1. Pattern Recognition
and Applications Lab
University
of Cagliari, Italy
Department of
Electrical and Electronic
Engineering
Evasion attacks against machine learning
at test time
Ba#sta
Biggio
(1)
Igino
Corona
(1),
Davide
Maiorca
(1),
Blaine
Nelson
(3),
Nedim
Šrndić
(2),
Pavel
Laskov
(2),
Giorgio
Giacinto
(1),
and
Fabio
Roli
(1)
(1)
University
of
Cagliari
(IT);
(2)
University
of
Tuebingen
(GE);
(3)
University
of
Postdam
(GE)

2.
http://pralab.diee.unica.it
Machine learning in adversarial settings
• Machine learning in computer security
– spam filtering, intrusion detection, malware detection
legitimate
malicious
x1
x2
f(x)
2

3.
http://pralab.diee.unica.it
Machine learning in adversarial settings
• Machine learning in computer security
– spam filtering, intrusion detection, malware detection
• Adversaries manipulate samples at test time to evade detection
legitimate
malicious
x1
x2
f(x)
3
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…

4.
http://pralab.diee.unica.it
Our work
Problem: can machine learning be secure? (1)
• Framework for proactive security evaluation of ML algorithms (2)
Adversary model
• Goal of the attack
• Knowledge of the attacked system
• Capability of manipulating data
• Attack strategy as an optimization problem
4
Bounded adversary!
(1) M.
Barreno,
B.
Nelson,
R.
Sears,
A.
D.
Joseph,
and
J.
D.
Tygar.
Can
machine
learning
be
secure?
ASIACCS
2006
(2) B.
Biggio,
G.
Fumera,
F.
Roli.
Security
evaluaVon
of
paWern
classifiers
under
aWack.
IEEE
Trans.
on
Knowl.
and
Data
Engineering,
2013
In
this
work
we
exploit
our
framework
for
security
evaluaVon
against
evasion
a)acks!

5.
http://pralab.diee.unica.it
Bounding the adversary’s capability
• Cost of manipulations
– Spam: message readability
• Encoded by a distance function in feature space (L1-norm)
– e.g., number of words that are modified in spam emails
5
d (x, !x ) ≤ dmax
x2
x1
f(x)
Bounded by a maximum value
x
Feasible domain
x '
We
will
evaluate
classifier
performance
vs.
increasing
dmax

6.
http://pralab.diee.unica.it
Gradient-descent evasion attacks
• Goal: maximum-confidence evasion
• Knowledge: perfect
• Attack strategy:
• Non-linear, constrained optimization
– Gradient descent: approximate
solution for smooth functions
• Gradients of g(x) can be analytically
computed in many cases
– SVMs, Neural networks
6
−2−1.5−1−0.500.51
x
f (x) = sign g(x)( )=
+1, malicious
−1, legitimate
"
#
$
%$
min
x'
g(x')
s.t. d(x, x') ≤ dmax
x '

7.
http://pralab.diee.unica.it
Computing descent directions
Support vector machines
Neural networks
7
x1
xd
δ1
δk
δm
xf
g(x)
w1
wk
wm
v11
vmd
vk1
……
……
g(x) = αi yik(x,
i
∑ xi )+ b, ∇g(x) = αi yi∇k(x, xi )
i
∑
g(x) = 1+exp − wkδk (x)
k=1
m
∑
#
$
%
&
'
(
)
*
+
,
-
.
−1
∂g(x)
∂xf
= g(x) 1− g(x)( ) wkδk (x) 1−δk (x)( )vkf
k=1
m
∑
RBF kernel gradient: ∇k (x,xi
) = −2γ exp −γ || x − xi
||2
{ }(x − xi
)

8.
http://pralab.diee.unica.it
g(x) − λ p(x|yc=−1), λ=0
−4 −3 −2 −1 0 1 2 3 4
−4
−2
0
2
4
−1
−0.5
0
0.5
1
• Problem: greedily min. g(x) may not lead to classifier evasion!
• Solution: adding a mimicry component that attracts the attack
samples towards samples classified as legitimate
Density-augmented gradient-descent
Mimicry component
(Kernel Density Estimator)
8
g(x) − λ p(x|yc=−1), λ=20
−4 −3 −2 −1 0 1 2 3 4
−4
−2
0
2
4
−4.5
−4
−3.5
−3
−2.5
−2
−1.5
−1
Now
all
the
aWack
samples
evade
the
classifier!
Some
aWack
samples
may
not
evade
the
classifier!
min
x'
g(x')− λp(x' | yc
= −1)
s.t. d(x, x') ≤ dmax

9.
http://pralab.diee.unica.it
Density-augmented gradient-descent
9
∇p(x | yc
= −1) = −
2
nh
exp −
|| x − xi ||2
h
#
$
%
&
'
( x − xi( )i|yi
c=−1∑KDE
gradient
(RBF
kernel):

