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
Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process
TL;DR: This survey reviews the methods that have been applied to network data with the purpose of developing an intrusion detector, but contrary to previous reviews in the area, they are analyzed from the perspective of the Knowledge Discovery in Databases (KDD) process.
Proceedings ArticleDOI
DRaNN: A Deep Random Neural Network Model for Intrusion Detection in Industrial IoT
TL;DR: A deep random neural (DRaNN) based scheme for intrusion detection in IIoT successfully classified nine different types of attacks with a low false-positive rate and great accuracy, and is compared with state-of-the-art deep learning-based intrusion detection schemes.
Journal ArticleDOI
An Efficient Mixed Attribute Outlier Detection Method for Identifying Network Intrusions
TL;DR: A clustering-based outlier detection (CBOD) approach is proposed for classifying normal and intrusive patterns and appears to be promising in terms of DR, FAR and ACC.
Journal ArticleDOI
Securing Data With Blockchain and AI
TL;DR: The SecNet is proposed, an architecture that can enable secure data storing, computing, and sharing in the large-scale Internet environment, aiming at a more secure cyberspace with real big data and thus enhanced AI with plenty of data source by integrating three key components.
Journal ArticleDOI
Role of machine learning and deep learning in securing 5G-driven industrial IoT applications
TL;DR: In this paper, the authors present a comprehensive review for securing industrial IoT devices to contribute to the development of security methods for I-IoT deployed over 5G and blockchain, which can help overcome many challenges in the IoT security and pave way for implementation with emerging technologies like 5G, blockchain, edge computing, fog computing and their use cases for creating smart environments.
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