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Empirical Study on Intelligent Android Malware Detection based on Supervised Machine Learning
2020
International Journal of Advanced Computer Science and Applications
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Furthermore, a comprehensive review of the existing static, dynamic, and hybrid Android malware detection approaches is presented in this study. ...
Therefore, the most recently published research studies have suggested machine learning techniques as an alternative method to detect Android malware due to their ability to learn and use the existing ...
Pindroid [17] used a group of permissions and intents supplemented with ensemble methods for accomplishing more accurate malware detection [17] . ...
doi:10.14569/ijacsa.2020.0110429
fatcat:q6au2thucjhjfny3el5vwhhdqy
Towards Accurate Run-Time Hardware-Assisted Stealthy Malware Detection: A Lightweight, Yet Effective Time Series CNN-Based Approach
2021
Cryptography
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In this paper, we first present a comprehensive review of recent advances in hardware-assisted malware detection studies that have used standard ML techniques to detect the malware signatures. ...
, outperforming the detection performance of state-of-the-art HMD and general time series classification methods by up to 42% and 36%, respectively. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/cryptography5040028
fatcat:tdgn54ormvf4tidbwzajazjwky
Android Malware Detection through Machine Learning Techniques: A Review
2020
International Journal of Online and Biomedical Engineering (iJOE)
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This paper provided a comprehensive review of machine learning techniques and their applications in Android malware detection as found in contemporary literature.</p> ...
The models created via machine learning methods work by first learning the existing patterns of malware behaviour and then use this knowledge to separate or identify any such similar behaviour from unknown ...
Each of the algorithms were assessed using performance metric. Their findings revealed that Support Vector Machine and Random Forest provided the best outcomes for malware detection. ...
doi:10.3991/ijoe.v16i02.11549
fatcat:hurc3snq6zgzzmrbnhvzafxvxu
MFDroid: A Stacking Ensemble Learning Framework for Android Malware Detection
2022
Sensors
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based on stacking ensemble learning—MFDroid—to identify Android malware. ...
Considering the poor detection effects of the single feature selection algorithm and the low detection efficiency of traditional machine learning methods, we propose an Android malware detection framework ...
All authors have read and agreed to the published version of the manuscript.
Conflicts of Interest: The author has no relevant interest to disclose. ...
doi:10.3390/s22072597
pmid:35408211
pmcid:PMC9002842
fatcat:r3pj7tqcgraovfejygdhwcb7bm
A Systematic Literature Review of Android Malware Detection Using Static Analysis
2020
IEEE Access
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Then we assess the malware detection capability of static analysis, and we compare the performance of different models in Android malware detection by analyzing the results of empirical evidence. ...
Static analysis is one of these techniques, which is widely applied to Android malware detection and can detect malware quickly and prohibit malware before installation. ...
widely used model is machine learning model in Android malware detection, where ensemble learning takes up the largest proportion.6) RQ2.6: WHICH PERFORMANCE MEASURES ARE USED FOR ANDROID MALWARE DETECTION ...
doi:10.1109/access.2020.3002842
fatcat:uzforojdpndljf6qgscjpgsns4
XMD: An Expansive Hardware-telemetry based Malware Detector to enhance Endpoint Detection
[article]
2022
arXiv
pre-print
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We train and evaluate XMD using hardware telemetries collected from 904 benign applications and 1205 malware samples. ...
Hardware-based Malware Detectors (HMDs) have shown promise in detecting malicious workloads. ...
and Rong Ge. DeepPower: Non-intrusive and deep learning-based
14
detection of IoT malware using power side channels. ...
arXiv:2206.12447v1
fatcat:efmg2oglzbh55ckae3wpnmqsyy
Machine Learning Methods for Detecting Internet-of-Things (IoT) Malware
2021
International Journal of Cognitive Informatics and Natural Intelligence
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This study aims to analyze the performance of machine learning models for detecting Internet of Things malware utilizing a recent IoT dataset. ...
Therefore, the results of this study can help both the researchers and application developers in designing and building intelligent malware detection systems for IoT devices. ...
It evaluated nine supervised machine learning algorithms on the Bot-IoT for the detection of malware. ...
doi:10.4018/ijcini.286768
fatcat:okfhclio5jefnjdbvjj7cbjs2e
Hardware-assisted Machine Learning in Resource-constrained IoT Environments for Security: Review and Future Prospective
2022
IEEE Access
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To protect an IoT infrastructure, various solutions look into hardware-based methods for ML-based IoT authentication, access control, secure offloading, and malware detection schemes. ...
