Journal ArticleDOI
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
Anna L. Buczak,Erhan Guven +1 more
TLDR
The complexity of ML/DM algorithms is addressed, discussion of challenges for using ML/ DM for cyber security is presented, and some recommendations on when to use a given method are provided.Abstract:
This survey paper describes a focused literature survey of machine learning (ML) and data mining (DM) methods for cyber analytics in support of intrusion detection. Short tutorial descriptions of each ML/DM method are provided. Based on the number of citations or the relevance of an emerging method, papers representing each method were identified, read, and summarized. Because data are so important in ML/DM approaches, some well-known cyber data sets used in ML/DM are described. The complexity of ML/DM algorithms is addressed, discussion of challenges for using ML/DM for cyber security is presented, and some recommendations on when to use a given method are provided.read more
Citations
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Journal ArticleDOI
Leveraging Deep Reinforcement Learning for Traffic Engineering: A Survey
TL;DR: A comprehensive overview of DRL-based TE is provided including three fundamental issues: routing optimization, congestion control, and resource management and the insights into the challenges and future research perspectives are discussed.
Journal ArticleDOI
An Insider Data Leakage Detection Using One-Hot Encoding, Synthetic Minority Oversampling and Machine Learning Techniques.
TL;DR: Wang et al. as mentioned in this paper proposed a machine learning-based model for detecting insider threat incidents, which addresses the possible bias of detection results that can occur due to an inappropriate encoding process by employing the feature scaling and one-hot encoding techniques.
Journal ArticleDOI
A statistical pattern based feature extraction method on system call traces for anomaly detection
TL;DR: A new feature extraction method that aims at extracting features that are irrelevant to the names of system calls that is suitable for anomaly detection across platforms.
Journal ArticleDOI
A Brute-Force Black-Box Method to Attack Machine Learning-Based Systems in Cybersecurity
Sicong Zhang,Xiaoyao Xie,Yang Xu +2 more
TL;DR: The preliminary experimental results show that the proposed brute-force attack method, which is more efficient in computation and outperforms the state-of-the-art attack methods based on generative adversarial networks, can be used to evaluate the robustness of various machine learning based systems in cybersecurity against adversarial examples.
Posted Content
Recurrent Neural Network Language Models for Open Vocabulary Event-Level Cyber Anomaly Detection
TL;DR: In this paper, a flexible, powerful, and unsupervised approach to detecting anomalous behavior in computer and network logs, one that largely eliminates domain-dependent feature engineering employed by existing methods, is introduced.
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