422 Hits in 9.7 sec

Empirical Study on Intelligent Android Malware Detection based on Supervised Machine Learning

Talal A.A Abdullah, Waleed Ali, Rawad Abdulghafor
2020 International Journal of Advanced Computer Science and Applications  
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

Hossein Sayadi, Yifeng Gao, Hosein Mohammadi Makrani, Jessica Lin, Paulo Cesar Costa, Setareh Rafatirad, Houman Homayoun
2021 Cryptography  
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

Abikoye Oluwakemi Christiana, Benjamin Aruwa Gyunka, Akande Noah
2020 International Journal of Online and Biomedical Engineering (iJOE)  
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

Xusheng Wang, Linlin Zhang, Kai Zhao, Xuhui Ding, Mingming Yu
2022 Sensors  
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

Ya Pan, Xiuting Ge, Chunrong Fang, Yong Fan
2020 IEEE Access  
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]

Harshit Kumar, Biswadeep Chakraborty, Sudarshan Sharma, Nikhil Chawla, Saibal Mukhopadhyay
2022 arXiv   pre-print
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  
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

Georgios Kornaros
2022 IEEE Access  
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

Luca Caviglione, Michal Choras, Igino Corona, Artur Janicki, Wojciech Mazurczyk, Marek Pawlicki, Katarzyna Wasielewska
2020 IEEE Access  
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

Afifa Maryam, Usman Ahmed, Muhammad Aleem, Jerry Chun-Wei Lin, Muhammad Arshad Islam, Muhammad Azhar Iqbal, Gautam Srivastava
2020 Security and Communication Networks  
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

Jemal H. Abawajy, Andrei Kelarev, Morshed Chowdhury
2014 IEEE Transactions on Emerging Topics in Computing  
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

Kaijun Liu, Shengwei Xu, Guoai Xu, Miao Zhang, Dawei Sun, Haifeng Liu
2020 IEEE Access  
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]

Aleieldin Salem
2018 arXiv   pre-print
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

Ramani Sagar, Rutvij Jhaveri, Carlos Borrego
2020 Electronics  
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

Jinpei Yan, Yong Qi, Qifan Rao
2018 Security and Communication Networks  
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
« Previous Showing results 1 — 15 out of 422 results