Shadow: Real Applications, Simulated Networks. Dr. Rob Jansen U.S. Naval Research Laboratory Center for High Assurance Computer Systems
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1 Shadow: Real Applications, Simulated Networks Dr. Rob Jansen Center for High Assurance Computer Systems Cyber Modeling and Simulation Technical Working Group Mark Center, Alexandria, VA October 25 th, 2017
2 What is Shadow? Open-source network simulator / emulator hybrid Directly executes real applications like Tor and Bitcoin over a simulated network topology Efficient, scalable, deterministic, accurate, and more! Shadow: Real Applications, Simulated Networks 2
3 How does Shadow Work? App OS Kernel TCP IP App OS Kernel TCP IP Internet App OS Kernel TCP IP Shadow: Real Applications, Simulated Networks 3
4 How does Shadow Work? Shadow - OS emulation and network simulation App OS Kernel TCP IP App OS Kernel TCP IP Internet App OS Kernel TCP IP Shadow: Real Applications, Simulated Networks 4
5 Why should you care? Expedite research and development Evaluate software mods or attacks without harming real users Understand holistic effects before deployment Shadow supports simulation for general-purpose applications Shadow: Real Applications, Simulated Networks 5
6 Talk Outline Experimentation options and tradeoffs Shadow design Simulation use cases The Tor Anonymity Network Gentle Tor introduction Congestion problems Scheduling algorithms Performance enhancing algorithms Denial of service attacks Conclusion Shadow: Real Applications, Simulated Networks 6
7 Experimentation Options
8 Desirable Experiment Properties Controllable Scalable Reproducible Accurate Shadow s design goal Shadow: Real Applications, Simulated Networks 8
9 Live Network Experiments Experimenting in a deployed distributed system Pros Most realistic The target environment Cons Hard to manage/debug Lengthy deployment Security risks Shadow: Real Applications, Simulated Networks 9
10 Testbed Network Experiments Experimenting in a distributed testbed (e.g. PlanetLab) Pros Close to target environment Runs on the Internet or uses Internet protocols Cons Hard to manage and debug Doesn t scale well in low-resource environs Can be hard to model network properties Shadow: Real Applications, Simulated Networks 10
11 Network Emulation Experimenting with network emulators (e.g. Modelnet) Pros Runs on the target OS and uses Internet protocols Can model various network properties Cons Requires multiple machines and custom installed OS Must run in real time Per-process overhead may cause kernel issues Shadow: Real Applications, Simulated Networks 11
12 Network Simulation Experimenting with network simulators (e.g. NS3) Pros Deterministic (reproducible) Can model various network properties Can scale very well Cons Application model can be too abstract Abstractions can lead to inaccurate results Shadow: Real Applications, Simulated Networks 12
13 Simulation vs. Emulation: Realism Simulation Abstracts away most system components Simulator is generally only internally consistent Less resource intensive Emulation Runs the real OS, kernel, protocols, applications Software is interoperable with external components More resource intensive Shadow: Real Applications, Simulated Networks 13
14 Simulation vs. Emulation: Time Simulation As-fast-as-possible Control over clock, can pause time without issue Weak hardware extends total experiment runtime Emulation Real time Time must advance in synchrony with wall-clock Weak hardware causes glitches that are difficult to detect and diagnose Shadow: Real Applications, Simulated Networks 14
15 Shadow Design
16 Shadow Overview Parallel discrete-event network simulator Models routing, latency, bandwidth Simulates time, CPU, OS TCP/UDP, sockets, queuing, threading Emulates POSIX C API on Linux Directly executes apps as plug-ins Shadow: Real Applications, Simulated Networks 16
17 Initializing the Simulation Load network model, create virtual hosts Shadow: Real Applications, Simulated Networks 17
18 Shadow Simulation Layout Network model Global simulation clock Discrete event queue Shadow worker threads Virtual hosts Virtual processes (i.e. namespaces) Directly executed software plug-ins Virtual threads Shadow: Real Applications, Simulated Networks 18
19 App Memory Management Apps loaded in independent namespaces, copy-on-write Namespace 1 Namespace 2 Namespace 3 Library Data 1 Library Code (read-only) Library Data 2 Library Data 3 Plug-in Code (read-only) Plug-in Data 1 Plug-in Data 2 Plug-in Data 3 Shadow: Real Applications, Simulated Networks 19
20 Direct Execution in a Simulator Namespace 1 Namespace 2 Namespace 3 libc libc libc Shadow Simulated Linux Kernel Libraries and Network Transport Function Interposition Function Interposition Function Interposition libc API (send, write, etc.) libc API (send, write, etc.) libc API (send, write, etc.) Application Application Application Shadow: Real Applications, Simulated Networks 20
21 Simulation Use Cases The Tor Anonymity Network
22 Gentle Tor Introduction
23 Tor Overview Tor: a censorship resistant, privacy-enhancing anonymous communication system Estimated ~1.75 M. Users/Day (metrics.torproject.org) Shadow: Real Applications, Simulated Networks 23
24 Anonymous Communication Shadow: Real Applications, Simulated Networks 24
25 Congestion Analysis
26 Multiple Hops Tor is Slower Shadow: Real Applications, Simulated Networks 26
27 Buffers in a Tor Relay Kernel Input Tor Input Tor Circuits Tor Output Kernel Output Shadow: Real Applications, Simulated Networks 27
