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Dynamic Analysis for IoT Malware Detection with Convolution Neural Network model

Jueun Jeon, Jong Hyuk Park, Young-Sik Jeong
2020 IEEE Access  
INDEX TERMS Cloud-based malware detection, convolution neural network, dynamic analysis, IoT malware, malware detection.  ...  The DAIMD scheme learns IoT malware using the convolution neural network (CNN) model and analyzes IoT malware dynamically in nested cloud environment.  ...  a vast amount of behavior data based on a convolution neural network (CNN) model.  ... 
doi:10.1109/access.2020.2995887 fatcat:sjch2uh54ja2xapedtkwddwgiq

The trend malware source of IoT network

Susanto Susanto, M. Agus Syamsul Arifin, Deris Stiawan, Mohd. Yazid Idris, Rahmat Budiarto
2021 Indonesian Journal of Electrical Engineering and Computer Science  
Research works related to the development of IoT malware detection have been carried out with various methods and algorithms to increase detection accuracy.  ...  Therefore, malware detection in the IoT system/network becomes an important issue.  ...  From the results of literature studies, the most widely used malware detection method is deep learning with the convolutional neural network algorithm.  ... 
doi:10.11591/ijeecs.v22.i1.pp450-459 fatcat:bvgmqkelrnf2fordoanlqbjpui

Cyber Security Threats detection in Internet of Things using Deep Learning approach

Farhan Ullah, Hamad Naeem, Sohail Jabbar, Shehzad Khalid, Muhammad Ahsan Latif, Fadi Al-Turjman, Leonardo Mostarda
2019 IEEE Access  
Apart from this, the deep convolutional neural network is used to detect malicious infections in IoT network through color image visualization.  ...  In this paper, we have proposed a combined deep learning approach to detect the pirated software and malware-infected files across the IoT network.  ...  Secondly, we proposed a novel methodology based on convolution neural network and color image visualization to detect malware using IoT.  ... 
doi:10.1109/access.2019.2937347 fatcat:l5fhaizpbfee3gv4jlyhw7e2b4

Collective Intelligence: Decentralized Learning for Android Malware Detection in IoT with Blockchain [article]

Rajesh Kumar, WenYong Wang, Jay Kumar, Zakria, Ting Yang, Waqar Ali
2021 arXiv   pre-print
In this way, the proposed model not only improves malware detection accuracy using decentralized model network but also model efficacy with blockchain.  ...  Specifically for malware detection task, (i) we propose a novel user (local) neural network (LNN) which trains on local distribution and (ii) then to assure the model authenticity and quality, we propose  ...  The proposed model can identify the malware effectively which can provide more security for the Android IoT devices.  ... 
arXiv:2102.13376v2 fatcat:3gpn7qg56zgbvnugdrbxyiyfju

A New Malware Classification Framework Based on Deep Learning Algorithms

Omer Aslan, Abdullah Asim YILMAZ
2021 IEEE Access  
. model with two selected deep neural networks.  ...  Malware analysis can be divided into two main categories including static and dynamic. Malware analysis starts with basic static analysis and finishes with advanced dynamic analysis [15] .  ... 
doi:10.1109/access.2021.3089586 fatcat:4digsk4eirg6vmx2bfbso3e624

Categorizing Malware via A Word2Vec-based Temporal Convolutional Network Scheme

Jiankun Sun, Xiong Luo, Honghao Gao, Weiping Wang, Yang Gao, Xi Yang
2020 Journal of Cloud Computing: Advances, Systems and Applications  
To improve malware detection performance on IoT smart devices, we conduct a malware categorization analysis based on the Kaggle competition of Microsoft Malware Classification Challenge (BIG 2015) dataset  ...  Practically speaking, motivated by temporal convolutional network (TCN) structure, we propose a malware categorization scheme mainly using Word2Vec pre-trained model.  ...  In addition to detection performance, memory footprint and response speed are also of enormous importance for current smart devices on IoT, and this poses higher requirements for edge malware analysis.  ... 
doi:10.1186/s13677-020-00200-y fatcat:p353m5arnrfirnkjuxltpro3ui

Design of Secure Blockchain Convolution Neural Network Architecture for Detection Malware Attacks

2020 International journal of recent technology and engineering  
the targeted system, so it is needed to develop and deploy advance method to these kind of attacks for detecting correctly with a trusted and a better accuracy.  ...  method with better detection.  ...  Frist, they extracted one-channel where gray-scale image is converted from binaries then used a light-weight convolution neural network to classify IoT malware families.  ... 
doi:10.35940/ijrte.f8094.038620 fatcat:2353yyfseben5akrz74uav2lum

IoT Malware Network Traffic Classification using Visual Representation and Deep Learning

Gueltoum Bendiab, Stavros Shiaeles, Abdulrahman Alruban, Nicholas Kolokotronis
2020 2020 6th IEEE Conference on Network Softwarization (NetSoft)  
The experimental results of Residual Neural Network (ResNet50) are very promising, providing a 94.50% accuracy rate for detection of malware traffic.  ...  To address this challenge, we are proposing a novel IoT malware traffic analysis approach using deep learning and visual representation for faster detection and classification of new malware (zero-day  ...  for IoT malware traffic analysis.  ... 
doi:10.1109/netsoft48620.2020.9165381 dblp:conf/netsoft/BendiabSAK20 fatcat:qu5icb63e5gatgxncnrzaglvey

