A state-of-the-art survey of malware detection approaches using data mining techniques

By Alireza Souri, Rahil Hosseini. Human-Centric Computing and Information Sciences, 2018

355 citations recorded by the source corpus.

Data mining techniques have been concentrated for malware detection in the recent decade. The battle between security analyzers and malware scholars is everlasting as innovation grows. The proposed methodologies are not adequate while evolutionary and complex nature of malware is changing quickly and therefore turn out to be harder to recognize. This paper presents a systematic and detailed survey of the malware detection mechanisms using data mining techniques. In addition, it classifies the malware detection approaches in two main categories including signature-based methods and behavior-based detection. The main contributions of this paper are: (1) providing a summary of the current challenges related to the malware detection approaches in data mining, (2) presenting a systematic and categorized overview of the current approaches to machine learning mechanisms, (3) exploring the structure of the significant methods in the malware detection approach and (4) discussing the important factors of classification malware approaches in the data mining. The detection approaches have been compared with each other according to their importance factors. The advantages and disadvantages of them were discussed in terms of data mining models, their evaluation method and their proficiency. This survey helps researchers to have a general comprehension of the malware detection field and for specialists to do consequent examinations.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/e6803bd1e264ad198bca7489a14d2de7d7dcba12

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jun Zhang
2. Wei Luo
3. Ömer Aslan
4. Surya Nepal
5. Roberto Baldoni
6. Refik Samet
7. Leonardo Aniello
8. Lei Pan

In Computer Networks and Communications

1. Jun Zhang
2. Ömer Aslan
3. Surya Nepal
4. Roberto Baldoni
5. Refik Samet
6. Leonardo Aniello
7. Daniele Ucci
8. Yang Xiang

In Signal Processing

1. Wei Luo
2. Ömer Aslan
3. Surya Nepal
4. Roberto Baldoni
5. Refik Samet
6. Leonardo Aniello
7. Lei Pan
8. Junyang Qiu

Citations in the corpus

• Survey on the Usage of Machine Learning Techniques for Malware Analysis (2017, background): “Souri and Hosseini [5] proposes a taxonomy of malware detection approaches based on machine learning.”
• A Survey of Android Malware Detection with Deep Neural Models (2020, methodology): “That is, an overview of Android malware penetration and defense strategies was presented in Reference [32] along with ML-based detection tools with 117 references; and four ML algorithm families, including SVM, NB, perceptron, and DNN for Android malware detection were briefly reviewed in Reference ”
• A Comprehensive Review on Malware Detection Approaches (2020, methodology): “This method works fast and efficiently against the known malware, but does not perform well against the zero-day malware [21], [29].”