28 Tracking Congestion in Tor Track the Unique ID Unique ID Unique ID Track the Unique ID Kernel Input Tor Input Tor Circuits Tor Output Kernel Output Shadow: Real Applications, Simulated Networks 28
29 Tracking Congestion in Tor Track the Unique ID Unique ID Congestion occurs almost exclusively in outbound kernel buffers Unique ID Track the Unique ID Kernel Input Tor Input Tor Circuits Tor Output Kernel Output Shadow: Real Applications, Simulated Networks 29
30 Scheduling Analysis
31 Tor Circuit Priority Scenarios to understand outbound queue congestion Shadow: Real Applications, Simulated Networks 31
32 Tor Circuit Priority Scenarios to understand outbound queue congestion Cumulative Fraction Correctly differentiated pri+ 0.6 rr pri Throughput (KiB/s) pri+ 0.6 rr pri Throughput (KiB/s) Shadow DETER Cumulative Fraction No differentiation Shadow DETER Shadow: Real Applications, Simulated Networks 32
33 Tor Circuit Priority Scenarios to understand outbound queue congestion Cumulative Fraction Correctly differentiated are unshared pri+ 0.6 rr pri Throughput (KiB/s) pri+ 0.6 rr pri Throughput (KiB/s) Shadow DETER Cumulative Fraction No differentiation % of any two circuits Shadow DETER Shadow: Real Applications, Simulated Networks 33
34 Performance Analysis
35 Kernel-Informed Socket Transport Queuing delays in kernel output buffer Problem 1: Circuit scheduling Solution: Don t handle sockets individually Process flush requests from all writable sockets Problem 2: Flushing to sockets buffer bloat Solution: Don t write if kernel can t send Use TCP info to bound kernel writes Shadow: Real Applications, Simulated Networks 35
36 Network Simulation with Shadow Enhanced Shadow with several missing TCP algorithms CUBIC congestion control Retransmission timers Selective acknowledgements (SACK) Forward acknowledgements (FACK) Fast retransmit/recovery Designed largest known private Tor network 3600 relays and simultaneously active clients Internet topology graph: ~700k vertices and 1.3m edges Analyze network-wide effects Shadow: Real Applications, Simulated Networks 36
37 KIST Reduces Kernel Congestion 1.0 Cumulative Fraction vanilla global KIST Time (ms) Shadow: Real Applications, Simulated Networks 37
38 KIST Improves Network Latency Cumulative Fraction vanilla global KIST Time to First Byte (s) Shadow: Real Applications, Simulated Networks 38
39 Denial of Service Attacks
40 The Sniper Attack Memory-based denial of service (DoS) attack Exploits vulnerabilities in Tor s flow control protocol Can be used to disable arbitrary Tor relays Shadow: Real Applications, Simulated Networks 40
41 The Sniper Attack Start Download entry exit Request Shadow: Real Applications, Simulated Networks 41
42 The Sniper Attack Package and Relay Reply entry exit Shadow: Real Applications, Simulated Networks 42
43 The Sniper Attack Stop Reading from Connection R entry exit Shadow: Real Applications, Simulated Networks 43
44 The Sniper Attack Flow Window Closed R entry exit Shadow: Real Applications, Simulated Networks 44
45 The Sniper Attack R entry exit Periodically Send SENDME SENDME Shadow: Real Applications, Simulated Networks 45
46 The Sniper Attack Flow Window Opened R entry exit SENDME Shadow: Real Applications, Simulated Networks 46
47 The Sniper Attack Out of Memory, Killed by OS Flow Window Opened R entry exit Periodically Send SENDME SENDME SENDME Shadow: Real Applications, Simulated Networks 47
48 The Sniper Attack: Results Implemented Sniper Attack Prototype Control Sybils via the Tor control protocol Tested in Shadow for safety Measured: Victim memory consumption rate Adversary bandwidth usage Developed defense, tested in Shadow, merged in Tor Shadow: Real Applications, Simulated Networks 48
49 RAM Consumed at Victim 1.0 Cumulative Fraction direct anonymous Mean Target RAM Consumption Rate (KiB/s) Shadow: Real Applications, Simulated Networks 49
50 Bandwidth Consumed at Adversary 1.0 Cumulative Fraction direct Tx anonymous Tx direct Rx anonymous Rx Mean Sniper BW Consumption Rates (KiB/s) Shadow: Real Applications, Simulated Networks 50
51 Speed of the Sniper Attack Direct Anonymous Relay Groups Select % 1 GiB 8 GiB 1 GiB 8 GiB Top Entry 1.7 0:01 0:18 0:02 0:14 Top 5 Entries 6.5 0:08 1:03 0:12 1:37 Top 20 Entries 19 0:45 5:58 1:07 8:56 Top Exit 3.2 0:01 0:08 0:01 0:12 Top 5 Exits 13 0:05 0:37 0:07 0:57 Top 20 Exits 35 0:29 3:50 0:44 5:52 < 1 GiB RAM < 50 KiB/s Downstream BW < 100 KiB/s Upstream BW Time (hours:minutes) to Consume RAM Shadow: Real Applications, Simulated Networks 51
52 Conclusion
53 Other Shadow Uses Tor Latency and throughput correlation attacks Denial of Service attacks (sockets, RAM, bandwidth) Changes to path selection algorithms Traffic admission control algorithms Traffic scheduling and prioritization algorithms Network load balancing algorithms Process RAM consumption and optimization Network and memory attacks in Bitcoin Distributed secure multiparty computation algorithms Software debugging Shadow: Real Applications, Simulated Networks 53
54 Future Shadow Enhancements Distribute across physical machines Support for multiple programming languages Host mobility Internet routing, network modeling User behavior modeling CPU performance modeling User interface Support additional applications (HTTP clients/server, bitcoin, etc.) Improve code stability, documentation, testing, etc. Shadow: Real Applications, Simulated Networks 54
55 Questions Dr. Rob Jansen Center for High Assurance Computer Systems The Shadow Simulator shadow.github.io github.com/shadow
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