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Multichannel Based IoT Malware Detection System Using System Calls and Opcode Sequences
2022
˜The œinternational Arab journal of information technology
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The real-time Internet of Things (IoT) malware samples were collected from the IoT honeyPot (IOTPOT), which emulates different CPU architectures of IoT devices. ...
These extracted system calls and opcode sequences of elf files were discriminated against using two more deep learning algorithms along with multichannel CNN, namely Recurrent Neural Network (RNN) and ...
An et al [5] suggested a malware detection system for the Amazon Alexa Echo using system calls from IoT malware. ...
doi:10.34028/iajit/19/2/13
fatcat:wfumduxi2rexjk4atyu2ptkvq4
Android Malware Detection via Graph Representation Learning
2021
Mobile Information Systems
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Then, we use the graph neural network to generate a vector representation of the application, and then malware detection is performed on this representation space. ...
Particularly, we construct approximate call graphs from function invocation relationships within an Android application to represent this application and further extract intrafunction attributes, including ...
approximate call graph to perform effective malware detection. ...
doi:10.1155/2021/5538841
fatcat:o4beznwd4zadvcqfqubbwgalmy
Comprehensive Analysis of IoT Malware Evasion Techniques
2021
Engineering, Technology & Applied Science Research
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Many security researchers have studied the IoT malware detection domain. Many studies proposed the static or dynamic analysis on IoT malware detection. ...
Malware detection in Internet of Things (IoT) devices is a great challenge, as these devices lack certain characteristics such as homogeneity and security. ...
[31] Non-graph and graph-based malware detection methods. Two groups of malware detection methods were used: non-graph and graph-based methods. ...
doi:10.48084/etasr.4296
fatcat:hyfkdspwizce3cyeu6erygpqai
A Deep Learning-based Fine-grained Hierarchical Learning Approach for Robust Malware Classification
[article]
2020
arXiv
pre-print
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To do so, examine the performance of the state-of-the-art methods against adversarial IoT software crafted using the graph embedding and augmentation techniques. ...
Upon optimization and with small perturbation, by use of SGEA, all the IoT malware samples are misclassified as benign. ...
[51] proposed a graph-based detection system that uses a quantitative data flow graphs generated from the system calls, and use the graph node properties, i.e., centrality metric, as a feature vector ...
arXiv:2005.07145v2
fatcat:vsiin3udfjfd7acelbdh5vkhiu
Dynamic Analysis for IoT Malware Detection with Convolution Neural Network model
2020
IEEE Access
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The DAIMD scheme learns IoT malware using the convolution neural network (CNN) model and analyzes IoT malware dynamically in nested cloud environment. ...
INDEX TERMS Cloud-based malware detection, convolution neural network, dynamic analysis, IoT malware, malware detection. ...
In addition, various actions in memory, network, process, system call, and virtual file system are extracted to detect malware that perform malicious actions on embedded Linux-based IoT devices. ...
doi:10.1109/access.2020.2995887
fatcat:sjch2uh54ja2xapedtkwddwgiq
A Survey on Cross-Architectural IoT Malware Threat Hunting
2021
IEEE Access
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This study aims at providing a comprehensive survey on the latest developments in cross-architectural IoT malware detection and classification approaches. ...
We further provide more insights on the practical challenges involved in cross-architectural IoT malware threat hunting and discuss various avenues to instill potential future research. ...
The PSI-Graph extraction is done as a simplified version of the function call graph (FCG) using only the functions that contain PSI characters with lengths more than or equal to three. ...
doi:10.1109/access.2021.3091427
fatcat:tsfno6qdirhbdasj3fzrqqqzm4
Malware Detection and Classification in IoT Network using ANN
2022
Mehran University Research Journal of Engineering and Technology
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In this paper, we have explored the potential of neural networks for detection and classification of malware using IoT network dataset comprising of total 4,61,043 records with 3,00,000 as benign while ...
With the proposed methodology, malware is detected with an accuracy of 94.17% while classified with 97.08% accuracy ...
Function call graphs were created using these samples. Further PSI graphs were created using functions that were close to IoT botnets. ...
doi:10.22581/muet1982.2201.08
doaj:0bfd1c088ebb4199a37dd4855d001439
fatcat:x7bvatk7azbcvgon7zzlehntv4
HawkEye: Cross-Platform Malware Detection with Representation Learning on Graphs
2021
Zenodo
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In more detail, HawkEye utilizes a graph neural network to convert the control flow graphs of executable to vectors with the trainable instruction embedding and then uses a machine-learning-based classifier ...
to create a malware detection system. ...
