Journal ArticleDOI
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
Anna L. Buczak,Erhan Guven +1 more
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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
A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection
TL;DR: A detailed investigation and analysis of various machine learning techniques have been carried out for finding the cause of problems associated with variousMachine learning techniques in detecting intrusive activities and future directions are provided for attack detection using machinelearning techniques.
Journal ArticleDOI
Smart Cities: A Survey on Data Management, Security, and Enabling Technologies
Ammar Gharaibeh,Mohammad A. Salahuddin,Sayed Jahed Hussini,Abdallah Khreishah,Issa Khalil,Mohsen Guizani,Ala Al-Fuqaha +6 more
TL;DR: The fundamental data management techniques employed to ensure consistency, interoperability, granularity, and reusability of the data generated by the underlying IoT for smart cities are described.
Journal ArticleDOI
A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective
TL;DR: This paper comprehensively survey the body of existing research on I-IoT, and proposes a three-dimensional framework to explore the existing research space and investigate the adoption of some representative networking technologies, including 5G, machine-to-machine communication, and software-defined networking.
Journal ArticleDOI
A Two-Layer Dimension Reduction and Two-Tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks
TL;DR: A novel model for intrusion detection based on two-layer dimension reduction and two-tier classification module, designed to detect malicious activities such as User to Root (U2R) and Remote to Local (R2L) attacks is presented.
Journal ArticleDOI
Network Intrusion Detection System: A systematic study of Machine Learning and Deep Learning approaches
Zeeshan Ahmad,Zeeshan Ahmad,Adnan Shahid Khan,Cheah Wai Shiang,Johari Abdullah,Farhan Ahmad,Farhan Ahmad +6 more
TL;DR: The concept of IDS is clarified and the taxonomy based on the notable ML and DL techniques adopted in designing network‐based IDS (NIDS) systems is provided, which highlights various research challenges and provided the future scope for the research in improving ML andDL‐based NIDS.
References
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Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Book
Fuzzy sets
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Journal ArticleDOI
Collective dynamics of small-world networks
TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
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