Vision-Based Malware Detection and Classification Using Lightweight Deep Learning Paradigm

2020 ◽  
pp. 62-73
Author(s):  
S. Abijah Roseline ◽  
G. Hari ◽  
S. Geetha ◽  
R. Krishnamurthy
Keyword(s):  
Deep Learning ◽  
Malware Detection ◽  
Learning Paradigm ◽  
Malware Detection And Classification

Android Malware Detection Techniques: A Literature Review

2020 ◽  
Vol 14 ◽  
Author(s):  
Meghna Dhalaria ◽  
Ekta Gandotra
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Malware Detection ◽  
Future Research ◽  
Android Malware ◽  
Detection Techniques ◽  
Android Malware Detection ◽  
Future Research Directions ◽  
To Come ◽  
Tools And Techniques

Purpose: This paper provides the basics of Android malware, its evolution and tools and techniques for malware analysis. Its main aim is to present a review of the literature on Android malware detection using machine learning and deep learning and identify the research gaps. It provides the insights obtained through literature and future research directions which could help researchers to come up with robust and accurate techniques for classification of Android malware. Design/Methodology/Approach: This paper provides a review of the basics of Android malware, its evolution timeline and detection techniques. It includes the tools and techniques for analyzing the Android malware statically and dynamically for extracting features and finally classifying these using machine learning and deep learning algorithms. Findings: The number of Android users is expanding very fast due to the popularity of Android devices. As a result, there are more risks to Android users due to the exponential growth of Android malware. On-going research aims to overcome the constraints of earlier approaches for malware detection. As the evolving malware are complex and sophisticated, earlier approaches like signature based and machine learning based are not able to identify these timely and accurately. The findings from the review shows various limitations of earlier techniques i.e. requires more detection time, high false positive and false negative rate, low accuracy in detecting sophisticated malware and less flexible. Originality/value: This paper provides a systematic and comprehensive review on the tools and techniques being employed for analysis, classification and identification of Android malicious applications. It includes the timeline of Android malware evolution, tools and techniques for analyzing these statically and dynamically for the purpose of extracting features and finally using these features for their detection and classification using machine learning and deep learning algorithms. On the basis of the detailed literature review, various research gaps are listed. The paper also provides future research directions and insights which could help researchers to come up with innovative and robust techniques for detecting and classifying the Android malware.

scholarly journals Expert camouflage-breakers can accurately localize search targets

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Fallon Branch ◽  
Allison JoAnna Lewis ◽  
Isabella Noel Santana ◽  
Jay Hegdé
Keyword(s):  
Deep Learning ◽  
Visual Search ◽  
Target Localization ◽  
Specific Training ◽  
Learning Paradigm ◽  
Accuracy And Precision ◽  
Per Se ◽  
Special Case

AbstractCamouflage-breaking is a special case of visual search where an object of interest, or target, can be hard to distinguish from the background even when in plain view. We have previously shown that naive, non-professional subjects can be trained using a deep learning paradigm to accurately perform a camouflage-breaking task in which they report whether or not a given camouflage scene contains a target. But it remains unclear whether such expert subjects can actually detect the target in this task, or just vaguely sense that the two classes of images are somehow different, without being able to find the target per se. Here, we show that when subjects break camouflage, they can also localize the camouflaged target accurately, even though they had received no specific training in localizing the target. The localization was significantly accurate when the subjects viewed the scene as briefly as 50 ms, but more so when the subjects were able to freely view the scenes. The accuracy and precision of target localization by expert subjects in the camouflage-breaking task were statistically indistinguishable from the accuracy and precision of target localization by naive subjects during a conventional visual search where the target ‘pops out’, i.e., is readily visible to the untrained eye. Together, these results indicate that when expert camouflage-breakers detect a camouflaged target, they can also localize it accurately.

scholarly journals An Efficient DenseNet-Based Deep Learning Model for Malware Detection

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 344
Author(s):  
Jeyaprakash Hemalatha ◽  
S. Abijah Roseline ◽  
Subbiah Geetha ◽  
Seifedine Kadry ◽  
Robertas Damaševičius
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Malware Detection ◽  
Class Imbalance ◽  
Learning Model ◽  
Computational Time ◽  
Learning Approaches ◽  
Security Threat ◽  
Static And Dynamic Analysis ◽  
Deep Learning Model

Recently, there has been a huge rise in malware growth, which creates a significant security threat to organizations and individuals. Despite the incessant efforts of cybersecurity research to defend against malware threats, malware developers discover new ways to evade these defense techniques. Traditional static and dynamic analysis methods are ineffective in identifying new malware and pose high overhead in terms of memory and time. Typical machine learning approaches that train a classifier based on handcrafted features are also not sufficiently potent against these evasive techniques and require more efforts due to feature-engineering. Recent malware detectors indicate performance degradation due to class imbalance in malware datasets. To resolve these challenges, this work adopts a visualization-based method, where malware binaries are depicted as two-dimensional images and classified by a deep learning model. We propose an efficient malware detection system based on deep learning. The system uses a reweighted class-balanced loss function in the final classification layer of the DenseNet model to achieve significant performance improvements in classifying malware by handling imbalanced data issues. Comprehensive experiments performed on four benchmark malware datasets show that the proposed approach can detect new malware samples with higher accuracy (98.23% for the Malimg dataset, 98.46% for the BIG 2015 dataset, 98.21% for the MaleVis dataset, and 89.48% for the unseen Malicia dataset) and reduced false-positive rates when compared with conventional malware mitigation techniques while maintaining low computational time. The proposed malware detection solution is also reliable and effective against obfuscation attacks.

scholarly journals AFCGDroid: Deep Learning Based Android Malware Detection Using Attributed Function Call Graphs

2020 ◽  
Vol 1693 ◽  
pp. 012080
Author(s):  
Tong Lu ◽  
Xiaoyuan Liu ◽  
Jingwei Chen ◽  
Naitian Hu ◽  
Bo Liu
Keyword(s):  
Deep Learning ◽  
Malware Detection ◽  
Android Malware ◽  
Android Malware Detection ◽  
Function Call ◽  
Call Graphs

An Effective Approach for Malware Detection and Explanation via Deep Learning Analysis

2021 ◽  
Author(s):  
Huozhu Wang ◽  
Ziyuan Zhu ◽  
Zhongkai Tong ◽  
Xiang Yin ◽  
Yusi Feng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Malware Detection ◽  
Learning Analysis

scholarly journals A Multimodal Deep Learning Method for Android Malware Detection Using Various Features

2019 ◽  
Vol 14 (3) ◽  
pp. 773-788 ◽  
Author(s):  
TaeGuen Kim ◽  
BooJoong Kang ◽  
Mina Rho ◽  
Sakir Sezer ◽  
Eul Gyu Im
Keyword(s):  
Deep Learning ◽  
Malware Detection ◽  
Learning Method ◽  
Android Malware ◽  
Android Malware Detection
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