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Similarity hash based scoring of portable executable files for efficient malware detection in IoT

Anitta Patience Namanya, Irfan U. Awan, Jules Pagna Disso, Muhammad Younas
2019 Future generations computer systems  
This paper explores four hash types currently used in malware analysis for portable executable (PE) files.  ...  The current rise in malicious attacks shows that existing security systems are bypassed by malicious files. Similarity hashing has been adopted for sample triaging in malware analysis and detection.  ...  This justifies the reason of further exploring hash-based similarity matching for a possibility of efficient malware detection.  ... 
doi:10.1016/j.future.2019.04.044 fatcat:mnnxoxrnazfw3hosea3xjvciaq

Rule-Based Approach to Detect IoT Malicious Files

Faisal Alsattam, Mousa Al-Akhras, Marwah M. Almasri, Mohammed Alawairdhi
2020 Journal of Computer Science  
In this study, a flexible and an effective rule-based approach is proposed to detect malicious files by searching for specific types of strings that should not exist in normal legitimate files.  ...  Current malware detection solutions are only able to identify known malwares that were previously detected. They also lack the ability to deeply investigate every file in the system.  ...  The authors would like to thank editors for their efforts in handling the manuscript and all reviewers for the constructive comments which improved the original submission.  ... 
doi:10.3844/jcssp.2020.1203.1211 fatcat:lq76ow44lfalnlbfn53akuylxu

Malware static analysis and DDoS capabilities detection [article]

Mounir Baammi
2018 arXiv   pre-print
Based on the discoveries, a set of rules was elaborated to detect those features in binaries. The method is tested on a dataset of 815 samples.  ...  We have implemented a process to extract meaningful data from malware samples, the extracted data was used to find characteristics and features that can lead to the detection of DDoS capabilities in binaries  ...  Survey of 2018, found that 71% of IoT run Linux-Bases OS 6 , thanks to the scalability, small footprint, portability, and modularity of such systems, they are widely used on IoT devices since those devices  ... 
arXiv:1812.00784v1 fatcat:hsvx2ep3cvg3lawtbhjc3ycgey

A Survey on Cross-Architectural IoT Malware Threat Hunting

Anandharaju Durai Raju, Ibrahim AbuAlhaol, Ronnie Salvador Giagone, Yang Zhou, Huang Shengqiang.
2021 IEEE Access  
The IoT devices employ various Unix-based architectures that follow ELF (Executable and Linkable Format) as their standard binary file specification.  ...  This study aims at providing a comprehensive survey on the latest developments in cross-architectural IoT malware detection and classification approaches.  ...  string hashes, and finally employing KNN based detection.  ... 
doi:10.1109/access.2021.3091427 fatcat:tsfno6qdirhbdasj3fzrqqqzm4

Platform-Independent Malware Analysis Applicable to Windows and Linux Environments

Chanwoong Hwang, Junho Hwang, Jin Kwak, Taejin Lee
2020 Electronics  
It uses platform-independent binary data rather than features based on the structured format of the executable files. We analyzed the strings from binary data to classify malware.  ...  As is known, Linux/embedded environments support various architectures, so it is difficult to identify the architecture in which malware operates when analyzing malware.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/electronics9050793 fatcat:ouh5phpok5hdxet4awdpkal3hi

A Comprehensive Review on Malware Detection Approaches

Omer Aslan, Refik Samet
2020 IEEE Access  
On the other hand, behavior-based, model checking-based, and cloud-based approaches perform well for unknown and complicated malware; and deep learning-based, mobile devices-based, and IoT-based approaches  ...  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 detection schema can be seen in Figure 10 . 1) RELATED WORKS FOR IoT-BASED MALWARE DETECTION Malware detection approach for IoT devices is represented in [105] , [60] .  ... 
doi:10.1109/access.2019.2963724 fatcat:ecckbq7ylzbepgl5az5qfupyxi

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  ...  We propose in this work a malware classification scheme that constructs a model using low-end computing resources and a very large balanced dataset for malware.  ...  The file form agnostic features Histogram of bytes in the executable, 2-dimensional byte entropy for executable, and the string are hashed.  ... 
doi:10.25046/aj050566 fatcat:7fko7vaksvdh3ky5a7a3cjtani

Droid-IoT: Detect Android IoT Malicious Applications Using ML and Blockchain

Hani Mohammed Alshahrani
2022 Computers Materials & Continua  
Android is one of the most popular operating systems (OS) used by IoT devices for communication and data exchange. Android OS captured more than 70 percent of the market share in 2021.  ...  One of the most rapidly growing areas in the last few years is the Internet of Things (IoT), which has been used in widespread fields such as healthcare, smart homes, and industries.  ...  Funding Statement: The authors received no specific funding for this study. Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study.  ... 
doi:10.32604/cmc.2022.019623 fatcat:uypts4tukvgrxevbujajkcj54u

