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An Efficient Approach to Detect and Classify IoT Malware Based On Byte Sequences from Executable Files

Tzu-Ling Wan, Tao Ban, Shin-Ming Cheng, Yen-Ting Lee, Bo Sun, Ryoichi Isawa, Takeshi Takahashi, Daisuke Inoue
2020 IEEE Open Journal of the Computer Society  
We show that reliable and efficient detection and classification can be implemented by exploring the essential discriminant information stored in the byte sequences at the entry points of executable programs  ...  The simple implementation and monotonous operation features make Internet of Things (IoT) vulnerable to malware attacks.  ...  this paper we proposed a novel approach to detect IoToriented malware and classify their families based on the bytes sequence extracted from ELF files.  ... 
doi:10.1109/ojcs.2020.3033974 fatcat:5b55cl3qcvbrdp2hiayqbqkk3y

2020 Index IEEE Open Journal of the Computer Society Vol. 1

2020 IEEE Open Journal of the Computer Society  
., +, OJCS 2020 145-154 Efficient Detection and Classification of Internet-of-Things Malware Based on Byte Sequences from Executable Files.  ...  ., OJCS 2020 73-85 Efficient Detection and Classification of Internet-of-Things Malware Based on Byte Sequences from Executable Files.  ... 
doi:10.1109/ojcs.2020.3047784 fatcat:r2i6j54vanczbjb5eee6mmw7qy

Trust-based Voting Method for Efficient Malware Detection

Shraddha S. More, Pranit P. Gaikwad
2016 Procedia Computer Science  
The internet plays an important role in all areas of society from the economy to the government. Security means permitting things you do want, while preventing things you don't want from performing.  ...  This paper presents a malware detection system based on the data mining and machine learning technique.  ...  Features that are commonly extracted from executable files include byte code n-gram, printable strings, instruction sequence, system calls, opcode n-gram. N-gram is a sequence of n characters.  ... 
doi:10.1016/j.procs.2016.03.084 fatcat:lq4gvmzdlfhcpd4jwq522hnuna

Malware Classification Based on Multilayer Perception and Word2Vec for IoT Security

Yanchen Qiao, Weizhe Zhang, Xiaojiang Du, Mohsen Guizani
2022 ACM Transactions on Internet Technology  
First, for one malware sample, Word2Vec is used to calculate a word vector for all bytes of the binary file and all instructions in the assembly file.  ...  To solve these issues, a malware classification method based on Word2Vec and Multilayer Perception (MLP) is proposed in this article.  ...  Malware Classification Based on Multilayer Perception and Word2Vec for IoT Security 10:3 malware binary file entities, and call flow diagrams, instruction sequences, API sequences, and so forth extracted  ... 
doi:10.1145/3436751 fatcat:ajhrymuplbho5hpravplambao4

Digital Forensics for Malware Classification: An Approach for Binary Code to Pixel Vector Transition

Muhammad Rehan Naeem, Rashid Amin, Sultan S. Alshamrani, Abdullah Alshehri, Konstantinos Demertzis
2022 Computational Intelligence and Neuroscience  
Traditional machine learning approaches, in which classifiers learn based on a hand-crafted feature vector, are ineffective for classifying malware.  ...  To evaluate the performance of our technique, we used a Microsoft malware dataset of 10,000 samples with nine distinct classifications.  ...  [16] proposed an approach to malware detection that relies on the unique behavior of malware executable files, which has been presented. e main thing is to identify any similarity in the conduct of  ... 
doi:10.1155/2022/6294058 pmid:35498213 pmcid:PMC9050294 fatcat:ozk6lxcxuzdd5lg6c2t2c3r7uq

Detection Method for Classifying Malicious Firmware

David Noever, Samantha E. Miller Noever
2021 International journal of network security and its applications  
A malicious firmware update may prove devastating to the embedded devices both that make up the Internet of Things (IoT) and that typically lack the same security verifications now applied to full operating  ...  This work converts the binary headers of 40,000 firmware examples from bytes into 1024-pixel thumbnail images to train a deep neural network.  ...  Firmware ELF-binaries as Thumbnail Images Why the Internet of Things (IoT) firmware? Embedded and Internet of Things (IoT) infrastructure depends on updates that users and industry can trust.  ... 
doi:10.5121/ijnsa.2021.13601 fatcat:m2uopqqovngdzee4zbndpztocq

A Deep Learning Approach for Malware and Software Piracy Threat Detection

K. Aldriwish
2021 Engineering, Technology & Applied Science Research  
Internet of Things (IoT) -based systems need to be up to date on cybersecurity threats.  ...  This paper attempts to improve IoT cybersecurity by proposing a combined model based on deep learning to detect malware and software piracy across the IoT network.  ...  Byte sequence technique is considered a statistic method and removes n-byte sequences from patterns. A.  ... 
doi:10.48084/etasr.4412 fatcat:tdu5smb5hjgsdeegll6nmlo7vy

Detection of Malicious Data using hybrid of Classification and Clustering Algorithms under Data Mining

