Intrusion Detection Algorithm Based on Deep Learning for Industrial Control Networks

2019 ◽  
Author(s):  
Yongzhong Li ◽  
Yi Li ◽  
Shipeng Zhang
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Detection Algorithm ◽  
Industrial Control ◽  
Control Networks ◽  
Industrial Control Networks

scholarly journals Exploring Ensemble-Based Class Imbalance Learners for Intrusion Detection in Industrial Control Networks

2021 ◽  
Vol 5 (4) ◽  
pp. 72
Author(s):  
Maya Hilda Lestari Louk ◽  
Bayu Adhi Tama
Keyword(s):  
Intrusion Detection ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Kappa Statistics ◽  
Classifier Ensembles ◽  
Industrial Control ◽  
Control Networks ◽  
Detection Systems ◽  
Random Undersampling ◽  
Industrial Control Networks

Classifier ensembles have been utilized in the industrial cybersecurity sector for many years. However, their efficacy and reliability for intrusion detection systems remain questionable in current research, owing to the particularly imbalanced data issue. The purpose of this article is to address a gap in the literature by illustrating the benefits of ensemble-based models for identifying threats and attacks in a cyber-physical power grid. We provide a framework that compares nine cost-sensitive individual and ensemble models designed specifically for handling imbalanced data, including cost-sensitive C4.5, roughly balanced bagging, random oversampling bagging, random undersampling bagging, synthetic minority oversampling bagging, random undersampling boosting, synthetic minority oversampling boosting, AdaC2, and EasyEnsemble. Each ensemble’s performance is tested against a range of benchmarked power system datasets utilizing balanced accuracy, Kappa statistics, and AUC metrics. Our findings demonstrate that EasyEnsemble outperformed significantly in comparison to its rivals across the board. Furthermore, undersampling and oversampling strategies were effective in a boosting-based ensemble but not in a bagging-based ensemble.

A Guide to Securing Industrial Control Networks: Integrating IT and OT Systems

2020 ◽  
Vol 26 (2) ◽  
pp. 47-53
Author(s):  
Richard Paes ◽  
David C. Mazur ◽  
Bruce K. Venne ◽  
Jack Ostrzenski
Keyword(s):  
Industrial Control ◽  
Control Networks ◽  
Industrial Control Networks

Anomaly Detection for Industrial Control Networks Based on Improved One-Class Support Vector Machine

2020 ◽  
pp. 2150012
Author(s):  
Haicheng Qu ◽  
Jianzhong Zhou ◽  
Jitao Qin ◽  
Xiaorong Tian
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Anomaly Detection ◽  
Support Vector ◽  
Industrial Control ◽  
Control Networks ◽  
Detection Algorithms ◽  
Hinge Loss ◽  
Feature Fitting ◽  
Industrial Control Networks

In traditional network anomaly detection algorithms, the anomaly threshold needs to be defined manually. Keeping this as background, this study proposes an anomaly detection algorithm (VAEOCSVM), which combines the variable auto-encoder (VAE) and one-class support vector machine (OCSVM) to realize anomaly detection in industrial control networks. First, the VAE model is used to obtain the distribution of the original normal sample data represented by the low-dimensional code; the reconstruction error of the VAE model is merged into the new input. Then, using OCSVM’s hinge-loss objective function and the random Fourier feature fitting radial basis function (RBF) kernel method, the OCSVM model is represented and solved using the deep neural network and gradient descent method. Finally, the decision function of the OCSVM model is constructed by using the solved parameter information to realize the detection of abnormal data. The proposed algorithm is compared with other machine-learning-based anomaly detection algorithms in terms of multiple indicators such as precision, recall, and [Formula: see text] score. The experimental results using various datasets show that the proposed algorithm has a better outlier recognition ability than the machine-learning-based anomaly detection algorithms.

An approach for failure recognition in IP-based industrial control networks and systems

2012 ◽  
Vol 22 (6) ◽  
pp. 477-493 ◽  
Author(s):  
Youngjoon Won ◽  
Mi-Jung Choi ◽  
Byungchul Park ◽  
James Won-Ki Hong
Keyword(s):  
Industrial Control ◽  
Control Networks ◽  
Industrial Control Networks

Corrigendum to “Intrusion detection algorithm based on OCSVM in industrial control system”

2016 ◽  
Vol 9 (17) ◽  
pp. 4822-4822
Author(s):  
Wenli Shang ◽  
Peng Zeng ◽  
Ming Wan ◽  
Lin Li ◽  
Panfeng An
Keyword(s):  
Control System ◽  
Intrusion Detection ◽  
Detection Algorithm ◽  
Industrial Control System ◽  
Industrial Control
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