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1. Cyber Code Intelligence for Android
Malware Detection
Abstract
Evolving Android malware poses a severe security threat to mobile users, and
machine-learning (ML)-based defense techniques attract active research. Due
to the lack of knowledge, many zero
undetected until the classifier ga
ML-based methods will take a long time to learn new malware families in the
latest malware family landscape. Existing ML
detection and classification methods struggle with the fast evolutio
malware landscape, particularly in terms of the emergence of zero
malware families and limited representation of single
article, a new multiview feature intelligence (MFI) framework is developed to
learn the representation of a targeted capability from known malware families
for recognizing unknown and evolving malware with the same capability. The
new framework performs reverse engineering to extract multiview
Cyber Code Intelligence for Android
Malware Detection
Evolving Android malware poses a severe security threat to mobile users, and
based defense techniques attract active research. Due
to the lack of knowledge, many zero-day families’ malware may remain
undetected until the classifier gains specialized knowledge. The most existing
based methods will take a long time to learn new malware families in the
latest malware family landscape. Existing ML-based Android malware
detection and classification methods struggle with the fast evolutio
malware landscape, particularly in terms of the emergence of zero
malware families and limited representation of single-view features. In this
article, a new multiview feature intelligence (MFI) framework is developed to
on of a targeted capability from known malware families
for recognizing unknown and evolving malware with the same capability. The
new framework performs reverse engineering to extract multiview
Cyber Code Intelligence for Android
Evolving Android malware poses a severe security threat to mobile users, and
based defense techniques attract active research. Due
day families’ malware may remain
ins specialized knowledge. The most existing
based methods will take a long time to learn new malware families in the
based Android malware
detection and classification methods struggle with the fast evolution of the
malware landscape, particularly in terms of the emergence of zero-day
view features. In this
article, a new multiview feature intelligence (MFI) framework is developed to
on of a targeted capability from known malware families
for recognizing unknown and evolving malware with the same capability. The
new framework performs reverse engineering to extract multiview

2. heterogeneous features, including semantic string features, API call graph
features, and smali opcode sequential features. It can learn the representation
of a targeted capability from known malware families through a series of
processes of feature analysis, selection, aggregation, and encoding, to detect
unknown Android malware with shared target capability. We create a new
dataset with ground-truth information regarding capability. Many experiments
are conducted on the new dataset to evaluate the performance and
effectiveness of the new method. The results demonstrate that the new
method outperforms three state-of-the-art methods, including: 1) Drebin; 2)
MaMaDroid; and 3) N -opcode, when detecting unknown Android malware
with targeted capabilities.