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
Integrating complex event processing and machine learning: An intelligent architecture for detecting IoT security attacks
TL;DR: An intelligent architecture that integrates Complex Event Processing (CEP) technology and the Machine Learning (ML) paradigm in order to detect different types of IoT security attacks in real time is proposed and results obtained demonstrate that this architecture satisfactorily fulfils its objectives.
Proceedings ArticleDOI
Evaluating shallow and deep networks for ransomware detection and classification
TL;DR: This paper evaluates shallow and deep networks for the detection and classification of ransomware, and finds that MLP has performed well in detecting and classifying ransomwares in comparison to the other classical machine learning classifiers.
Journal ArticleDOI
Applying Big Data Based Deep Learning System to Intrusion Detection
Wei Zhong,Ning Yu,Chunyu Ai +2 more
TL;DR: The Big Data based Hierarchical Deep Learning System (BDHDLS) utilizes behavioral features and content features to understand both network traffic characteristics and information stored in the payload and can increase the detection rate of intrusive attacks as compared to the previous single learning model approaches.
Journal ArticleDOI
A novel intrusion detection system based on an optimal hybrid kernel extreme learning machine
TL;DR: A novel accurate and effective misuse intrusion detection system that relies on specific attack signatures to distinguish between normal and malicious activities is presented to detect various attacks based on an extreme learning machine with a hybrid kernel function (HKELM).
Journal ArticleDOI
A Machine-Learning-Based Data-Centric Misbehavior Detection Model for Internet of Vehicles
Prinkle Sharma,Hong Liu +1 more
TL;DR: This work proposes a data-centric misbehavior detection model based on supervised machine learning (ML), which integrates plausibility checks with ML techniques and instantiates the model with six algorithms to demonstrate their comparative effectiveness.
References
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