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
Boosting-Based DDoS Detection in Internet of Things Systems
TL;DR: In this article , the authors presented a DDoS traffic detection model that uses a boosting method of logistic model trees for different IoT device classes and demonstrated that the accuracy of their proposed approach is between 99.92% and 99.99% for these four device classes.
Proceedings ArticleDOI
Realtime Robust Malicious Traffic Detection via Frequency Domain Analysis
Chuanpu Fu,Qi Li,Meng Shen,Ke Xu +3 more
TL;DR: Wang et al. as mentioned in this paper proposed Whisper, a real-time machine learning based malicious traffic detection system that achieves both high accuracy and high throughput by utilizing frequency domain features.
Proceedings ArticleDOI
Important Complexity Reduction of Random Forest in Multi-Classification Problem
TL;DR: An approach to improve the complexity of a multi-classification learning problem in cloud networks using the Random Forest algorithm and the highly dimensional UNSW-NB 15 dataset, which yields substantial improvement in terms of computational complexity both during training and prediction phases.
Proceedings ArticleDOI
LuNet: A Deep Neural Network for Network Intrusion Detection
Peilun Wu,Hui Guo +1 more
TL;DR: LuNet as discussed by the authors proposes a hierarchical CNN+RNN neural network for network intrusion detection, where the convolutional neural network (CNN) and the RNN learn input traffic data in sync with a gradually increasing granularity such that both spatial and temporal features can be effectively extracted.
Journal ArticleDOI
A tree-based stacking ensemble technique with feature selection for network intrusion detection
TL;DR: Wang et al. as mentioned in this paper proposed a tree-based stacking ensemble technique (SET) and test the effectiveness of the proposed model on two intrusion datasets (NSL-KDD and UNSW-NB15).
References
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Outside the Closed World: On Using Machine Learning for Network Intrusion Detection
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