A Comprehensive Review on Malware Detection Approaches
Omer Aslan,Refik Samet +1 more
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TLDR
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
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Scalable and Multifaceted Search and Its Application for Binary Malware Files
TL;DR: Wang et al. as mentioned in this paper presented a fast, scalable, and multifaceted search scheme to find similar binary malware files using MinHash to reduce any feature set of any file to a fixed size.
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Machine Learning for Detecting Malware in PE Files
Collin Connors,Dilip Sarkar +1 more
TL;DR: In this paper , the authors review and evaluate machine learning-based portable executable (PE) malware detection techniques using a large benchmark dataset, and evaluate features of PE files using the most common machine learning techniques to detect malware.
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Quickest Inference of Network Cascades With Noisy Information
TL;DR: In this paper , the authors study the problem of estimating the source of a network cascade given a time series of noisy information about the spread and derive near-optimal estimators for simple cascade dynamics and network topologies.
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Malware Detection and Classification Based on Graph Convolutional Networks and Function Call Graphs
TL;DR: In this article , a malware detection and classification model based on graphical convolutional networks and function call graphs is proposed to identify malware effectively and quickly by analyzing the behavior of malware executions through sandboxes.
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A Novel Ransomware Virus Detection Technique using Machine and Deep Learning Methods
TL;DR: In this article , the authors proposed a system for detecting ransomware attacks that uses machine learning and deep learning approaches to analyse the malware that enters the network in order to stop these malware attacks.
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