A Two-layer Deep Learning Method for Android Malware Detection Using Network Traffic

Jiayin Feng, Limin Shen, Zhen Chen, Yuying Wang, Hui Li
2020 IEEE Access  
It combines the static features with fully connected neural network to detect the malware and test its effectiveness through experiment, the detection rate of the first layer is 95.22%.  ...  INDEX TERMS Android, malware detection, deep learning, network traffic.  ...  Other is feeding the TFRecoder data into the convolutional Auto-Encoder cascading the convolutional neural networks to train the malware detection model. 1) CONVOLUTIONAL AUTO-ENCODER MODEL After the  ... 
doi:10.1109/access.2020.3008081 fatcat:3frpziqpjbcapik6cupr5ojyaa

Android Malware Detection Using TCN with Bytecode Image

Wenhui Zhang, Nurbol Luktarhan, Chao Ding, Bei Lu
2021 Symmetry  
Then the image size is unified and input to the designed neural network with three different convolution methods for experimental validation.  ...  At present, the existing Android malware bytecode image detection method, based on a convolution neural network (CNN), relies on a single DEX file feature and requires a large amount of computation.  ...  detection model by applying the time series convolution neural network to detect the bytecode image of malware for the first time.  ... 
doi:10.3390/sym13071107 fatcat:etl4l5xndragdlcjdtbj47x7ju

Review of Android Malware Detection Based on Deep Learning

Zhiqiang Wang, Qian Liu, Yaping Chi
2020 IEEE Access  
ACKNOWLEDGMENT This research was financially supported by the Key Lab of Information Network Security, Ministry of Public Security (C19614), the Fundamental Research Funds for the Central Universities(  ...  We gratefully acknowledge the anonymous reviewers for their valuable comments.  ...  It converts a series of event logs into flat data with two-dimensional features and uses a model based on convolutional neural networks for malware detection.  ... 
doi:10.1109/access.2020.3028370 fatcat:tujn3ghssrfkffzafat7l3cnse

Cognitive and Scalable Technique for Securing IoT Networks Against Malware Epidemics

P D Sai Manoj, Xiaojie Guo, Hossein Sayadi, Cameron Nowzari, Avesta Sasan, Setareh Rafatirad, Liang Zhao, Houman Homayoun
2020 IEEE Access  
for runtime malware detection on IoT devices.  ...  To this aim, a lightweight runtime malware detector is deployed on IoT nodes for detecting malware with high accuracy.  ... 
doi:10.1109/access.2020.3011919 fatcat:pdibd3722nay7obxkkomkiy64u

Malware Detection Based on Graph Attention Networks for Intelligent Transportation Systems

Cagatay Catal, Hakan Gunduz, Alper Ozcan
2021 Electronics  
The main objective of this study is to improve the performance of malware detection models using Graph Attention Networks.  ...  A GAN-based model is combined with different types of node features during the experiments and the performance is compared against Graph Convolutional Network (GCN).  ...  Several deep learning models such as Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), Boltzmann Machines, Recurrent Neural Networks (RNN) have been used to detect the malware [9]  ... 
doi:10.3390/electronics10202534 fatcat:akgkn5cp6baodoqesyh4vlprlm

Malware threat analysis techniques and approaches for IoT applications: a review

Chimeleze Collins Uchenna, Norziana Jamil, Roslan Ismail, Lam Kwok Yan, Mohamad Afendee Mohamed
2021 Bulletin of Electrical Engineering and Informatics  
However, with the sophistication of technology has resulted in IoT applications facing with malware threat.  ...  This study gives a better understanding of the holistic approaches to malware threats in IoT applications and the way forward for strengthening the protection defense in IoT applications.  ...  An efficient malware detection technique in cloud infrastructure employing CNN (convolutional neural network) was proposed by Abdelsalam et al. [36] .  ... 
doi:10.11591/eei.v10i3.2423 fatcat:tmkgezmv5ngcblgcxr6bmbqd3q

Artificial Neural Network for Cybersecurity: A Comprehensive Review [article]

Prajoy Podder, Subrato Bharati, M. Rubaiyat Hossain Mondal, Pinto Kumar Paul, Utku Kose
2021 arXiv   pre-print
With the increase in the scale of the Internet and the evolution of cyber attacks, developing novel cybersecurity tools has become important, particularly for Internet of things (IoT) networks.  ...  Next, a discussion is provided on the feasibility of DL systems for malware detection and classification, intrusion detection, and other frequent cyber-attacks, including identifying file type, spam, and  ...  Convolutional Neural Network Convolutional neural network (CNN) is a portion of deep NN that processes as well as analyze visual imagery input.  ... 
arXiv:2107.01185v1 fatcat:eowuriahobdpnakopzupxsvpsy
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