Post on 10-Jul-2020
transcript
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Beauty and the Burst Remote Identification of Encrypted
Video Streams
Roei Schuster
Cornell Tech, Tel Aviv University
Vitaly Shmatikov
Cornell Tech
Eran Tromer
Columbia University, Tel Aviv University
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Video traffic is interesting
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Video traffic is encrypted
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What can still be learned?
Video traffic is encrypted
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victim
streaming service
Traffic analysis for video identification
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victim
streaming service
Traffic analysis for video identification
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victim
Metadata! packet times, sizes, …
streaming service
Traffic analysis for video identification
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Victim is watching
“Beauty and the Beast”!
victim
Metadata! packet times, sizes, …
streaming service
Traffic analysis for video identification
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Initial buffering, then “on”/“off” bursts pa
cke
t siz
e (
byte
s)
time (seconds)
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Initial buffering, then “on”/“off” bursts pa
cke
t siz
e (
byte
s)
time (seconds)
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[RLLTBD ’11], [ARNL ’12], [MFWS ’13], …
Initial buffering, then “on”/“off” bursts pa
cke
t siz
e (
byte
s)
time (seconds)
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[RLLTBD ’11], [ARNL ’12], [MFWS ’13], …
Where do bursts come from?
Initial buffering, then “on”/“off” bursts pa
cke
t siz
e (
byte
s)
time (seconds)
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streaming service
Video representation on server
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streaming service
Video representation on server
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streaming service
Pulp Fiction
Die Hard
12 Monkeys
The Fifth Element
Die Hard II
Armageddon
Video representation on server
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Pulp Fiction
Die Hard
12 Monkeys
The Fifth Element
Die Hard II
Armageddon
MPEG-DASH standard:
widely adopted by Netflix, YouTube, others
Video representation on server
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Pulp Fiction
Die Hard
12 Monkeys
The Fifth Element
Die Hard II
Armageddon
segment2.m4s
segment3.m4s
segment4.m4s
segment1.m4s
video stored in segment-files
MPEG-DASH standard:
widely adopted by Netflix, YouTube, others
Video representation on server
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Pulp Fiction
Die Hard
12 Monkeys
The Fifth Element
Die Hard II
Armageddon
segment2.m4s
segment3.m4s
segment4.m4s
segment1.m4s
video stored in segment-files
segment = a few seconds
of playback
MPEG-DASH standard:
widely adopted by Netflix, YouTube, others
0-5sec
5-10sec
10-15sec
15-20sec
Video representation on server
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server client
buffer below threshold?
yes
request next
segment
no
segment2.m4s
segment3.m4s
segment4.m4s
segment1.m4s
server
segment5.m4s
segment6.m4s
DASH client-server interaction (simplified)
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server client
buffer below threshold?
yes
request next
segment
no
segment2.m4s
segment3.m4s
segment4.m4s
segment1.m4s
server
segment5.m4s
segment6.m4s
segment fetched every few seconds
DASH client-server interaction (simplified)
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server client
buffer below threshold?
yes
request next
segment
no
segment2.m4s
segment3.m4s
segment4.m4s
segment1.m4s
server
segment5.m4s
segment6.m4s
fetching causes a traffic burst
segment fetched every few seconds
DASH client-server interaction (simplified)
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Different video seconds require different amount of bytes to encode
Bitra
te (
byte
s)
Time (seconds)
Iguana vs. Snakes VBR
Variable bit rate encoding
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Phases of Iguana vs Snakes in Bitrate
scenery, movement,
tension rising
Bitra
te (
bits p
er
se
co
nd
)
Time (seconds)
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Phases of Iguana vs Snakes in Bitrate
tension peaking, iguana is still
Bitra
te (
bits p
er
se
co
nd
)
Time (seconds)
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Phases of Iguana vs Snakes in Bitrate
chase
Bitra
te (
bits p
er
se
co
nd
)
Time (seconds)
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Phases of Iguana vs Snakes in Bitrate
chase
iguana almost captured
Bitra
te (
bits p
er
se
co
nd
)
Time (seconds)
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Phases of Iguana vs Snakes in Bitrate
iguana safe, resting
Bitra
te (
bits p
er
se
co
nd
)
Time (seconds)
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Different video seconds require different amount of bytes to encode
Bitra
te (
byte
s)
Time (seconds)
Iguana vs. Snakes VBR
Variable bit rate encoding
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Pulp Fiction
Die Hard
12 Monkeys
The Fifth Element
Die Hard II
Armageddon Segment2.m4s
Segment3.m4s
Segment4.m4s
Segment1.m4s
Segment5.m4s
0-5sec
5-10sec
10-15sec
15-20sec
20-25sec
Variable bit rate variable segment size
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Variable segment size variable burst size
buffering On/off bursts
bu
rst siz
e (
byte
s)
Time (seconds)
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Variable segment size variable burst size
buffering On/off bursts
bu
rst siz
e (
byte
s)
Time (seconds)
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segments
VBR pattern
stream time
burst sizes
content
MPEG-DASH leak
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burst sizes
stream time
Does the pattern of burst (segment) sizes uniquely
characterize a title?