Related Work MalConv [10] Adagio [4] implements a kernel-hashing-based malware detection system on the function call graph. ...
doi:10.5281/zenodo.5750058
fatcat:3j42h4lkojfldjz2tmerqt5ixy
Cognitive and Scalable Technique for Securing IoT Networks Against Malware Epidemics
2020
IEEE Access
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It exploits perf_event_open function call in the background to measure multiple events simultaneously. ...
for runtime malware detection on IoT devices. ...
doi:10.1109/access.2020.3011919
fatcat:pdibd3722nay7obxkkomkiy64u
A Novel Framework to Classify Malware in MIPS Architecture-Based IoT Devices
2019
Security and Communication Networks
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IoT devices use the MIPS architecture with a large proportion running on embedded Linux operating systems, but the automatic analysis of IoT malware has not been resolved. ...
We proposed a framework to classify malware in IoT devices by using MIPS-based system behavior (system call—syscall) obtained from our F-Sandbox passive process and machine learning techniques. ...
Deep4-MalDroid [15] extracted the Linux kernel system calls from the executing apps on Android, generates a weighted directed graph, and then applies a deep learning framework resting on the graph-based ...
doi:10.1155/2019/4073940
fatcat:llefjvtxc5be3h4yc5vemkfcga
Robust Malware Detection for Internet Of (Battlefield) Things Devices Using Deep Eigenspace Learning
2018
IEEE Transactions on Sustainable Computing
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A common attack vector is the use of malware. ...
Lastly, we make available our malware sample on Github, which hopefully will benefit future research efforts (e.g. for evaluation of proposed malware detection approaches). ...
ACKNOWLEDGMENTS We thank VirusTotal for providing us a private API key to access their data for constructing our dataset. ...
doi:10.1109/tsusc.2018.2809665
dblp:journals/tsusc/AzmoodehDC19
fatcat:wo5ppc7j6nc7jhzfplivt7kxii
Static Feature Selection for IoT Malware Detection
2022
Journal of Science and Technology on Information security
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IoT malware detection based on opcode sequence features. ...
Therefore, it is necessary to come up with more efficient approaches to IoT malware detection with machine learning models that can be used in solutions using limited resources. ...
With static features, common forms have been used include strings [13] , bytes n-gram [14] , opcode [15] , function call graph [16] , entropy-based [17] , etc. ...
doi:10.54654/isj.v1i15.844
fatcat:gunra42245hwhfwt3suiftyjhy
Multi-relational Instruction Association Graph for Cross-architecture Binary Similarity Comparison
[article]
2022
arXiv
pre-print
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Furthermore, evaluations on a large-scale real-world IoT malware reuse function collection show that our approach is valuable for identifying malware propagated on IoT devices of various architectures. ...
However, instruction embeddings pre-trained on external code corpus are not universal in diverse real-world applications. ...
Furthermore, our approach can achieve effective cross-architecture reuse function detection on a large-scale IoT malware dataset collected from the real-world network environment, which is meaningful for ...
arXiv:2206.12236v2
fatcat:nt24bnvqg5epppqpkrp2zfrgvi
IDAPro for IoT Malware analysis?
2019
USENIX Security Symposium
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Defending against the threat of IoT malware will require new techniques and tools. ...
A key question is whether PC-oriented disassemblers can be effective on IoT malware, given the difference in the malware programs and the processors that support them. ...
Disassemblers in malware analysis. Several efforts use disassemblers in analyzing the malware structure like call graphs [15] . These studies use disassemblers for malware classification. ...
dblp:conf/uss/GDFAS19
fatcat:hnl2vwta6fcxdb5rkn6qbeuyxy
An Efficient Approach to Detect and Classify IoT Malware Based On Byte Sequences from Executable Files
2020
IEEE Open Journal of the Computer Society
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However, current analysis approaches based on opcode or call-graph usually do not work well with the diversity in CPU architectures and are often resource demanding. ...
In this paper, we propose an efficient scheme to detect and classify IoT malware programs leveraging machine learning methods. ...
[23] focused on the detection of IoT botnets by using Printable String Information (PSI) -graph as the main feature for the learning. ...
doi:10.1109/ojcs.2020.3033974
fatcat:5b55cl3qcvbrdp2hiayqbqkk3y
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