Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Exploring Security in PROFINET IO
COMPSAC '09 Proceedings of the 2009 33rd Annual IEEE International Computer Software and Applications Conference - Volume 01
False data injection attacks against state estimation in electric power grids
Proceedings of the 16th ACM conference on Computer and communications security
Neural network based intrusion detection system for critical infrastructures
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Study on Comparison of Discretization Methods
AICI '09 Proceedings of the 2009 International Conference on Artificial Intelligence and Computational Intelligence - Volume 04
Intrusion detection in SCADA networks
AIMS'10 Proceedings of the Mechanisms for autonomous management of networks and services, and 4th international conference on Autonomous infrastructure, management and security
State-based network intrusion detection systems for SCADA protocols: a proof of concept
CRITIS'09 Proceedings of the 4th international conference on Critical information infrastructures security
Ethernet-based real-time communications with PROFINET IO
ACMOS'05 Proceedings of the 7th WSEAS international conference on Automatic control, modeling and simulation
Learning intrusion detection: supervised or unsupervised?
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Difficulties in modeling SCADA traffic: a comparative analysis
PAM'12 Proceedings of the 13th international conference on Passive and Active Measurement
Real-Time and resilient intrusion detection: a flow-based approach
AIMS'12 Proceedings of the 6th IFIP WG 6.6 international autonomous infrastructure, management, and security conference on Dependable Networks and Services
N-Gram against the machine: on the feasibility of the n-gram network analysis for binary protocols
RAID'12 Proceedings of the 15th international conference on Research in Attacks, Intrusions, and Defenses
An event buffer flooding attack in DNP3 controlled SCADA systems
Proceedings of the Winter Simulation Conference
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Recent trends in automation technology lead to a rising exposition of industrial control systems (ICS) to new vulnerabilities. This requires the introduction of proper security approaches in this field. Prevalent in ICS is the use of access control. Especially in critical infrastructures, however, preventive security measures should be complemented by reactive ones, such as intrusion detection. Beginning from the characteristics of automation networks we outline the implications for a suitable application of intrusion detection in this field. On this basis, an approach for creation of self-learning anomaly detection for ICS protocols is presented. In contrast to other approaches, it takes all network data into account: flow information, application data, and the packet order. We discuss the challenges that have to be solved in each step of the network data analysis to identify future aspects of research towards learning normality in industrial control networks.