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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Proceedings ArticleDOI
KARL: Fast Kernel Aggregation Queries
TL;DR: A novel and effective bounding technique to speedup the computation of kernel aggregation by leveraging index structures and exploiting index tuning opportunities and is extensible to different types of kernel functions and weightings.
Proceedings ArticleDOI
Network Intrusion Detection Based on Xgboost Model Improved by Quantum-behaved Particle Swarm Optimization
TL;DR: The experimental results show that the Xgboost model optimized by QPSO has good performance in terms of precision, recall rate and average precision, and is better than the unoptimized Xg Boost model and grid search method.
Proceedings ArticleDOI
An efficient Trust Related Attack Detection Model based on Machine Learning for Social Internet of Things
TL;DR: In this article, the authors proposed a trust management model to detect and prevent malicious attacks in the social Internet of Things (SIoT) based on Machine Learning (ML) techniques, which can identify these attacks by learning proposed trust features from the description of malicious node behaviors.
Proceedings ArticleDOI
A monitoring and threat detection system using stream processing as a virtual function for Big Data
TL;DR: A deteccao tardia de ameacas de seguranca causa um significante aumento no risco of danos irreparaveis, impossibilitando qualquer tentativa de defesa.
Journal ArticleDOI
On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunities
Masoud Mehrabi Koushki,Ibrahim Abualhaol,Anand Raju,Yang Zhou,Ronnie Salvador Giagone,Huang Shengqiang +5 more
TL;DR: In this article , a review of 42 highly-cited papers, spanning a decade of research (from 2011 to 2021), is presented, covering how they have used ML algorithms, what features they have engineered, which dimensionality reduction techniques have employed, what datasets they have employed for training, and what their evaluation and explanation strategies are.
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