VisDroid: Android malware classification based on local and global image features, bag of visual words and machine learning techniques

2020 ◽  
Author(s):  
Khaled Bakour ◽  
Halil Murat Ünver
Keyword(s):  
Machine Learning ◽  
Image Features ◽  
Machine Learning Techniques ◽  
Bag Of Visual Words ◽  
Android Malware ◽  
Visual Words ◽  
Malware Classification ◽  
Learning Techniques

scholarly journals Delving into Android Malware Families with a Novel Neural Projection Method

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Rafael Vega Vega ◽  
Héctor Quintián ◽  
Carlos Cambra ◽  
Nuño Basurto ◽  
Álvaro Herrero ◽  
...  
Keyword(s):  
Machine Learning ◽  
Projection Method ◽  
Hebbian Learning ◽  
Real Life ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Android Malware ◽  
Learning Techniques ◽  
First Time

Present research proposes the application of unsupervised and supervised machine-learning techniques to characterize Android malware families. More precisely, a novel unsupervised neural-projection method for dimensionality-reduction, namely, Beta Hebbian Learning (BHL), is applied to visually analyze such malware. Additionally, well-known supervised Decision Trees (DTs) are also applied for the first time in order to improve characterization of such families and compare the original features that are identified as the most important ones. The proposed techniques are validated when facing real-life Android malware data by means of the well-known and publicly available Malgenome dataset. Obtained results support the proposed approach, confirming the validity of BHL and DTs to gain deep knowledge on Android malware.

scholarly journals Android Malware Detection through Machine Learning Techniques: A Review

2020 ◽  
Vol 16 (02) ◽  
pp. 14
Author(s):  
Abikoye Oluwakemi Christiana ◽  
Benjamin Aruwa Gyunka ◽  
Akande Noah
Keyword(s):  
Machine Learning ◽  
Malware Detection ◽  
Social Engineering ◽  
Machine Learning Techniques ◽  
Android Malware ◽  
Detection Systems ◽  
Android Malware Detection ◽  
Android Os ◽  
Learning Techniques ◽  
The Individual

<p class="0abstract">The open source nature of Android Operating System has attracted wider adoption of the system by multiple types of developers. This phenomenon has further fostered an exponential proliferation of devices running the Android OS into different sectors of the economy. Although this development has brought about great technological advancements and ease of doing businesses (e-commerce) and social interactions, they have however become strong mediums for the uncontrolled rising cyberattacks and espionage against business infrastructures and the individual users of these mobile devices. Different cyberattacks techniques exist but attacks through malicious applications have taken the lead aside other attack methods like social engineering. Android malware have evolved in sophistications and intelligence that they have become highly resistant to existing detection systems especially those that are signature-based. Machine learning techniques have risen to become a more competent choice for combating the kind of sophistications and novelty deployed by emerging Android malwares. The models created via machine learning methods work by first learning the existing patterns of malware behaviour and then use this knowledge to separate or identify any such similar behaviour from unknown attacks. This paper provided a comprehensive review of machine learning techniques and their applications in Android malware detection as found in contemporary literature.</p>

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