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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. ...
Research works on hunting Windows PE-based malwares are maturing, whereas the developments on Linux malware threat hunting are relatively scarce. ...
[17] proposed a cross-platform IoT malware family classification task based on printable string information (PSI). ...
doi:10.1109/access.2021.3091427
fatcat:tsfno6qdirhbdasj3fzrqqqzm4
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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The proposed scheme achieves near optimal generalization performance for malware detection (99.9% in accuracy) and for malware family classification (98.4% in accuracy). ...
In order to understand the behavior of IoT malware for further mitigation and prevention, static analysis on executable files of IoT malware is a feasible approach. ...
[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
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. ...
In this work, multichannel Convolutional Neural Network (CNN) is proposed whereas each channel's CNN works on each type of input parameter. ...
[11] proposed a malware detection system for the IoT platform using RNN-LSTM by extracting the opcode sequences of Advanced RISC Machine (ARM)-based IoT malware. ...
doi:10.34028/iajit/19/2/13
fatcat:wfumduxi2rexjk4atyu2ptkvq4
Machine Learning-based Analysis of Program Binaries: A Comprehensive Study
2019
IEEE Access
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Traditionally adopted techniques for binary code analysis are facing multiple challenges, such as the need for cross-platform analysis, high scalability and speed, and improved fidelity, to name a few. ...
In this paper, we provide the taxonomy of machine learning-based binary code analysis, describe the recent advances and key findings on the topic, and discuss the key challenges and opportunities. ...
Based on these malware features and group information, malware can be classified into different families. ...
doi:10.1109/access.2019.2917668
fatcat:fwjpykkdpjev7pzkhaoily4zci
A New Malware Classification Framework Based on Deep Learning Algorithms
2021
IEEE Access
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MALWARE DETECTION ON DIFFERENT DEVICES AND PLATFORMS Malware detection and classification approaches can be performed on different devices and platforms including: At first, malware variants were written ...
Based on the previous studies, it is examined that malware types, which belong to the same family, have similar images [8, 27, 28, 29] .
D. ...
doi:10.1109/access.2021.3089586
fatcat:4digsk4eirg6vmx2bfbso3e624
HSAS-MD Analyzer: A Hybrid Security Analysis System Using Model-Checking Technique and Deep Learning for Malware Detection in IoT Apps
2022
Sensors
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Its main task focuses on detecting malware and verifying app behavior. There are many SASs implemented in various IoT applications. ...
This paper proposes a new hybrid (static and dynamic) SAS based on the model-checking technique and deep learning, called an HSAS-MD analyzer, which focuses on the holistic analysis perspective of IoT ...
This technique detecting malware belonging to the same family well and is also successful against malware based on obfuscation and polymorphic techniques. ...
doi:10.3390/s22031079
pmid:35161823
pmcid:PMC8839744
fatcat:zk67psewwrbl5dccqdyihf3uru
Digital Forensics for Malware Classification: An Approach for Binary Code to Pixel Vector Transition
2022
Computational Intelligence and Neuroscience
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Traditional machine learning approaches, in which classifiers learn based on a hand-crafted feature vector, are ineffective for classifying malware. ...
A novel model of deep learning is introduced to categorize malware families and multiclassification. ...
Classification based on machine learning has long been a prominent way of malware protection. ...
doi:10.1155/2022/6294058
pmid:35498213
pmcid:PMC9050294
fatcat:ozk6lxcxuzdd5lg6c2t2c3r7uq
V-Sandbox For Dynamic Analysis IoT Botnet
2020
IEEE Access
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Nowadays, studies based on machine learning and deep learning have focused on dealing with IoT Botnet with many successes, and these studies have required relevant data during malware execution. ...
Experimental results on the 6141 IoT Botnet samples in our dataset have demonstrated the effectiveness of the proposed sandbox, compared to existing ones. ...
In this paper, based on Bencheton's classification approach [11] , IoT devices are divided into resource-constrained and high-capacity ones. ...
doi:10.1109/access.2020.3014891
fatcat:jyf7utxqxzdjnpdbtauxxqfv5i
Using a Subtractive Center Behavioral Model to Detect Malware
2020
Security and Communication Networks
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Signature-based and traditional behavior-based malware detectors cannot effectively detect this new generation of malware. ...
