IEEE Transactions on Software Engineering - Special issue on computer security and privacy
The roots of backpropagation: from ordered derivatives to neural networks and political forecasting
The roots of backpropagation: from ordered derivatives to neural networks and political forecasting
State Transition Analysis: A Rule-Based Intrusion Detection Approach
IEEE Transactions on Software Engineering
Intrusion detection with neural networks
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Learning Program Behavior Profiles for Intrusion Detection
Proceedings of the Workshop on Intrusion Detection and Network Monitoring
Detecting Anomalous and Unknown Intrusions Against Programs
ACSAC '98 Proceedings of the 14th Annual Computer Security Applications Conference
Decision tree classifier for network intrusion detection with GA-based feature selection
Proceedings of the 43rd annual Southeast regional conference - Volume 2
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Human interface for cyber security anomaly detection systems
HSI'09 Proceedings of the 2nd conference on Human System Interactions
Decision tree based light weight intrusion detection using a wrapper approach
Expert Systems with Applications: An International Journal
SERENE'11 Proceedings of the Third international conference on Software engineering for resilient systems
Security aspects of SCADA and DCS environments
Critical Infrastructure Protection
An ecological approach to anomaly detection: the EIA model
ICARIS'12 Proceedings of the 11th international conference on Artificial Immune Systems
Divided two-part adaptive intrusion detection system
Wireless Networks
Proceedings of the Eighth Annual Cyber Security and Information Intelligence Research Workshop
Towards learning normality for anomaly detection in industrial control networks
AIMS'13 Proceedings of the 7th IFIP WG 6.6 international conference on Autonomous Infrastructure, Management, and Security: emerging management mechanisms for the future internet - Volume 7943
Improving the performance of neural networks with random forest in detecting network intrusions
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
DIMVA'13 Proceedings of the 10th international conference on Detection of Intrusions and Malware, and Vulnerability Assessment
A survey of intrusion detection techniques for cyber-physical systems
ACM Computing Surveys (CSUR)
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Resiliency and security in control systems such as SCADA and Nuclear plant's in today's world of hackers and malware are a relevant concern. Computer systems used within critical infrastructures to control physical functions are not immune to the threat of cyber attacks and may be potentially vulnerable. Tailoring an intrusion detection system to the specifics of critical infrastructures can significantly improve the security of such systems. The IDS-NNM - Intrusion Detection System using Neural Network based Modeling, is presented in this paper. The main contributions of this work are: 1) the use and analyses of real network data (data recorded from an existing critical infrastructure); 2) the development of a specific window based feature extraction technique; 3) the construction of training dataset using randomly generated intrusion vectors; 4) the use of a combination of two neural network learning algorithms - the Error-Back Propagation and Levenberg-Marquardt, for normal behavior modeling. The presented algorithm was evaluated on previously unseen network data. The IDS-NNM algorithm proved to be capable of capturing all intrusion attempts presented in the network communication while not generating any false alerts.