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
The role of artificial intelligence and machine learning in wireless networks security: principle, practice and challenges
Journal ArticleDOI
A cyber-secure control-detector architecture for nonlinear processes
TL;DR: This work presents a detector-integrated two-tier control architecture capable of identifying the presence of various types of cyber-attacks, and ensuring closed-loop system stability upon detection of the cyber- attacks, and allowing convenient reconfiguration of the control system to stabilize the process to its operating steady state.
Proceedings ArticleDOI
INSOMNIA: Towards Concept-Drift Robustness in Network Intrusion Detection
Giuseppina Andresini,Feargus Pendlebury,Fabio Pierazzi,Corrado Loglisci,Annalisa Appice,Lorenzo Cavallaro +5 more
TL;DR: INSOMNIA as discussed by the authors is a semi-supervised intrusion detector which continuously updates the underlying machine learning model as network traffic characteristics are affected by concept drift, using active learning to reduce latency in the model updates, label estimation to reduce labeling overhead, and apply explainable AI to better interpret how the model reacts to the shifting distribution.
Proceedings ArticleDOI
EnClass: Ensemble-Based Classification Model for Network Anomaly Detection in Massive Datasets
TL;DR: A new hybrid anomaly detection scheme called as Ensemble-based Classification Model for Network Anomaly Detection (EnClass) is proposed to detect anomalies in real- world networking datasets to remove gaps in existing solutions.
Journal ArticleDOI
Employing a Machine Learning Approach to Detect Combined Internet of Things Attacks against Two Objective Functions Using a Novel Dataset
TL;DR: The machine learning approach is successful in detecting combined attacks against two popular OFs of RPL based on the power and network metrics in which MLP and RF algorithms are the most successful classifier deployment for single and ensemble models.
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