Signal Processing - March 2016 - 109
previously applied for malware classification. We use the SRC framework
to identify the malware family of a test
sample and compare it with NN classification that we previously used in [6].
We vary the projected dimensions from
48 to 512, which are consistent for both
RP and GIST. In our experiments, we
choose 80% of a data set for training and
minimization. The overall approach is
shown in Figure 6.
We test our technique on two public malware data sets: the Malimg data
set [8] and the Malheur data set [9]. On
both data sets, we select equal number
of samples to reduce any bias toward a
particular family. For comparison, we
use GIST descriptors, which we had
20% for testing. On both the Malimg
data set [Figure 7(a)] and the Malheur
data set [Figure 7(b)], the best accuracy
is obtained for the combination of RPs
and the SRC classification framework
(92.83% for Malimg and 98.55% for
Malheur). The projected dimension is
512 from higher dimensions of 840,960
(Malimg) and 3,364,864 (Malheur).
AV Label Database
Initial Phase
Get AV Labels
From Virustotal
Tree Indices
Build
Ball Tree
for Fast NN
Compute
Fingerprints
Malware
Benign
Query Phase
New
Sample
Compute
Fingerprint
Retrieve
Top Matches
Malware
Very High Confidence
Malware
Very High Confidence
Malware
High Confidence
Malware
Low Confidence
Benign
Very Low Confidence
figuRE 5. The block schematic of SARVAM: In the initial phase, the image similarity descriptors and AV labels are computed and stored in a database. In
the query phase, the NNs along with their corresponding AV labels are retrieved.
Signal
Representation
Malware Data
Sparse Modeling
α1
=
RA1
RAN
.
.
.
D×1
D×N
u1
u2
.
.
.
α2
.
.
.
α1
α2
=
A1A2
.
.
.
w1
.
.
.
wD
uM
RPs
×
M×1
αN
RA
.
.
.
αN
N×1
w
AN
α
u
A
N×1
α
figuRE 6. The SRC framework for malware classification.
IEEE Signal Processing Magazine
|
March 2016
|
109
Table of Contents for the Digital Edition of Signal Processing - March 2016
Signal Processing - March 2016 - Cover1
Signal Processing - March 2016 - Cover2
Signal Processing - March 2016 - 1
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Signal Processing - March 2016 - Cover3
Signal Processing - March 2016 - Cover4
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