Autoencoder-based Malware Analysis: An Imagery Analysis Approach to Enhance the Security of Smart City IoT
Authors: 
Huiyao Dong and Igor KotenkoAuthors Info & Claims
Huiyao Dong and Igor KotenkoAuthors Info & ClaimsOctober 2023
Pages 56 - 59
Abstract
Abstract. Smart Cities, the modern digital urban landscapes, are primarily facilitated by the Internet of Things (IoT) infrastructures for information communication. Despite Smart Cities' benefits, risks revolving around data privacy and security within the IoT sphere raise concern. Particularly, malware attacks significantly threaten IoT systems, demanding proactive research into malware prevention techniques. This paper presents a study on autoencoder (AE)-based methodologies for efficient imagery analysis-based malware classification, aiming to enhance the Smart Cities IoT security. It focuses on effective malware classification utilizing various AE structures applied to grayscale or RGB malware derived images, contributing to improved malware detection and analysis. We conduct experiments with different input shapes and multi-label classification output to ascertain the robustness and generalizability of the proposed method. By analysing the classification capabilities of different AE types, we prove that variational AE built with convolutional neural network can achieve effective malware imagery classification in Smart City IoT environments.
References
[1]
Javier Carrillo-Mondejar, (2020) Automatic Analysis Architecture of IoT Malware Samples. Security and Communication Networks. 10.
[2]
Byungho Jung, (2018) Malware Classification Using Byte Sequence Information. Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems. 18: 143–148.
[3]
Jueun Jeon, (2020) Dynamic Analysis for IoT Malware Detection With Convolution Neural Network Model. IEEE Access. 8: 96899-96911.
[4]
Gueltoum Bendiab, (2020) IoT Malware Network Traffic Classification using Visual Representation and Deep Learning. 6th IEEE Conference on Network Softwarization (NetSoft): 444-449.
[5]
Ahmed Abusnaina, (2022) DL-FHMC: Deep Learning-Based Fine-Grained Hierarchical Learning Approach for Robust Malware Classification. IEEE Transactions on Dependable and Secure Computing. 19(5): 3432-3447.
[6]
Khanh Duy Tung Nguyen, (2018) Comparison of Three Deep Learning-based Approaches for IoT Malware Detection.10th International Conference on Knowledge and Systems Engineering (KSE): 382-388.
[7]
Hamed Haddad Pajouh, (2018) A deep Recurrent Neural Network based approach for Internet of Things malware threat hunting. Future Generation Computer Systems. 85: 88-96.
[8]
Jiawei Su, (2018) Lightweight Classification of IoT Malware Based on Image Recognition. 2018 IEEE 42nd Annual Computer Software and Applications Conference. 2: 664-669.
[9]
Ensieh Modiri Dovom, (2019) Fuzzy pattern tree for edge malware detection and categorization in IoT. Journal of Systems Architecture. 97: 1-7.
[10]
Weizhong Qiang, (2022) Efficient and Robust Malware Detection Based on Control Flow Traces Using Deep Neural Networks. Computers & Security. 122: 102871.
[11]
Zonghua Zhang, (2018) Detecting Malware with an Ensemble Method Based on Deep Neural Network. Security and Communication Networks.
[12]
Mohammed K. Alzaylaee, (2020) DL-Droid: Deep learning based android malware detection using real devices. Computers & Security. 89: 101663.
[13]
Muhammad Ijaz, (2019) Static and Dynamic Malware Analysis Using Machine Learning. 16th International Bhurban Conference on Applied Sciences and Technology. 687-691.
[14]
Hamid Darabian, (2020) Detecting Cryptomining Malware: a Deep Learning Approach for Static and Dynamic Analysis. J Grid Computing. 18: 293–303.
[15]
Lucas Pinheiro Cinelli, (2021). Variational Autoencoder. Variational Methods for Machine Learning with Applications to Deep Networks. 111–149.
[16]
Karen Simonyan, (2017) Very Deep Convolutional Networks for Large-Scale Image Recognition. International Conference on Learning Representations.
[17]
Francois Chollet(2017) Xception: Deep Learning With Depthwise Separable Convolutions.
Recommendations
The Detection of 8 Type Malware botnet using Hybrid Malware Analysis in Executable File Windows Operating Systems
ICEC '15: Proceedings of the 17th International Conference on Electronic Commerce 2015Nowadays a lot of botnet are being used for the purpose of cybercrime such as distributed denial of services (DDos) or information stealing. Botnet is a collection of computers connected through Internet that has been taken over by an attacker using ...
Malware Detection by Static Checking and Dynamic Analysis of Executables
The advanced malware continue to be a challenge in digital world that signature-based detection techniques fail to conquer. The malware use many anti-detection techniques to mutate. Thus no virus scanner can claim complete malware detection even for ...
Security in a smart city: challenges and solutions
SCA '19: Proceedings of the 4th International Conference on Smart City ApplicationsIn this paper we will discuss what concerns the smart city, the services in a smart city, also the mechanism of data processing and data analysis especially the data generated by the sensors because it considered a point of vulnerability in a smart city ...
Comments
Information & Contributors
Information
Published In
October 2023
120 pages
ISBN:9798400708954
DOI:10.1145/3640771
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 29 March 2024
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- Russian Science Foundation
Conference
ISCAI 2023
ISCAI 2023: 2023 2nd International Symposium on Computing and Artificial Intelligence
October 13 - 15, 2023
Shanghai, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 14Total Downloads
- Downloads (Last 12 months)14
- Downloads (Last 6 weeks)2
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format