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
Posted Content
Predictive Models in Software Engineering: Challenges and Opportunities.
TL;DR: The key models and approaches used, classify the different models, summarize the range of key application areas, and analyze research results are described.
Journal ArticleDOI
A comprehensive survey on deep learning based malware detection techniques
TL;DR: In this article , the authors investigated recently proposed deep learning-based malware detection systems and their evolution and offered a thorough analysis of the recently developed DL-based detection techniques, including mobile malware, Windows malware, IoT malware, Advanced Persistent Threat (APTs), and Ransomware.
Proceedings ArticleDOI
Android Malware Detection using Chi-Square Feature Selection and Ensemble Learning Method
Meghna Dhalaria,Ekta Gandotra +1 more
TL;DR: In this paper, a technique based on static and dynamic features for the detection of Android malware was proposed, which applied a chi-square feature selection algorithm to choose the appropriate features that contribute for detecting malware.
Journal ArticleDOI
A Real-Time and Adaptive-Learning Malware Detection Method Based on API-Pair Graph
TL;DR: This article proposed a new model that could detect malware real-time in principle and learn new features adaptively and proved the feasibility of the model in real- time detection through the simulation experiment, and robustness against a typical adversarial attack.
Journal ArticleDOI
Image-based malware representation approach with EfficientNet convolutional neural networks for effective malware classification
TL;DR: In this article , the authors proposed an efficient neural network model EfficientNetB1 to perform the malware family classification using the malware byte level image representation technique, which has achieved an accuracy of 99% to classify the Microsoft Malware Classification Challenge (MMCC) malware classes.
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