Malware detection with dynamic evolving graph convolutional networks
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.22880
Abstract: Malware detection is a vital task for cybersecurity. For malware dynamic behavior, threats come from a small number of Application Programming Interfaces (APIs) embedded in the API sequences, which are easily ignored or obfuscated in… read more here.
Keywords: api level; malware detection; dynamic evolving; level ... See more keywords
HEMD: a highly efficient random forest-based malware detection framework for Android
Sign Up to like & getrecommendations! Published in 2017 at "Neural Computing and Applications"
DOI: 10.1007/s00521-017-2914-y
Abstract: Mobile phones are rapidly becoming the most widespread and popular form of communication; thus, they are also the most important attack target of malware. The amount of malware in mobile phones is increasing exponentially and… read more here.
Keywords: malware detection; detection; proposed method; hemd highly ... See more keywords
A novel permission-based Android malware detection system using feature selection based on linear regression
Sign Up to like & getrecommendations! Published in 2021 at "Neural Computing and Applications"
DOI: 10.1007/s00521-021-05875-1
Abstract: With the developments in mobile and wireless technology, mobile devices have become an important part of our lives. While Android is the leading operating system in market share, it is the platform most targeted by… read more here.
Keywords: system; android malware; malware detection; selection ... See more keywords
Improvement of malware detection and classification using API call sequence alignment and visualization
Sign Up to like & getrecommendations! Published in 2017 at "Cluster Computing"
DOI: 10.1007/s10586-017-1110-2
Abstract: Conventional malware detection technologies have the limitation to detect malware because recent malware uses a variety of the avoidance techniques such as obfuscation, packing, anti-virtualization, anti-emulation, encapsulation technology in order to evade the detection of… read more here.
Keywords: malware detection; detection; api call; detection classification ... See more keywords
Feature Selection for Malware Detection on the Android Platform Based on Differences of IDF Values
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Computer Science and Technology"
DOI: 10.1007/s11390-020-9323-x
Abstract: Android is the mobile operating system most frequently targeted by malware in the smartphone ecosystem, with a market share significantly higher than its competitors and a much larger total number of applications. Detection of malware… read more here.
Keywords: malware detection; android platform; malware; feature selection ... See more keywords
Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Ambient Intelligence and Humanized Computing"
DOI: 10.1007/s12652-018-0803-6
Abstract: Android security incidents occurred frequently in recent years. To improve the accuracy and efficiency of large-scale Android malware detection, in this work, we propose a hybrid model based on deep autoencoder (DAE) and convolutional neural… read more here.
Keywords: android malware; model; deep autoencoder; cnn ... See more keywords
Android malware detection method based on bytecode image
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Ambient Intelligence and Humanized Computing"
DOI: 10.1007/s12652-020-02196-4
Abstract: Traditional machine learning based malware detection methods often use decompiling techniques or dynamic monitoring techniques to extract the feature representation of malware. This procedure is time consuming and strongly depends on the skills of experts.… read more here.
Keywords: malware detection; detection; detection method; method based ... See more keywords
End-to-end malware detection for android IoT devices using deep learning
Sign Up to like & getrecommendations! Published in 2020 at "Ad Hoc Networks"
DOI: 10.1016/j.adhoc.2020.102098
Abstract: Abstract The Internet of Things (IoT) has grown rapidly in recent years and has become one of the most active areas in the global market. As an open source platform with a large number of… read more here.
Keywords: malware detection; detection; deep learning; malware ... See more keywords
Malware detection based on visualization of recombined API instruction sequence
Sign Up to like & getrecommendations! Published in 2022 at "Connection Science"
DOI: 10.1080/09540091.2022.2139353
Abstract: This paper introduces a malware detection method based on the reorganisation of API instruction sequence and image representation in an effort to address the challenges posed by current methods of malware detection in terms of… read more here.
Keywords: instruction sequence; detection; malware detection; api instruction ... See more keywords
A Novel Dynamic Android Malware Detection System With Ensemble Learning
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DOI: 10.1109/access.2018.2844349
Abstract: With the popularity of Android smartphones, malicious applications targeted Android platform have explosively increased. Proposing effective Android malware detection method for preventing the spread of malware has become an emerging issue. Various features extracted through… read more here.
Keywords: ensemble learning; android malware; malware detection; malware ... See more keywords
IoMT Malware Detection Approaches: Analysis and Research Challenges
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2960412
Abstract: The advancement in Information and Communications Technology (ICT) has changed the entire paradigm of computing. Because of such advancement, we have new types of computing and communication environments, for example, Internet of Things (IoT) that… read more here.
Keywords: iot; research challenges; environment; malware detection ... See more keywords