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Malware detection using machine learning

Dragos Gavrilut, Mihai Cimpoesu, Dan Anton, Liviu Ciortuz
2009 2009 International Multiconference on Computer Science and Information Technology  
of malware and clean files.  ...  In this paper we present the ideas behind our framework by working firstly with cascade one-sided perceptrons and secondly with cascade kernelized one-sided perceptrons.  ...  ACKNOWLEDGMENTS The authors would like to thank the management staff of BitDefender for their kind support they offered on these issues.  ... 
doi:10.1109/imcsit.2009.5352759 dblp:conf/imcsit/GavrilutCAC09 fatcat:ncdivdjxhnhs3kba4v2anvskda

A Study of Android Malware Detection Techniques and Machine Learning

Balaji Baskaran, Anca Ralescu
2016 Midwest Artificial Intelligence and Cognitive Science Conference  
So given this state of affairs, there is an increasing need for an alternative, really tough malware detection system to complement and rectify the signature based system.  ...  Numerous researches have been conducted which claims that traditional signature based detection system work well up to certain level and malware authors use numerous techniques to evade these tools.  ...  Android Malware Detection Based on the features used to classify an application, we can categorize the analysis as Static and Dynamic. Static analysis is done without running an application.  ... 
dblp:conf/maics/BaskaranR16 fatcat:xcpoc5f63nehjpoelaglakisga

Malware Detection Using Perceptrons and Support Vector Machines

Dragos Gavrilut, Mihai Cimpoesu, Dan Anton, Liviu Ciortuz
2009 2009 Computation World: Future Computing, Service Computation, Cognitive, Adaptive, Content, Patterns  
In this paper we explore the capabilities of a framework that can use different machine learning algorithms to successfully detect malware files, aiming to minimize the number of false positives.  ...  We report the results obtained in our framework, working firstly with cascades of one-sided perceptron and kernelized one-sides perceptrons and secondly with cascade of one-sided support vector machines  ...  ACKNOWLEDGMENTS The authors would like to thank the management staff of BitDefender for the kind support they offered us while working on these issues.  ... 
doi:10.1109/computationworld.2009.85 fatcat:hw25dwglhzforplewk33egqcki

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)  
Android malware have evolved in sophistications and intelligence that they have become highly resistant to existing detection systems especially those that are signature-based.  ...  Machine learning techniques have risen to become a more competent choice for combating the kind of sophistications and novelty deployed by emerging Android malwares.  ...  An anomaly based malware detection framework for the Android platform was proposed in [33] .  ... 
doi:10.3991/ijoe.v16i02.11549 fatcat:hurc3snq6zgzzmrbnhvzafxvxu

A Client/Server Malware Detection Model Based on Machine Learning for Android Devices

Arthur Fournier, Franjieh El Khoury, Samuel Pierre
2021 IoT  
malware detection.  ...  In this paper, we designed and implemented a model for malware detection on Android devices to protect private and financial information, for the mobile applications of the ATISCOM project.  ...  Data Availability Statement: Publicly available datasets for TheZoo [39] and Contagio [40] were analyzed in this study.  ... 
doi:10.3390/iot2030019 fatcat:popqta3cabhrrhgoe3ux5czxce

MLPdf: An Effective Machine Learning Based Approach for PDF Malware Detection [article]

Jason Zhang
2018 arXiv   pre-print
In this paper, we propose a novel approach based on a multilayer perceptron (MLP) neural network model, termed MLPdf, for the detection of PDF based malware.  ...  More specifically, the MLPdf model uses a backpropagation algorithm with stochastic gradient decent search for model update.  ...  CONCLUSION In this paper, we have proposed a novel approach based on a multilayer perceptron neural network model, termed MLP df , for the detection of PDF based malware.  ... 
arXiv:1808.06991v1 fatcat:6n6pllf45jguvh27nvnkw5puwu

Malware Detection at the Microarchitecture Level using Machine Learning Techniques [article]

Abigail Kwan
2020 arXiv   pre-print
Security mechanisms, such as hardware-based malware detection, use machine learning algorithms to classify and detect malware with the aid of Hardware Performance Counters (HPCs) information.  ...  Detection of malware cyber-attacks at the processor microarchitecture level has recently emerged as a promising solution to enhance the security of computer systems.  ...  Conventional signature-based and semantic-based malware detection methods mostly impose significant computational overhead to the system.  ... 
arXiv:2005.12019v1 fatcat:oik6razrajdubpi3cotctjr6nm


Prithvi Chintha, Kakelli Anil Kumar
2020 Zenodo  
We systemized them on various aspects like their objectives, machine learning algorithms used, information about the malware, etc.  ...  From the past decade, various techniques of malware analysis and malware detection have been developed to prevent the efficacy of malware.  ...  Malware Detection using ML methodologies for windows executables:- A detailed methodology contains a proposed malware detection and analysis system specifically used for '.exe' files.  ... 
doi:10.5281/zenodo.4282425 fatcat:3cl22qcjynejpkrhfmotf2kw54