INDEX TERMS AI-based IoT security, Hardware-based machine learning, IoT intrusion detection, Trusted embedded devices I. INTRODUCTION ...
Through use of fewer parameters results in lower calculation costs, which is ideal for real-time, hardware-assisted malware detection [32] [77] . ...
doi:10.1109/access.2022.3179047
fatcat:damwrncpzzbxzamtghwlmrg6v4
Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection
2020
IEEE Access
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That is why, in this paper we first perform a detailed meta-review of the existing surveys related to malware and its detection techniques, showing an arms race between these two sides of a barricade. ...
The survey is concluded with the description of potential future research directions in the field of malware detection. ...
MACHINE LEARNING ATTACKS All modern malware detection technologies are based on the concept of learning by example, the basic mechanism of human learning, and, more generally, of all living species, necessary ...
doi:10.1109/access.2020.3048319
fatcat:tatdk6pzczgp3aylvbxoxabuta
cHybriDroid: A Machine Learning-Based Hybrid Technique for Securing the Edge Computing
2020
Security and Communication Networks
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We collect these hybrid features using permissions, intents, and run-time features (such as information leakage, cryptography's exploitation, and network manipulations) to analyse the effectiveness of ...
The contemporary state-of-the-art model suggests that hybrid features based on machine learning (ML) techniques could play a significant role in android malware detection. ...
Acknowledgments e research was partially supported by Western Norway University of Applied Sciences, Norway. ...
doi:10.1155/2020/8861639
fatcat:kotjbzysynda5d4zt2xickh7jm
Large Iterative Multitier Ensemble Classifiers for Security of Big Data
2014
IEEE Transactions on Emerging Topics in Computing
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These classifiers are very large, but are quite easy to generate and use. They can be so large that it makes sense to use them only for big data. ...
They are generated automatically as a result of several iterations in applying ensemble meta classifiers. ...
ACKNOWLEDGMENT The authors are grateful to three reviewers for thorough reports with comments and corrections that have helped to improve this article. ...
doi:10.1109/tetc.2014.2316510
fatcat:wbmlb2wzfjao5grvu3gyiwsjum
A Review of Android Malware Detection Approaches based on Machine Learning
2020
IEEE Access
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This paper presents a comprehensive survey of Android malware detection approaches based on machine learning. ...
Finally, we assess the future prospects for research into Android malware detection based on machine learning. ...
We briefly introduced the background to Android malware and gave a comprehensive review of machine learning-based approaches for detecting Android malware, arranged roughly in the order of the machine ...
doi:10.1109/access.2020.3006143
fatcat:5rn2qg67ezdixkrefwxmyejhsi
Stimulation and Detection of Android Repackaged Malware with Active Learning
[article]
2018
arXiv
pre-print
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Our evaluation of a sample implementation of Aion using two malware datasets (Malgenome and Piggybacking) shows that active learning can outperform conventional detection techniques and, hence, has great ...
We implemented an architecture, Aion, that connects the processes of stimulating and detecting repackaged malware using a feedback loop depicting active learning. ...
Using a sample implementation of our proposed active learning architecture Aion, we used active learning to stimulate, analyze, and detect Android repackaged malware. ...
arXiv:1808.01186v1
fatcat:afz7t3y62zf7pihyo3ytwvsvhq
Applications in Security and Evasions in Machine Learning: A Survey
2020
Electronics
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ML is used to address serious issues such as real-time attack detection, data leakage vulnerability assessments and many more. ...
ML extensively supports the demanding requirements of the current scenario of security and privacy across a range of areas such as real-time decision-making, big data processing, reduced cycle time for ...
Author Contributions: This survey articles was the result of contributions from all authors more or less equally. All authors have read and agreed to the published version of the manuscript. ...
doi:10.3390/electronics9010097
fatcat:ttmpehdctjhbdk7arxgczl6224
Detecting Malware with an Ensemble Method Based on Deep Neural Network
2018
Security and Communication Networks
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Then MalNet uses CNN and LSTM networks to learn from grayscale image and opcode sequence, respectively, and takes a stacking ensemble for malware classification. ...
accuracy and achieves a considerable speed-up on detecting efficiency comparing with two state-of-the-art results on Microsoft malware dataset. ...
Acknowledgments This work is partially supported by the National Natural Science Foundation of China under Grant no. 61672421. ...
doi:10.1155/2018/7247095
fatcat:tmyysltalvhu7olp3ajynd2aqe
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