Can we learn a title’s identifying
pattern?
From a leak to a fingerprint
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burst sizes
stream time
Does the pattern of burst (segment) sizes uniquely
characterize a title?
Can we learn a title’s identifying
pattern?
Diversity: empirically measure pairwise distances for 3500 downloaded and segmented YouTube titles
From a leak to a fingerprint
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burst sizes
stream time
Does the pattern of burst (segment) sizes uniquely
characterize a title?
Consistency: empirically evaluate attacker’s measurement error bound
Can we learn a title’s identifying
pattern?
Diversity: empirically measure pairwise distances for 3500 downloaded and segmented YouTube titles
From a leak to a fingerprint
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burst sizes
stream time
Does the pattern of burst (segment) sizes uniquely
characterize a title?
Consistency: empirically evaluate attacker’s measurement error bound
Can we learn a title’s identifying
pattern?
~20% of YouTube titles have fingerprints
Diversity: empirically measure pairwise distances for 3500 downloaded and segmented YouTube titles
From a leak to a fingerprint
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victim network
Pulp Fiction
Die Hard
Armageddon attacker network
12 Monkeys
Attack overview
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victim network
Pulp Fiction
Die Hard
Armageddon attacker network
12 Monkeys
Attack overview
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metadata
victim network
Pulp Fiction
Die Hard
Armageddon attacker network
12 Monkeys
Attack overview
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detectors
metadata
train
ing
victim network
Pulp Fiction
Die Hard
Armageddon attacker network
12 Monkeys
Attack overview
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detectors
metadata
train
ing
victim network
Pulp Fiction
Die Hard
Armageddon Armageddon attacker network
12 Monkeys
Attack overview
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detectors
metadata
train
ing
victim network
Pulp Fiction
Die Hard
Armageddon Armageddon attacker network
12 Monkeys
Attack overview
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detectors
metadata
train
ing
victim network
Pulp Fiction
Die Hard
Armageddon Armageddon attacker network
Victim is watching
“Armageddon”!
12 Monkeys
Attack overview
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detectors
train
ing
victim network
Pulp Fiction
Die Hard
12 Monkeys
Armageddon Armageddon attacker network
vantage point?
Attack details
metadata
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bursts
Wi-Fi access points, proxies, routers, enterprise or national network censors, ISPs
on-path vantage
point
Scenario I: on-path attack
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detectors
train
ing
victim network
Pulp Fiction
Die Hard
12 Monkeys
Armageddon Armageddon attacker network
machine learning
Attack details
metadata
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• Very good at learning high-level concepts that are hard to
express formally (e.g., “traffic traces are similar”)
• Existing NN architectures very accurate on classification
and detection problems
Deep neural networks
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• Robust: can operate on noisy and coarse
measurements
• Agnostic to protocol-specific attributes (e.g.,
QUIC vs. TLS)
• Can learn features other than burst patterns,
e.g., arrival patterns of individual packets
• Can use multiple session representations,
train on all at once
Advantages of neural networks
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Each feature is a time-series, sampled at 0.25-second intervals (example: bytes per second)
0 0.25 0.5 0.75 1
time (seconds)
packe
t siz
e
1500
300
0.25 ⋅
15000
2 ⋅ 1500300……
Features considered: downstream/upstream/total values of bytes per
second, packet per second, average packet length, and burst sizes
Features
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detectors
train
ing
victim network
Pulp Fiction
Die Hard
12 Monkeys
Armageddon Armageddon attacker network
neural net
Attack
On-path attacker
metadata
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10 titles 100 1-minute sessions
18 titles 100 3-minute sessions + 3500 sessions of different other titles
10 titles 100 1.5-minute sessions
100 titles 100 1-minute sessions
Datasets and identification experiments
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10 titles 100 1-minute sessions
18 titles 100 3-minute sessions + 3500 sessions of different other titles
10 titles 100 1.5-minute sessions
100 classes
100 titles 100 1-minute sessions
Datasets and identification experiments
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10 titles 100 1-minute sessions
18 titles 100 3-minute sessions + 3500 sessions of different other titles
10 titles 100 1.5-minute sessions
100 classes
18+1=19 classes
open-world identification
100 titles 100 1-minute sessions
Datasets and identification experiments
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10 titles 100 1-minute sessions
18 titles 100 3-minute sessions + 3500 sessions of different other titles
10 titles 100 1.5-minute sessions
100 classes
18+1=19 classes
10 classes
10 classes
open-world identification
100 titles 100 1-minute sessions
Datasets and identification experiments
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10 titles 100 1-minute sessions
18 titles 100 3-minute sessions + 3500 sessions of different other titles