Milan Jain, Bikram Pal
2014 International Journal of Computer Applications  
The another reason that enhance malware to infect and spread very rapidly is high-speed Internet connections as it has become more popular now a days, therefore it is very important to eradicate and detect  ...  A method that is commonly used for launching these types of attack is popularly known as malware i.e. viruses, Trojan horses and worms, which, when propagate can cause a great damage to commercial companies  ...  The string or byte sequences in this method contain same feature as signature and instruction to the machines.  ... 
doi:10.5120/18244-9193 fatcat:ur25f37r7rfzlkgrf7q7giubze

Machine Learning Aided Static Malware Analysis: A Survey and Tutorial [chapter]

Andrii Shalaginov, Sergii Banin, Ali Dehghantanha, Katrin Franke
2018 Advances in Information Security  
Malware analysis and detection techniques have been evolving during the last decade as a reflection to development of different malware techniques to evade network-based and host-based security protections  ...  PE32) Windows files and develop taxonomy for better understanding of these techniques.  ...  infrastructure [46] and Internet of Things networks [47] ).  ... 
doi:10.1007/978-3-319-73951-9_2 fatcat:rtzoclkjofh6tloo5v3nipu4uu

A Comprehensive Review on Malware Detection Approaches

Omer Aslan, Refik Samet
2020 IEEE Access  
In order to protect computer systems and the Internet from the malware, the malware needs to be detected before it affects a large number of systems.  ...  Signature-based and heuristic-based detection approaches are fast and efficient to detect known malware, but especially signature-based detection approach has failed to detect unknown malware.  ...  IoT-BASED MALWARE DETECTION Internet of Things (IoT) architecture generally consists of a wide range of Internet-connected smart devices such as home appliances, network cameras, and sensors.  ... 
doi:10.1109/access.2019.2963724 fatcat:ecckbq7ylzbepgl5az5qfupyxi

Mining Patterns of Sequential Malicious APIs to Detect Malware

Abdurrahman Pektas, Elif Nurdan Pektas, Tankut Acarman
2018 International journal of network security and its applications  
In the era of information technology and connected world, detecting malware has been a major security concern for individuals, companies and even for states.  ...  Based on the experimental results, the proposed method assures favorable results with 0.999 F-measure on a dataset including 8152 malware samples belonging to 16 families and 523 benign samples.  ...  EVALUATION We download the latest malware dataset from Virusshare and select the Windows executable files from the dataset.  ... 
doi:10.5121/ijnsa.2018.10401 fatcat:x5mrl6enzrd2raql3fol3x5fei

Binary Black-Box Adversarial Attacks with Evolutionary Learning against IoT Malware Detection

Fangwei Wang, Yuanyuan Lu, Changguang Wang, Qingru Li, Dr. Muhammad Shafiq
2021 Wireless Communications and Mobile Computing  
The generated adversarial samples can effectively combat DL-based malware detection models while preserving the consistency of the executable and malicious behavior of the original malware samples.  ...  5G is about to open Pandora's box of security threats to the Internet of Things (IoT).  ...  [38] proposed a modification method that injected a minor byte sequence into the originally binary file. It is also based on white-box attacks and is not efficient in real scenarios. Anderson et al  ... 
doi:10.1155/2021/8736946 fatcat:zxq644udbjb3lldmzz4mhyxmza

Classification of Malware with MIST and N-Gram Features Using Machine Learning

Udayakumar Nandagopal, Vellore Institute of Technology, Subbulakshmi Thirumalaivelu, Vellore Institute of Technology
2021 International Journal of Intelligent Engineering and Systems  
Malwares (Malicious Software's) has increased rapidly in the recent years over the internet, In-order to detect the malwares many anti-malware strategies also been introduced but most of them relay on  ...  Then the overall based on the N-Gram value and Byte length, various classifiers are evaluated, in that random forest has provided the best result.  ...  As mentioned above, we extracted 2 bytes, 3 bytes, and 4 bytes of N-Grams of different sizes to determine the N-Gram value to obtain the highest detection efficiency.  ... 
doi:10.22266/ijies2021.0430.29 fatcat:gmc4gu6cmjfwvfv2jk7sl2fffm

Polymorphic malware detection using sequence classification methods and ensembles

Jake Drew, Michael Hahsler, Tyler Moore
2017 EURASIP Journal on Information Security  
Whereas most gene classification tools are optimized for and restricted to an alphabet of four letters (nucleic acids), we have selected the Strand gene sequence classifier for malware classification.  ...  To demonstrate that gene sequence classification tools are suitable for classifying malware, we apply Strand to approximately 500 GB of malware data provided by the Kaggle Microsoft Malware Classification  ...  Modern malware frequently takes the form of a software program that is downloaded and executed by an unsuspecting Internet user.  ... 
doi:10.1186/s13635-017-0055-6 fatcat:rvtvbglnqbgtbamg7ihg7vkeaq

Malware classification using XGboost-Gradient Boosted Decision Tree

Rajesh Kumar, Geetha S
2020 Advances in Science, Technology and Engineering Systems  
The model is optimized for efficiency with the removal of noisy features by a reduction in features sets of the dataset by domain expertise in malware detection and feature importance functionality of  ...  Differentiation between the use of benign and malware is one way to make these transactions secure.  ...  Three of these will be based on file form agnostic parts and three will be based on the PE header part. The regrouping is selected based on domain knowledge of malware.  ... 
doi:10.25046/aj050566 fatcat:7fko7vaksvdh3ky5a7a3cjtani
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