Disruptions P. Pearce*, R. Ensafi*, F. Li, N. Feamster, V. Paxson *joint first authors IEEE S&P (“Oakland”) 2017 Augur: Spooky for Continuous Scanning Problem: Want to optimize Spooky to probe many hosts, all the time. Insight: Some measurements are much noisier than others. Probing Methodology: Until we have high enough confidence (or up to): - For first 4s, query IP_ID every sec Run - Send 10 spoofed SYNs Query IP_ID - Query IP_ID Augur: Spooky for Continuous Scanning Problem: Want to optimize Spooky to probe many hosts, all the time. Insight: Some measurements are much noisier than others. Probing Methodology: Until we have high enough confidence (or up to): Repeat runs and use - For first 4s, query IP_ID every sec Sequential Hypothesis Testing Run - Send 10 spoofed SYNs Query IP_ID to gradually build confidence. - Query IP_ID Sequential Hypothesis Testing in Augur Defining a random variable: if no IP_ID acceleration occurs if IP_ID acceleration occurs Sequential Hypothesis Testing in Augur Defining a random variable: if no IP_ID acceleration occurs* if IP_ID acceleration occurs* *measurement window following injection Calculate known outcome probabilities (priors): IP_ID evolution in control measurement Prior 1: Prob. of no IP_ID acceleration when there is blocking phase, ~0.5 Prior 2: Prob. of IP_ID acceleration
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