A Survey on Malware Detection Using Data Mining Techniques

By Yanfang Ye, Tao Li, D. Adjeroh, S. S. Iyengar. ACM Computing Surveys, 2017

612 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/087850e85d52628a0c909a4c731369f9e8574f54

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Citations in the corpus

Robust Malware Detection for Internet of (Battlefield) Things Devices Using Deep Eigenspace Learning (2019, background): “Intrusion and malware detection and prevention are two active research areas [1], [2], [3], [4], [5].”
Deep Learning for Anomaly Detection: A Survey (2019, methodology): “In order to protect legitimate users from malware, machine learning based efficient malware detection methods are proposed Ye et al. [2017]. In the classical machine learning methods, the process of malware detection is usually divided into two stages: feature extraction and classification/clusterin”
Dynamic Malware Analysis in the Modern Era—A State of the Art Survey (2019, methodology): “(5) Surveys on machine-learning methods for malware detection [9] and malware detection using dynamic analysis [8] have been presented, however these surveys don’t provide the reader with comprehensive information or a thorough analysis regarding the machine-learning methods that leverage dynamic…”
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources (2020, neutral): “, trojans, ransomware, scamware) that deliberately fulfills the harmful intent to device users has become a major threat to compromise the security in cyberspace [126].”
Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research (2022, background): “detection can be categorized into two main types: Static detection and Dynamic detection [149].”
Survey on the Usage of Machine Learning Techniques for Malware Analysis (2017, background): “[2] analyse scientific papers on malware detection only.”
Detection of Malicious Code Variants Based on Deep Learning (2018, background): “In recent years, several scholars have used data mining methods to analyze the features of malicious code [28].”
Towards Adversarial Malware Detection (2018, result): “The significant adversarial-related traits of this survey also completely distinguish it from other works on data mining for malware detection; for example, the work in [103] is not focused on the adversarial aspects of the problem.”