• Nuclear Engineering and Technology
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• v.52 no.12
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• pp.2687-2698
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• 2020

Most of the machine learning-based intrusion detection tools developed for Industrial Control Systems (ICS) are trained on network packet captures, and they rely on monitoring network layer traffic alone for intrusion detection. This approach produces weak intrusion detection systems, as ICS cyber-attacks have a real and significant impact on the process variables. A limited number of researchers consider integrating process measurements. However, in complex systems, process variable changes could result from different combinations of abnormal occurrences. This paper examines recent advances in intrusion detection algorithms, their limitations, challenges and the status of their application in critical infrastructures. We also introduce the discussion on the similarities and conflicts observed in the development of machine learning tools and techniques for fault diagnosis and cybersecurity in the protection of complex systems and the need to establish a clear difference between them. As a case study, we discuss special characteristics in nuclear power control systems and the factors that constraint the direct integration of security algorithms. Moreover, we discuss data reliability issues and present references and direct URL to recent open-source data repositories to aid researchers in developing data-driven ICS intrusion detection systems.

• ETRI Journal
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• v.32 no.6
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• pp.881-890
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• 2010

Conventional fire detection systems use physical sensors to detect fire. Chemical properties of particles in the air are acquired by sensors and are used by conventional fire detection systems to raise an alarm. However, this can also cause false alarms; for example, a person smoking in a room may trigger a typical fire alarm system. In order to manage false alarms of conventional fire detection systems, a computer vision-based fire detection algorithm is proposed in this paper. The proposed fire detection algorithm consists of two main parts: fire color modeling and motion detection. The algorithm can be used in parallel with conventional fire detection systems to reduce false alarms. It can also be deployed as a stand-alone system to detect fire by using video frames acquired through a video acquisition device. A novel fire color model is developed in CIE $L^*a^*b^*$ color space to identify fire pixels. The proposed fire color model is tested with ten diverse video sequences including different types of fire. The experimental results are quite encouraging in terms of correctly classifying fire pixels according to color information only. The overall fire detection system's performance is tested over a benchmark fire video database, and its performance is compared with the state-of-the-art fire detection method.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.537-542
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• 1994

This paper gives a general survey of model-based fault detection and dignosis methods. Specific applications of these ideas to boiler systems will also be discussed. A novel aspect of the fault detection technique described here is that it explicitly accounts for the effects of using simplified models and errors from linearizing a nonlinear system at an operation point. Inclusion of these effects is shown to lead to novel fault detection procedures which outperform existing methods when applied to typical fault scenarios in boiler systems.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.4
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• pp.310-319
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• 2003

As the importance and the need for network security are increased, many organizations use the various security systems. They enable to construct the consistent integrated security environment by sharing the network vulnerable information among IDS (Intrusion Detection System), firewall and vulnerable scanner. The multiple IDSes coordinate by sharing attacker's information for the effective detection of the intrusion is the effective method for improving the intrusion detection performance. The system which uses BBA (Blackboard Architecture) for the information sharing can be easily expanded by adding new agents and increasing the number of BB (Blackboard) levels. Moreover the subdivided levels of blackboard enhance the sensitivity of the intrusion detection. For the simulation, security models are constructed based on the DEVS (Discrete Event system Specification) formalism. The intrusion detection agent uses the ES (Expert System). The intrusion detection system detects the intrusions using the blackboard and the firewall responses to these detection information.

• Journal of Power Electronics
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• v.16 no.3
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• pp.1141-1151
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• 2016

This study proposes a novel islanding detection method for distributed photovoltaic (PV) systems with multi-inverters based on a combination of the power line carrier communication and Sandia frequency shift islanding detection methods. A parameter design method is provided for the novel scheme. On the basis of the designed parameters, the effect of frequency measurement errors and grid line impedance on the islanding detection performance of PV systems is analyzed. Experimental results show that the theoretical analysis is correct and that the novel method with the designed parameters has little effect on the power quality of the inverter output current. Non-detection zones are not observed, and a high degree of reliability is achieved. Moreover, the proposed islanding detection method is suitable for distributed PV systems with multi-inverters.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.5
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• pp.1400-1420
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• 2012