10 titles 100 1.5-minute sessions
100 classes
18+1=19 classes
10 classes
10 classes
98.5% accuracy
99.5% accuracy
98.6% accuracy
92.5% accuracy
open-world identification
100 titles 100 1-minute sessions
Datasets and identification experiments
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Netflix (feature: total burst size)
YouTube (feature: total burst size)
Predicted label Predicted label
“unknown” class, 3500 samples
Empirical results: confusion matrices
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Netflix (feature: total burst size)
YouTube (feature: total burst size)
Predicted label Predicted label
“unknown” class, 3500 samples Exactly 2 false positives
No recurrent confusions (despite many same-series titles)
Empirical results: confusion matrices
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0 false positives with 0.988 recall
0.0005 false positive rate with 0.93 recall
Netflix (feature: total burst size)
YouTube (feature: total burst size)
Tuning for precision
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detectors
train
ing
victim network
Pulp Fiction
Die Hard
12 Monkeys
Armageddon Armageddon attacker network
vantage point?
Attack details
train
ing
neural net
metadata
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Wi-Fi access points, proxies, routers, enterprise or national network censors, ISPs
bursts
victim network
on-path vantage
point
Off-path attackers
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bursts
victim network Off-path attackers
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A visited webpage? A smartphone app?
bursts
victim network Off-path attackers
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A visited webpage? A smartphone app?
bursts
victim network Off-path attackers
Example: checking Facebook feed while streaming “Armageddon”
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A visited webpage? A smartphone app?
bursts
victim network Off-path attackers
Example: checking Facebook feed while streaming “Armageddon”
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A visited webpage? A smartphone app?
bursts
victim network Off-path attackers
Example: checking Facebook feed while streaming “Armageddon”
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A visited webpage? A smartphone app?
bursts
victim network
Web ad
Off-path attackers
Example: checking Facebook feed while streaming “Armageddon”
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A visited webpage? A smartphone app?
bursts
victim network
Web ad
Off-path attackers
Example: checking Facebook feed while streaming “Armageddon”
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A visited webpage? A smartphone app?
bursts
victim network
Web ad
Three-fold confinement: different device, browser process, sandboxed iframe
Off-path attackers
Example: checking Facebook feed while streaming “Armageddon”
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Browser
neighbor
viewer Cross-device attack
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JavaScript attacker client
Browser
attacker Web site
neighbor
viewer Cross-device attack
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JavaScript attacker client
Browser
attacker Web site
neighbor
viewer Cross-device attack
messages
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JavaScript attacker client
Congestion
Browser
attacker Web site
neighbor
viewer Cross-device attack
messages
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JavaScript attacker client
Congestion
Browser
attacker Web site
bursts
neighbor
viewer Cross-device attack
messages
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JavaScript attacker client
Congestion
Browser
attacker Web site
bursts
delays
neighbor
viewer Cross-device attack
messages
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JavaScript attacker client
Noisy, coarse estimate of actual traffic bursts
Congestion
Browser
attacker Web site
bursts
delays
neighbor
viewer Cross-device attack
messages
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traffic burst sizes (scaled down) Message
delays
Delay-bursts dela
y (
mill
iseconds)
time (seconds)
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For each traffic burst, compute aggregate delay induced. Use resulting time-series as input to neural network
traffic burst sizes (scaled down) Message
delays
Delay-bursts dela
y (
mill
iseconds)
time (seconds)
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Delay-bursts vs. traffic bursts
delay-bursts time series: the delays induced by traffic bursts
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Accuracy: 0.965
false positive rate: 0.003, recall 0.933
1/10 cross-device attack: precision vs. recall
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JavaScript detector code
Browser
attacker Web site
Cross-device attack
neighbor
viewer
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attacker Web site
Cross-site attack
Streaming client
victim PC browser window
browser window
JavaScript detector code
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• Modern streaming traffic characteristics
– Title bitrate pattern unique when sampled at few-seconds granularity
– Fetching at segment granularity (= every few seconds)
• Maximizes “quality of experience”, server load, and network
bandwidth utilization
• However, information leakage is intrinsic…
Buffer below threshold?
yes
fetch next segment
no
Mitigating the DASH leak
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• Further information and the paper:
https://beautyburst.github.io/
Thank you!