10.
http://pralab.diee.unica.it
An example on MNIST handwritten digits
10
• Linear SVM, 3 vs 7. Features: pixel values.
Before attack (3 vs 7)
5 10 15 20 25
5
10
15
20
25
After attack, g(x)=0
5 10 15 20 25
5
10
15
20
25
After attack, last iter.
5 10 15 20 25
5
10
15
20
25
0 500
−2
−1
0
1
2
g(x)
number of iterations
Without mimicry
λ = 0
dmax
5000
Before attack (3 vs 7)
5 10 15 20 25
5
10
15
20
25
After attack, g(x)=0
5 10 15 20 25
5
10
15
20
25
After attack, last iter.
5 10 15 20 25
5
10
15
20
25
0 500
−2
−1
0
1
2
g(x)
number of iterations
With mimicry
λ = 10
dmax
5000

11.
http://pralab.diee.unica.it
Bounding the adversary’s knowledge
Limited knowledge attacks
• Only feature representation and learning algorithm are known
• Surrogate data sampled from the same distribution as the
classifier’s training data
• Classifier’s feedback to label surrogate data
11
PD(X,Y)data
Surrogate
training data
f(x)
Send queries
Get labels
Learn
surrogate
classifier
f’(x)

12.
http://pralab.diee.unica.it
Experiments on PDF malware detection
• PDF: hierarchy of interconnected objects (keyword/value pairs)
• Adversary’s capability
– adding up to dmax objects to the PDF
– removing objects may
compromise the PDF file
(and embedded malware code)!
12
/Type
2
/Page
1
/Encoding
1
…
13
0
obj
<<
/Kids
[
1
0
R
11
0
R
]
/Type
/Page
...
>>
end
obj
17
0
obj
<<
/Type
/Encoding
/Differences
[
0
/C0032
]
>>
endobj
Features:
keyword
count
min
x'
g(x')− λp(x' | y = −1)
s.t. d(x, x') ≤ dmax
x ≤ x'

13.
http://pralab.diee.unica.it
0 10 20 30 40 50
0
0.2
0.4
0.6
0.8
1
dmax
FN
SVM (Linear), λ=0
PK (C=1)
LK (C=1)
Experiments on PDF malware detection
Linear SVM
13
0 10 20 30 40 50
0
0.2
0.4
0.6
0.8
1
SVM (linear) − C=1, λ=500
dmax
FN
PK
LK
• Dataset: 500 malware samples (Contagio), 500 benign (Internet)
– 5-fold cross-validation
– Targeted (surrogate) classifier trained on 500 (100) samples
• Evasion rate (FN) at FP=1% vs max. number of added keywords
– Perfect knowledge (PK); Limited knowledge (LK)
Without mimicry
λ = 0
With mimicry
λ = 500

14.
http://pralab.diee.unica.it
Experiments on PDF malware detection
SVM with RBF kernel, Neural Network
14
0 10 20 30 40 50
0
0.2
0.4
0.6
0.8
1
Neural Netw. − m=5,λ=500
dmax
FN
PK
LK
0 10 20 30 40 50
0
0.2
0.4
0.6
0.8
1
SVM (RBF) − C=1, γ=1, λ=500
dmax
FN
PK
LK
0 10 20 30 40 50
0
0.2
0.4
0.6
0.8
1
dmax
FN
SVM (RBF), λ=0
PK (C=1)
LK (C=1)
0 10 20 30 40 50
0
0.2
0.4
0.6
0.8
1
dmax
FN
Neural Netw., λ=0
PK (C=1)
LK (C=1)
(m=5)
(m=5)

15.
http://pralab.diee.unica.it
Conclusions and future work
• Related work. Near-optimal evasion of linear and convex-
inducing classifiers (1,2)
• Our work. Linear and non-linear classifiers can be highly
vulnerable to well-crafted evasion attacks
– … even under limited attacker’s knowledge
• Future work
– Evasion of non-differentiable decision functions (decision trees)
– Surrogate data: how to query more efficiently the targeted classifier?
– Practical evasion: feature representation partially known or difficult to
reverse-engineer
– Securing learning: game theory to model classifier vs. adversary
15
(1) D.
Lowd
and
C.
Meek.
Adversarial
learning.
ACM
SIGKDD,
2005.
(2) B.
Nelson,
B.
I.
Rubinstein,
L.
Huang,
A.
D.
Joseph,
S.
J.
Lee,
S.
Rao,
and
J.
D.
Tygar.
Query
strategies
for
evading
convex-­‐inducing
classifiers.
JMLR,
2012.

16.
http://pralab.diee.unica.it
?
16
Any
ques@ons
Thanks
for
your
aWenVon!