Malicious Code Detection Based on Code Semantic Features

Li, Binglong

doi:10.1109/access.2020.3026052

Cited by 14 publications

(6 citation statements)

References 22 publications

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“…For this purpose, graphical processing units and cloud instances can be used. Zhang and Li [167] proposed a code semantic feature-based malware detection approach.…”

Section: Android Malware Classification and Identification Usingmentioning

confidence: 99%

A Comprehensive Review of Android Security: Threats, Vulnerabilities, Malware Detection, and Analysis

Acharya

Rawat

Bhatnagar

2022

Security and Communication Networks

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The popularity and open-source nature of Android devices have resulted in a dramatic growth of Android malware. Malware developers are also able to evade the detection methods, reducing the efficiency of malware detection techniques. It is hence desirable that security researchers and experts come up with novel and more efficient methods to analyze existing and zero-day Android malware. Most of the researchers have focused on Android system security. However, to examine Android security, with a specific focus on malware development, investigation of malware prevention techniques and already known malware detection techniques needs a broad inclusion. To overcome the research gaps, this paper provides a broad review of current Android security concerns, security implementation enhancements, significant malware detected during 2017–2021, and stealth procedures used by the malware developers along with the current Android malware detection techniques. A comparative analysis is presented between this article and similar recent survey articles to fill the existing research gaps. In the end, a three-phase model is proposed to efficiently identify and characterize Android malware. In the first phase, a lightweight deep transfer learning approach is used to classify Android applications into benign and malicious. In the second phase, the malicious applications are executed in a virtual emulator to reduce the number of false positives. Finally, the malicious applications having the same characteristic ratio are grouped into their corresponding families using the topic modelling approach. The proposed model can efficiently detect, characterize, and provide a familial classification of Android malware with a good accuracy rate.

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“…For this purpose, graphical processing units and cloud instances can be used. Zhang and Li [167] proposed a code semantic feature-based malware detection approach.…”

Section: Android Malware Classification and Identification Usingmentioning

confidence: 99%

A Comprehensive Review of Android Security: Threats, Vulnerabilities, Malware Detection, and Analysis

Acharya

Rawat

Bhatnagar

2022

Security and Communication Networks

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“…Feature Vector space is segregated using clustering and later random forest applied and achieved good results. Y. Zhang [5] proposed a Graph Convolutional Network for android malware detection. A tool named "SOOT" was used for analyzing and getting data flow chains.…”

Section: Literature Reviewmentioning

confidence: 99%

Android Malware Detection with Deep Learning using RNN from Opcode Sequences

Lakshmanarao

Shashi

2022

Int. J. Interact. Mob. Technol.

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Android is the most widely used operating system in smartphones. Mobile users can download and access apps easily from the play store. Due to lack of security awareness and risk associated with mobile apps, malware apps would be downloaded by normal users in general. The consequences after installing a malware app are unpredictable. Malware apps can gather user personal data, browsing history, user profiles, user sensitive data like passwords. Hence, android malware detection is essential for providing security to mobile users. Android malware detection using machine learning is done either by extracting static features (opcodes, permissions, intents, system commands) or by extracting dynamic features (log behavior, system calls, dataflow). In this paper, opcode sequences are extracted from malware and benign apps, and Recurrent Neural Networks are proposed on extracted sequences. Benign apps are collected from the play store, apkpure.com and malware apps are collected from the virus share website. The proposed Recurrent Neural Network model could achieve 96% accuracy for android malware detection.

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“…In the approach, virtual machine memory snapshot image of running malware and benign is captured and memory images converted to grayscale images, which is used for training and testing on the CNN-based model. Zhang et al [18] present an approach for malware classification. The approach is based on data flow analysis to extract semantic structure features of the code and the graph convolutional networks (GCNs) for detection.…”

Section: Related Workmentioning

confidence: 99%

A Novel Framework for Windows Malware Detection Using a Deep Learning Approach

Darem¹

2022

Computers, Materials &Amp; Continua

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Malicious software (malware) is one of the main cyber threats that organizations and Internet users are currently facing. Malware is a software code developed by cybercriminals for damage purposes, such as corrupting the system and data as well as stealing sensitive data. The damage caused by malware is substantially increasing every day. There is a need to detect malware efficiently and automatically and remove threats quickly from the systems. Although there are various approaches to tackle malware problems, their prevalence and stealthiness necessitate an effective method for the detection and prevention of malware attacks. The deep learning-based approach is recently gaining attention as a suitable method that effectively detects malware. In this paper, a novel approach based on deep learning for detecting malware proposed. Furthermore, the proposed approach deploys novel feature selection, feature co-relation, and feature representations to significantly reduce the feature space. The proposed approach has been evaluated using a Microsoft prediction dataset with samples of 21,736 malware composed of 9 malware families. It achieved 96.01% accuracy and outperformed the existing techniques of malware detection.

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Malicious Code Detection Based on Code Semantic Features

Cited by 14 publications

References 22 publications

A Comprehensive Review of Android Security: Threats, Vulnerabilities, Malware Detection, and Analysis

A Comprehensive Review of Android Security: Threats, Vulnerabilities, Malware Detection, and Analysis

Android Malware Detection with Deep Learning using RNN from Opcode Sequences

A Novel Framework for Windows Malware Detection Using a Deep Learning Approach

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