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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Book ChapterDOI
Intrusion Detection Based on Fusing Deep Neural Networks and Transfer Learning
Yingying Xu,Zhi Liu,Yanmiao Li,Yushuo Zheng,Haixia Hou,Mingcheng Gao,Yongsheng Song,Yang Xin +7 more
TL;DR: This paper proposes an intrusion detection method based on deep learning and transfer learning, which transforms the intrusion detection problem into image recognition problem and is more efficient and robust than the mainstream machine learning and deep learning methods.
Journal ArticleDOI
A statistical class center based triangle area vector method for detection of denial of service attacks
TL;DR: A class center based triangle area vector (CCTAV) method which computes the mean of target classes individually and extracts the correlation between features and reduces the complexity of feature extraction and enhances the attack detection process.
Journal ArticleDOI
Anomaly detection in smart card logs and distant evaluation with Twitter: a robust framework
TL;DR: This paper defines an anomaly as any perturbation in the transportation network with respect to a typical day: temporary interruption, intermittent habit shifts, closed stations, unusual high/low number of entrances in a station.
Journal ArticleDOI
A robust domain partitioning intrusion detection method
TL;DR: A robust algorithm Sample-Measure-Assess (SMA) that detects intrusion based on rules learnt from multiple samples and yields robust parameters and provides a generalisation that can be monitored and adapted to specific low levels of variability is proposed.
Journal ArticleDOI
Cyber Security for Detecting Distributed Denial of Service Attacks in Agriculture 4.0: Deep Learning Model
TL;DR: In this article , the authors used the CIC-DDoS2019 dataset to design a proposal for detecting different types of DDoS attacks, which achieved a score of 100% with respect to all evaluation metrics.
References
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