Object detection and tracking using visual sensors is a critical component of surveillance systems, which presents many challenges. This paper addresses the enhancement of object detection and tracking via the combination of multiple visual sensors. The enhancement method we introduce compensates for missed object detection based on the partial detection of objects by multiple visual sensors. When one detects an object or more visual sensors, the detected object's local positions transformed into a global object position. Local and global information exchange allows a missed local object's position to recover. However, the exchange of the information may degrade the detection and tracking performance by incorrectly recovering the local object position, which propagated by false object detection. Furthermore, local object positions corresponding to an identical object can transformed into nonequivalent global object positions because of detection uncertainty such as shadows or other artifacts. We improved the performance by preventing the propagation of false object detection. In addition, we present an evaluation method for the final global object position. The proposed method analyzed and evaluated using case studies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.601-617
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• 2014

In digital radio broadcasting systems, long delays are incurred in service start time when tuning to a particular frequency because several synchronization steps, such as symbol timing synchronization, frame synchronization, and carrier frequency offset and sampling frequency offset compensation are necessary. Therefore, the operation of the synchronization blocks causes delays ranging from several hundred milliseconds to a few seconds until the start of the radio service after frequency tuning. Furthermore, if spectrum inversed signals are transmitted in digital radio broadcasting systems, the receivers are unable to decode them, even though most receivers can demodulate the spectral inversed signals in analog radio broadcasting systems. Accordingly, fast synchronization techniques and a method for spectral inversion detection are required in digital radio broadcasting systems that are to replace the analog radio systems. This paper presents a joint detection method of frame, integer carrier frequency offset, and spectrum inversion for DRM Plus digital broadcasting systems. The proposed scheme can detect the frame and determine whether the signal is normal or spectral inversed without any carrier frequency offset and sampling frequency offset compensation, enabling fast frame synchronization. The proposed method shows outstanding performance in environments where symbol timing offsets and sampling frequency offsets exist.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.2
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• pp.11-17
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• 2018

IDS/IPS and networked computer systems are playing an increasingly important role in our society. They have been the targets of a malicious attacks that actually turn into intrusions. That is why computer security has become an important concern for network administrators. Recently, various Detection/Prevention System schemes have been proposed based on various technologies. However, the techniques, which have been applied in many systems is useful for existing intrusion patterns on standard-only systems. Therefore, probe detection of private clouds using BlockChain has become a major security protection technology to detection potential attacks. In addition, BlockChain and Probe detection need to take into account the relationship between the various factors. We should develop a new probe detection technology that uses BlockChain to fine new pattern detection probes in cloud service security in the end. In this paper, we propose a probe detection using Fuzzy Cognitive Map(FCM) and Self Adaptive Module(SAM) based on service security using BlockChain technology.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.2
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• pp.140-146
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• 2024

Active electronically scanning antennas are faster and more flexible in beam-scheduling than mechanical antennas. Thus, they require an advanced resource management or detection methods to operate efficiently. In a surveillance radar performing periodic detection, alert-confirm detection is an excellent method to improve the cumulative detection probability by reducing the period while maintaining the detection probability. This paper proposes a design method for alert-confirm detection based on the parameters of the conventional design. We developed a simulator based on simulink@matworks and verified the result through Monte Carlo simulation.

• Journal of applied mathematics & informatics
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• v.30 no.5_6
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• pp.847-864
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• 2012

In recent years, using computer networks (wired and wireless networks) has been widespread in many applications. As computer networks become increasingly complex, the accompanied potential threats also grow to be more sophisticated and as such security has become one of the major concerns in them. Prevention methods alone are not sufficient to make them secure; therefore, detection should be added as another defense before an attacker can breach the system. Intrusion Detection Systems (IDSs) have become a key component in ensuring systems and networks security. An IDS monitors network activities in order to detect malicious actions performed by intruders and then initiate the appropriate countermeasures. In this paper, we present a survey and taxonomy of intrusion detection systems and then evaluate and compare them.