An Efficient Malware Detection System using Hybrid Feature Selection Methods

2019 International Journal of Engineering and Advanced Technology  
The empirical results show that the classification using selected feature set and XGB classifier identifies malware efficiently with an accuracy of 98.9% and 99.26% for the two datasets.  ...  In this paper, we propose a malware detection system using hybrid feature selection approach to detect malware efficiently with a reduced feature set.  ...  [12] proposed an automatic feature selection framework based on reinforcement learning that selects distinguishing feature set for malware detection. Moskovitch et al.  ... 
doi:10.35940/ijeat.a1043.1291s319 fatcat:c45zgr2s7ffplp22hmww5a3ht4

An Adaptive Behavioral-Based Incremental Batch Learning Malware Variants Detection Model using Concept Drift Detection and Sequential Deep Learning

Abdulbasit A. Darem, Fuad A. Ghaleb, Asma A. Al-Hashmi, Jemal H. Abawajy, Sultan M. Alanazi, Afrah Y. AL-Rezami
2021 IEEE Access  
The deep learning approach, which has become the recent trend for constructing the malware detection models, can extract the features automatically and more effectively than the human crafted-based features  ...  The detection model is incrementally updated based on concept drift detection and API traces extracted from both new and old malware samples to avoid the forgetting behavior of the new model. 2) An  ...  Author Name: Preparation of Papers for IEEE Access (February 2017) Author Name: Preparation of Papers for IEEE Access (February 2017) VOLUME XX, 2021 Author Name: Preparation of Papers for IEEE Access  ... 
doi:10.1109/access.2021.3093366 fatcat:avowmx2fc5dylbnncainoq535u

Droiddetector: android malware characterization and detection using deep learning

Zhenlong Yuan, Yongqiang Lu, Yibo Xue
2016 Tsinghua Science and Technology  
We implement an online deep-learning-based Android malware detection engine (DroidDetector) that can automatically detect whether an app is a malware or not.  ...  An evaluation of ten popular anti-virus softwares demonstrates the urgency of advancing our capabilities in Android malware detection.  ...  Acknowledgements We would like to thank Zhen Chen for his insightful feedback and comments.  ... 
doi:10.1109/tst.2016.7399288 fatcat:sj4dvh2xgzbs7bbrvgq4ilgomq

A Lightweight Multi-Source Fast Android Malware Detection Model

Tao Peng, Bochao Hu, Junping Liu, Junjie Huang, Zili Zhang, Ruhan He, Xinrong Hu
2022 Applied Sciences  
to build base models for ensemble learning.  ...  Most of the current malware detection methods running on Android are based on signature and cloud technologies leading to poor protection against new types of malware.  ...  . • Muhammad's method: Static Malware Detection and Attribution in Android Bytecode through an End-to-End Deep System [43] .  ... 
doi:10.3390/app12115394 fatcat:mswfi3youzenxmn22567y2kh7a

Packed malware variants detection using deep belief networks

Zhigang Zhang, Chaowen Chang, Peisheng Han, Hongtao Zhang, J. Joo
2020 MATEC Web of Conferences  
In this paper, we propose a system call based malware detection technology.  ...  To detect unknown variants of malware, many researches have proposed various methods of malware detection based on machine learning in recent years.  ...  [10] proposed an automatic malware detection method by adopting system call and machine learning.  ... 
doi:10.1051/matecconf/202030902002 fatcat:iavdrcsbjbhm7ejzhnwviilzxy

Deep Android Malware Detection

Niall McLaughlin, Adam Doupé, Gail Joon Ahn, Jesus Martinez del Rincon, BooJoong Kang, Suleiman Yerima, Paul Miller, Sakir Sezer, Yeganeh Safaei, Erik Trickel, Ziming Zhao
2017 Proceedings of the Seventh ACM on Conference on Data and Application Security and Privacy - CODASPY '17  
Features indicative of malware are automatically learned by the network from the raw opcode sequence thus removing the need for hand-engineered malware features.  ...  In this paper, we propose a novel android malware detection system that uses a deep convolutional neural network (CNN).  ...  We would like to investigate the design of dataaugmentation schemes appropriate to malware detection.  ... 
doi:10.1145/3029806.3029823 dblp:conf/codaspy/McLaughlinRKYMS17 fatcat:acnnsggbengkxmw3st3zc7adrm

A Survey Paper on Malware Detection Techniques

2021 International Journal of Advanced Trends in Computer Science and Engineering  
The recent growth in new malwares have put a burden on our traditional anti malwares that use signature based or heuristic based techniques to detect malwares as these either cannot detect zero-day malwares  ...  In this survey paper we shall look into how machine learning can potentially be used as an anti-malware  ...  That is, it has to know the malware in prior to detect it. We need to keep updating it in order to keep it functional to new virus. It cannot detect zero-day malwares.  ... 
doi:10.30534/ijatcse/2021/151022021 fatcat:osrf66s4rjcilb5fhtj47sefkq
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