Machine Learning and Deep Learning Methods for Cybersecurity

By Yang Xin, Lingshuang Kong, Zhi Liu, Yuling Chen, Yanmiao Li, Hongliang Zhu, Mingcheng Gao, Haixia Hou, Chunhua Wang. IEEE Access, 2018

971 citations recorded by the source corpus.

With the development of the Internet, cyber-attacks are changing rapidly and the cyber security situation is not optimistic. This survey report describes key literature surveys on machine learning (ML) and deep learning (DL) methods for network analysis of intrusion detection and provides a brief tutorial description of each ML/DL method. Papers representing each method were indexed, read, and summarized based on their temporal or thermal correlations. Because data are so important in ML/DL methods, we describe some of the commonly used network datasets used in ML/DL, discuss the challenges of using ML/DL for cybersecurity and provide suggestions for research directions.

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

View Paper: https://www.semanticscholar.org/paper/39361b3507c9f8b0a97780568b645f80a208d78a

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Citations in the corpus

A Survey of Deep Learning Methods for Cyber Security (2019, background): “The paper by Xin et al. [7] focuses exclusively on cyber-attacks related to intrusion detection and targets shortcomings in datasets and areas of research for future model development.”
Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review (2019, background): “[255] presented a detailed study on machine learning and deep learning methods for intrusion detection, where the definitions of these areas are”
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018, methodology): “The main differences between traditional ML and DL methods has been discussed in previous literature [10, 11].”
Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research (2022, methodology): “Although cyber security data can be gathered straightforwardly by the use of numerous methods, like using software tools like Win Dump or Wireshark to capture network packets, these methods are mainly targeted and appropriate for gathering narrow or low volumes of data whereas high acquisition time ”
Deep Learning Approach for Intelligent Intrusion Detection System (2019, background): “text categorization, malicious URL detection, anomaly detection, and malicious domain name detection [11].”
State-of-the-art in artificial neural network applications: A survey (2018, methodology): “The DL techniques require raw image data only [109, 110, 111, 112].”
Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future (2019, neutral): “Also ML-based schemes for cyber-security [72]”
Deep Reinforcement Learning for Cyber Security (2019, methodology): “Among these ML applications, unsupervised and supervised learning methods have been used widely for intrusion detection [10]–[15], malware detection [16]–[19], cyber-physical attacks [20], [21], and data privacy protection [22].”
Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study (2020, background): “Our study and four works [13], [18], [19], [20] focus on deep learning approaches that are designed for cyber security intrusion detection.”