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
Proceedings ArticleDOI
An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registry
Wenxin Jiang,Nicholas Synovic,Matt Hyatt,Taylor R. Schorlemmer,Rohan Sethi,Yung-Hsiang Lu,George K. Thiruvathukal,James Charles Davis +7 more
TL;DR: In this paper , the authors present the first empirical investigation of pre-trained model reuse in the context of deep learning ecosystems, where they interviewed 12 practitioners from the most popular PTM ecosystem to learn the practices and challenges of model reuse.
Journal ArticleDOI
A PE header-based method for malware detection using clustering and deep embedding techniques
TL;DR: In this article, a deep learning method is proposed to learn different embedding representations for malware and benign programs, and the network parameters are then updated based on the clustering result.
Journal ArticleDOI
Application of Distance Metric Learning to Automated Malware Detection
Martin Jurecek,Róbert Lórencz +1 more
TL;DR: Wang et al. as discussed by the authors applied distance metric learning to the problem of malware detection and achieved a 1.09 % error rate at 0.74 % false positive rate (FPR) and outperformed all machine learning algorithms considered in the experiment.
Journal ArticleDOI
Malware Detection and Classification in IoT Network using ANN
TL;DR: This paper has explored the potential of neural networks for detection and classification of malware using IoT network dataset comprising of total 4,61,043 records with 3,00,000 as benign while 1, 61,043 as malicious.
Journal ArticleDOI
A Systematic Overview of the Machine Learning Methods for Mobile Malware Detection
TL;DR: The mobile malware detection techniques used in recent studies based on attack intentions are explored, such as server, network, client software, client hardware, and user, and they are classified as supervised and unsupervised learning.
References
More filters
Book
Learning Deep Architectures for AI
TL;DR: The motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer modelssuch as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks are discussed.
Proceedings ArticleDOI
A detailed analysis of the KDD CUP 99 data set
TL;DR: A new data set is proposed, NSL-KDD, which consists of selected records of the complete KDD data set and does not suffer from any of mentioned shortcomings.
Proceedings ArticleDOI
DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket.
TL;DR: DREBIN is proposed, a lightweight method for detection of Android malware that enables identifying malicious applications directly on the smartphone and outperforms several related approaches and detects 94% of the malware with few false alarms.
Proceedings ArticleDOI
Data mining methods for detection of new malicious executables
TL;DR: This work presents a data mining framework that detects new, previously unseen malicious executables accurately and automatically and more than doubles the current detection rates for new malicious executable.
Proceedings ArticleDOI
Crowdroid: behavior-based malware detection system for Android
TL;DR: The method is shown to be an effective means of isolating the malware and alerting the users of a downloaded malware, showing the potential for avoiding the spreading of a detected malware to a larger community.
Related Papers (5)
A state-of-the-art survey of malware detection approaches using data mining techniques
Alireza Souri,Rahil Hosseini +1 more