A Comprehensive Review on Malware Detection Approaches
Omer Aslan,Refik Samet +1 more
Reads0
Chats0
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
Citations
More filters
Journal ArticleDOI
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.
Journal ArticleDOI
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.
Journal ArticleDOI
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.
Journal ArticleDOI
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.
Journal ArticleDOI
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
More filters
Journal ArticleDOI
Effective analysis of malware detection in cloud computing
TL;DR: This work proposes novel consolidated Weighted Fuzzy K-means clustering algorithm with Auto Associative Neural Network (WFCM-AANN) that can identify the anomalies with high detection precision thereby outperforming existing classifiers.
Proceedings ArticleDOI
A behavior based malware detection scheme for avoiding false positive
TL;DR: This paper proposes a malware detection method based on evaluation of suspicious process behaviors on Windows OS that focuses on not only malware specific behaviors but also normal behavior that malware would usually not do, and compares it with completely behavior-based anti-virus software.
Proceedings ArticleDOI
Investigation of Possibilities to Detect Malware Using Existing Tools
Omer Aslan,Refik Samet +1 more
TL;DR: This research will suggest to users how to analyze and detect existing and unknown malware, and indicated that it is almost impossible to detect malware by only using one tool.
Posted Content
Context-aware, Adaptive and Scalable Android Malware Detection through Online Learning (extended version).
TL;DR: CASANDRA as mentioned in this paper proposes a novel online learning based framework to detect malware, named Context Aware, Adaptive and Scalable ANDRoid mAlware detector, which captures apps' security-sensitive behaviors along with their context information from dependency graphs.
Book ChapterDOI
Using verification technology to specify and detect malware
TL;DR: An overview of the toolchain for malware detection is given and a new system for computer assisted generation of malicious code specifications is presented using the expressive specification language CTPL based on classic CTL.
Related Papers (5)
A state-of-the-art survey of malware detection approaches using data mining techniques
Alireza Souri,Rahil Hosseini +1 more