In recent years, malware has evolved by using different obfuscation techniques; due to this evolution, the detection of malware has become problematic. ...
Mobile-and IoT-based detection approaches can use both static and dynamic features and improve detection rates on traditional and new generation of malware [34] . ...
doi:10.1155/2020/7501894
fatcat:7mkem6suyfbdtekhiwiwve3eom
On the Design of IoT Security: Analysis of Software Vulnerabilities for Smart Grids
2021
Energies
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In this setting, the Internet of Things (IoT) will proliferate, and IoT devices will be included in many 5G application contexts, including the Smart Grid. ...
development, to enhance the security of produced software, as well as in the domain of automated software testing, targeting improvements to vulnerability detection mechanisms, especially with a focus on ...
with malware and the second one realizing a man-in-the-middle exploit. ...
doi:10.3390/en14102818
fatcat:tgpbzjsntnbsffqhbwqdb264eu
Inter-BIN: Interaction-based Cross-architecture IoT Binary Similarity Comparison
[article]
2022
pre-print
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Research has found that IoT malware can spread quickly on devices of different processer architectures, which leads our attention to cross-architecture binary similarity comparison technology. ...
In this paper, we propose an interaction-based cross-architecture IoT binary similarity comparison system, Inter-BIN. ...
[32] extracted statistical features of printable strings to characterize IoT malware of different architectures. ...
doi:10.1109/jiot.2022.3170927
arXiv:2206.00219v1
fatcat:l73p3iy7kbgklnix3gru5uslwq
CSITSS Proceedings 2020
2019
2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS)
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Raspberry Pi is a proven low power platform that can be used to create IoT devices. In this paper, a Zonal Architecture is presented to simplify the IoT eco-system. ...
Confusion matrix
N-Fold cross
validation
Regression
N-Fold cross
validation
Learning Trees
Boosted regression
forests
Accuracy
Feature Importance
Classification forests
Accuracy
Accuracy ...
Product-based Neural Networks In this paper we have focused on the general architecture of ontology-based Information Retrieval used for Kannada. ...
doi:10.1109/csitss47250.2019.9031039
fatcat:yehi3bfgbva7xm74vp3a3i54pu
Exploring semantic reverse engineering for software binary protection
2019
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Finally, I build BinSec, a vulnerability assessment tool which leverages deep learning and dynamic analysis to do cross-platform binary code similarity detection to identify known vulnerabilities. ...
For BinSec, I evaluate 25 existing CVE vulnerability functions for the Google Pixel 2 smartphone and Android Things IoT firmware images. The deep learning model identifies vulne [...] ...
FIE Davidson et al. (2013) presents a platform to detect memory safety issues in firmware on the MSP430 family of micro-controllers. ...
doi:10.7282/t3-zy08-nn55
fatcat:dqxzc5akg5ag3iihoknm5lyb64
SCIENCE PEACE SECURITY '19: Proceedings of the Interdisciplinary Conference on Technical Peace and Security Research
2019
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ACKNOWLEDGEMENTS The three-day conference was mainly organized by PEASEC (Science and Technology for Peace and Security) in cooperation with IANUS (Interdisciplinary Working Group on Science, Technology ...
and Security) and CROSSING (DFG Collaborative Research Centre) at TU Darmstadt as well as FONAS (Research Association for Science, Disarmament and International Security) and the German Foundation for ...
Furthermore, MAEC captures detailed information about malware samples and is used by malware analysts to model behavior, collections, malware actions, malware families and malware instances. ...
doi:10.25534/tuprints-00009164
fatcat:v6353gscpbeungviumxnedydf4
Formal Model of Exploit-Resistant Systems
2018
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The proposed model will prevent an adversary from executing malicious code on a target system. ...
The use of standard functions allows for developing cross-platform attacks while identified ROP gadgets would differ between different operating systems. ...
The first requirement is quite straightforward and is based on the classification of the variables and the trustworthiness of each of these classes. ...
doi:10.26180/5b41944ea9f47
fatcat:rvssrs65jned3lj25l5e3w7v2a
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