scholarly journals DMIA : A Malware Detection System on IOS Platform

2016 ◽  
Author(s):  
Hongliang Liang ◽  
Yilun Xie ◽  
Yan Song
Keyword(s):  
Detection System ◽  
Malware Detection

scholarly journals Malware Detection Based on Code Visualization and Two-Level Classification

Information ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 118
Author(s):  
Vassilios Moussas ◽  
Antonios Andreatos
Keyword(s):  
Web Applications ◽  
Detection System ◽  
Malware Detection ◽  
Image Features ◽  
Malicious Code ◽  
Malware Analysis ◽  
Malicious Software ◽  
Antivirus Software ◽  
Malware Classification ◽  
Artificial Neural Network Ann

Malware creators generate new malicious software samples by making minor changes in previously generated code, in order to reuse malicious code, as well as to go unnoticed from signature-based antivirus software. As a result, various families of variations of the same initial code exist today. Visualization of compiled executables for malware analysis has been proposed several years ago. Visualization can greatly assist malware classification and requires neither disassembly nor code execution. Moreover, new variations of known malware families are instantly detected, in contrast to traditional signature-based antivirus software. This paper addresses the problem of identifying variations of existing malware visualized as images. A new malware detection system based on a two-level Artificial Neural Network (ANN) is proposed. The classification is based on file and image features. The proposed system is tested on the ‘Malimg’ dataset consisting of the visual representation of well-known malware families. From this set some important image features are extracted. Based on these features, the ANN is trained. Then, this ANN is used to detect and classify other samples of the dataset. Malware families creating a confusion are classified by a second level of ANNs. The proposed two-level ANN method excels in simplicity, accuracy, and speed; it is easy to implement and fast to run, thus it can be applied to antivirus software, smart firewalls, web applications, etc.

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.

Malware Detection System by Payload Analysis of Network Traffic

2015 ◽  
Vol 13 (3) ◽  
pp. 850-855 ◽  
Author(s):  
L.J.G. Villalba ◽  
A.L.S. Orozco ◽  
J.M. Vidal
Keyword(s):  
Network Traffic ◽  
Detection System ◽  
Malware Detection

An Android Malware Detection System Based on Feature Fusion

2018 ◽  
Vol 27 (6) ◽  
pp. 1206-1213 ◽  
Author(s):  
Jian Li ◽  
Zheng Wang ◽  
Tao Wang ◽  
Jinghao Tang ◽  
Yuguang Yang ◽  
...  
Keyword(s):  
Feature Fusion ◽  
Detection System ◽  
Malware Detection ◽  
Android Malware ◽  
Android Malware Detection

scholarly journals Macro Based Malware Detection System

2021 ◽  
Vol 12 (3) ◽  
pp. 5776-5787
Author(s):  
Balal Sohail Et. al.
Keyword(s):  
Detection System ◽  
Malware Detection ◽  
System Research ◽  
Text File ◽  
Technical Research ◽  
Antivirus Software ◽  
Development Cycle ◽  
Executable File ◽  
Tools And Techniques ◽  
The Way

Macro based Malware has taken a great rise is these recent years, Attackers are now using this malware for hacking purposes. This virus is embedded inside the macro of a word document and can be used to infect the victim’s machine. These infected files are usually sent through emails and all antivirus software are unable to detect the virus due to the format of the file. Due to the format being a rich text file and not an executable file, the infected file is able to bypass all security. Hence it is necessary to develop a detection system for such attacks to help reduce the threat. Technical research is carried out to identify the tools and techniques essential in the completion of this system. Research on methodology is done to finalise which development cycle will be used and how functions will be carried out at each phase of the development cycle. This paper outlines the problems that people face once they are attacked through macro malwares and the way it can be mitigated. Lastly, all information necessary to start the implementation has been gathered and analysed

scholarly journals Anomaly Network Based Malware Detection System Using Hybrid Techniques

2020 ◽  
Vol 9 (3) ◽  
Author(s):  
Onyedeke Obinna Cyril ◽  
Agubata Immaculate Chidimma ◽  
Ihedioha Uchechi Michael ◽  
Ezema Modesta
Keyword(s):  
Detection System ◽  
Malware Detection ◽  
Hybrid Techniques
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