Malware Binary Image Classification Using Convolutional Neural Networks

John Kiger, Shen-Shyang Ho, Vahid Heydari
2022 International Conference on Cyber Warfare and Security (ICIW)  
As of March 2020, the total number of new malware detections worldwide amounted to 677.66 million programs. In 2020, there was a 35.4% increase in new malware variants over the previous year.  ...  Furthermore, the proliferation of malicious files and new malware signatures increases year by year.  ...  Acknowledgement This material is based upon work supported by the National Science Foundation under Grant No. 1753900.  ... 
doi:10.34190/iccws.17.1.59 fatcat:3xgqmm3syfe3lninuqg5dxeumu

A Review of Computer Vision Methods in Network Security [article]

Jiawei Zhao, Rahat Masood, Suranga Seneviratne
2020 arXiv   pre-print
In this paper, we provide a comprehensive survey of such work under three topics; i) phishing attempt detection, ii) malware detection, and iii) traffic anomaly detection.  ...  Nonetheless, there is a significant amount of work that highlighted how methods from computer vision can be applied in network security for detecting attacks or building security solutions.  ...  Instead of extracting features directly from a Portable Executable (PE) file, authors calculated a set of features from different categories in hex and assembly view of PE.  ... 
arXiv:2005.03318v1 fatcat:pcng7535obec3l6fejkllbi3ii

Building malware classificators usable by State security agencies

David Esteban Useche-Peláez, Daniel Orlando Díaz-López, Daniela Sepúlveda-Alzate, Diego Edison Cabuya-Padilla
2018 Iteckne  
A proposal of architecture for an IoT sentinel that uses one of the developed machine learning model is also showed.  ...  The developed models are also tested obtaining an acceptable percentage of correctly classified samples, being in this way useful tools for a malware analyst.  ...  ACKNOWLEDGMENT This work has been supported partially by the Colombian School of Engineering Julio Garavito (Colombia) through the project "Cyber Security Architecture for Incident Management", funded  ... 
doi:10.15332/iteckne.v15i2.2072 fatcat:nfeiawae5vd4hpci2dufnpeb2y

Android Malware Clustering using Community Detection on Android Packages Similarity Network [article]

ElMouatez Billah Karbab, Mourad Debbabi, Abdelouahid Derhab, Djedjiga Mouheb
2020 arXiv   pre-print
Specifically, Cypider leverages this assumption for the detection of variants of known malware families and zero-day malicious apps.  ...  Our approach is based on our proposed concept, namely malicious community, in which we consider malicious instances that share common features are the most likely part of the same malware family.  ...  Other methods combine between malware detection and family attribution in case of Android OS [85, 86, 87, 88] and Win32 OS [89, 90] Similarity-based detection Similarity-based detection methods  ... 
arXiv:2005.06075v1 fatcat:43wg2wbvejg35ix6yz6tskmie4

When deep learning meets security [article]

Majd Latah
2018 arXiv   pre-print
In this work, we provide an overview for the recent studies that apply deep learning techniques to the field of security.  ...  Security, on the other hand, is one of the most essential issues in modern communication systems.  ...  As shown in Figure 2 , Cuckoo Sandbox was used in order to collect activity data of Portable Executable (PE) samples.  ... 
arXiv:1807.04739v1 fatcat:l3wtzn4rlbg6vnxk4bmbx2skhq

Intelligent Behavior-Based Malware Detection System on Cloud Computing Environment

Omer Aslan, Merve Ozkan-Okay, Deepti Gupta
2021 IEEE Access  
Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.  ...  We considered portable executable (exe, DLL) and macro files slightly more dangerous than other files including txt, image, multimedia files, etc.  ...  Multiple execution traces of the same malicious software can be collected. It enhances the detection performance for personal computers, mobile and IoT devices. III.  ... 
doi:10.1109/access.2021.3087316 fatcat:o657c33aireaner7yreutxixia

A Novel Approach to Detect Malware Variants Based on Classified Behaviors

Donggao Du, Yi Sun, Yan Ma, Fei Xiao
2019 IEEE Access  
Behavior features based on API play an important role in analyzing malware variants. However, the existing malware detection approaches have a lot of complex operations on construction and matching.  ...  In particular, to verify the efficiency of our approach, we perform a series of experiments with different families.  ...  [8] analyzed API call sequence that extracted from portable executable files and constructed Intelligent Malware Detection System (IMDS). Sathyanarayan et al.  ... 
doi:10.1109/access.2019.2924331 fatcat:xglopfgdufam5ehdxn5ma3qasy
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