An Effective Ensemble Deep Learning Framework for Malware Detection

2018 ◽  
Author(s):  
Dinh Viet Sang ◽  
Dang Manh Cuong ◽  
Le Tran Bao Cuong
Keyword(s):  
Deep Learning ◽  
Malware Detection ◽  
Learning Framework

DeepAM: a heterogeneous deep learning framework for intelligent malware detection

2017 ◽  
Vol 54 (2) ◽  
pp. 265-285 ◽  
Author(s):  
Yanfang Ye ◽  
Lingwei Chen ◽  
Shifu Hou ◽  
William Hardy ◽  
Xin Li
Keyword(s):  
Deep Learning ◽  
Malware Detection ◽  
Learning Framework

scholarly journals Deep-Droid: Deep Learning for Android Malware Detection

2020 ◽  
Vol 9 (12) ◽  
pp. 122-125
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Malware Detection ◽  
Learning Approaches ◽  
Android Malware ◽  
Detection Systems ◽  
Learning Framework ◽  
The Past ◽  
Android Malware Detection ◽  
Android Os

Android OS, which is the most prevalent operating system (OS), has enjoyed immense popularity for smart phones over the past few years. Seizing this opportunity, cybercrime will occur in the form of piracy and malware. Traditional detection does not suffice to combat newly created advanced malware. So, there is a need for smart malware detection systems to reduce malicious activities risk. Machine learning approaches have been showing promising results in classifying malware where most of the method are shallow learners like Random Forest (RF) in recent years. In this paper, we propose Deep-Droid as a deep learning framework, for detection Android malware. Hence, our Deep-Droid model is a deep learner that outperforms exiting cutting-edge machine learning approaches. All experiments performed on two datasets (Drebin-215 & Malgenome-215) to assess our Deep-Droid model. The results of experiments show the effectiveness and robustness of Deep-Droid. Our Deep-Droid model achieved accuracy over 98.5%.

scholarly journals DLSTM-HHO: Enhanced Deep Learning Framework for Malware Detection at the Edge of the Iot System

2021 ◽  
Author(s):  
Santhadevi D ◽  
B
Keyword(s):  
Performance Evaluation ◽  
Deep Learning ◽  
Internet Of Things ◽  
Cyber Security ◽  
Malware Detection ◽  
Manufacturing Industries ◽  
Apache Spark ◽  
Learning Framework ◽  
Proposed Model ◽  
Confidential Data

Abstract Internet of Things (IoT) technology has a dynamic atmosphere due to incorporating multiple smart peripherals, which provide autonomous homes, cities, manufacturing industries, medical domain, etc.; however, a threat by cyber security is still at constant risk, and it gets much attention in researches. Cyber issues in the IoT environment are usually coming due to intruder’s malware activity. This kind of malware affects the confidential data of users in the IoT environment. In this research, a novel framework is implemented with the association of an improved deep LSTM with Harris Hawk Optimization (DLSTM-HHO). This framework is highly improved by adopting an Apache Spark technique for pre-processing IoT dataset. An Apache Spark replaces the traditional data pre-processing, which provides more efficiency to this model for detecting the malware at the edge of the IoT environment. The implementation of this framework is done in the MATLAB2020a platform with Apache Spark. The proposed model provides better performance evaluation in terms of accuracy is at 98%, and the F1-Score at 98.5%.

A Deep Learning Framework for Prediction of Retinal Disorders

2020 ◽  
Author(s):  
Raniyaharini R ◽  
Madhumitha K ◽  
Mishaa S ◽  
Virajaravi R
Keyword(s):  
Deep Learning ◽  
Learning Framework ◽  
Retinal Disorders

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.

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