A Comprehensive Review on Malware Detection Approaches
Omer Aslan,Refik Samet +1 more
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This paper presents a detailed review on malware detection approaches and recent detection methods which use these approaches, and the pros and cons of each detection approach, and methods that are used in these approaches.Abstract:
According to the recent studies, malicious software (malware) is increasing at an alarming rate, and some malware can hide in the system by using different obfuscation techniques. 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. Recently, there have been made several studies on malware detection approaches. However, the detection of malware still remains problematic. 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. 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 also emerge to detect some portion of known and unknown malware. However, no approach can detect all malware in the wild. This shows that to build an effective method to detect malware is a very challenging task, and there is a huge gap for new studies and methods. This paper presents a detailed review on malware detection approaches and recent detection methods which use these approaches. Paper goal is to help researchers to have a general idea of the malware detection approaches, pros and cons of each detection approach, and methods that are used in these approaches.read more
Citations
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Hybrid Malware Classification Method Using Segmentation-Based Fractal Texture Analysis and Deep Convolution Neural Network Features
Maryam Nisa,Jamal Hussain Shah,Shansa Kanwal,Mudassar Raza,Muhammad Attique Khan,Robertas Damaševičius,Tomas Blažauskas +6 more
TL;DR: A feature fusion method to combine the features extracted from pre-trained AlexNet and Inception-v3 deep neural networks with features attained using segmentation-based fractal texture analysis (SFTA) of images representing the malware code to build a multimodal representation of malicious code.
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Tight Arms Race: Overview of Current Malware Threats and Trends in Their Detection
Luca Caviglione,Michal Choras,Igino Corona,Artur Janicki,Wojciech Mazurczyk,Marek Pawlicki,Katarzyna Wasielewska +6 more
TL;DR: A detailed meta-review of the existing surveys related to malware and its detection techniques, showing an arms race between these two sides of a barricade, is presented in this article.
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Ensemble-Based Classification Using Neural Networks and Machine Learning Models for Windows PE Malware Detection
TL;DR: An ensemble classification-based methodology for malware detection is proposed, with the best performance achieved by an ensemble of five dense and CNN neural networks, and the ExtraTrees classifier as a meta-learner.
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A systematic review on Deep Learning approaches for IoT security
TL;DR: This work aims at systematically reviewing and analyzing the research landscape about DL approaches applied to different IoT security scenarios and characterized these studies according to three main research questions, namely, the involved security aspects, the used DL network architectures, and the engaged datasets.
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A New Malware Classification Framework Based on Deep Learning Algorithms
Omer Aslan,Abdullah Asim Yilmaz +1 more
TL;DR: In this paper, a novel deep learning-based architecture is proposed which can classify malware variants based on a hybrid model, which integrates two wide-ranging pre-trained network models in an optimized manner.
References
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