• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1796-1816
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• 2020

The health and safety of elderly and disabled patients who cannot live alone is an important issue. Timely detection of sudden events is necessary to protect these people, and anomaly detection in smart homes is an efficient approach to extracting such information. In the real world, there is a causal relationship between an occupant's behaviour and the order in which appliances are used in the home. Bayesian networks are appropriate tools for assessing the probability of an effect due to the occurrence of its causes, and vice versa. This paper defines different subsets of random variables on the basis of sensory data from a smart home, and it presents an anomaly detection system based on various models of Bayesian networks and drawing upon these variables. We examine different models to obtain the best network, one that has higher assessment scores and a smaller size. Experimental evaluations of real datasets show the effectiveness of the proposed method.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.301-304
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• 2005

For detecting an intrusion based on the anomaly of a user's activities, previous works are concentrated on statistical techniques or frequent episode mining in order to analyze an audit data. But, since they mainly analyze the average behaviour of user's activities, some anomalies can be detected inaccurately. Therefore, we propose an anomaly detection method that utilizes an associative classification for modelling intrusion detection. Finally, we proof that a prediction model built from associative classification method yields better accuracy than a prediction model built from a traditional methods by experimental results.

• Journal of Korea Multimedia Society
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• v.10 no.12
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• pp.1726-1732
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• 2007

The rapid proliferation of wireless networks and mobile computing applications has changed the landscape of network security. Anomaly detection is a pattern recognition task whose goal is to report the occurrence of abnormal or unknown behavior in a given system being monitored. This paper presents an efficient rough set based anomaly detection method that can effectively identify a group of especially harmful internal attackers - masqueraders in cellular mobile networks. Our scheme uses the trace data of wireless application layer by a user as feature value. Based on this, the used pattern of a mobile's user can be captured by rough sets, and the abnormal behavior of the mobile can be also detected effectively by applying a roughness membership function with the age of the user profile. The performance of the proposed scheme is evaluated by using a simulation. Simulation results demonstrate that the anomalies are well detected by the proposed scheme that considers the age of user profiles.

• Journal of Information Processing Systems
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• v.7 no.3
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• pp.435-446
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• 2011

Microprocessors are becoming increasingly vulnerable to soft errors due to the current trends of semiconductor technology scaling. Traditional redundant multi-threading architectures provide perfect fault tolerance by re-executing all the computations. However, such a full re-execution technique significantly increases the verification workload on the processor resources, resulting in severe performance degradation. This paper presents a pro-active verification management approach to mitigate the verification workload to increase its performance with a minimal effect on overall reliability. An anomaly-speculation-based filter checker is proposed to guide a verification priority before the re-execution process starts. This technique is accomplished by exploiting a value similarity property, which is defined by a frequent occurrence of partially identical values. Based on the biased distribution of similarity distance measure, this paper investigates further application to exploit similar values for soft error tolerance with anomaly speculation. Extensive measurements prove that the majority of instructions produce values, which are different from the previous result value, only in a few bits. Experimental results show that the proposed scheme accelerates the processor to be 180% faster than traditional fully-fault-tolerant processor with a minimal impact on overall soft error rate.

• Journal of Chest Surgery
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• v.42 no.4
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• pp.509-512
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• 2009

Ebstein's anomaly is a complex congenital defect of the tricuspid valve and right ventricle. Various surgical methods to repair the regurgitant tricuspid valve have been reported, and most of them depend on monocuspidalization with using the anterior leaflet. We report here on our first experience with Ebstein's anomaly in a 31-year-old female patient who underwent cone reconstruction of the tricuspid valve with using three leaflets.

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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• pp.315-326
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• 2007

The rapid proliferation of wireless networks and mobile computing applications has changed the landscape of network security. Anomaly detection is a pattern recognition task whose goal is to report the occurrence of abnormal or unknown behavior in a given system being monitored. This paper presents a dynamic anomaly detection scheme that can effectively identify a group of especially harmful internal masqueraders in cellular mobile networks. Our scheme uses the trace data of wireless application layer by a user as feature value. Based on the feature values, the use pattern of a mobile's user can be captured by rough sets, and the abnormal behavior of the mobile can be also detected effectively by applying a roughness membership function with both the age of the user profile and weighted feature values. The performance of our scheme is evaluated by a simulation. Simulation results demonstrate that the anomalies are well detected by the proposed dynamic scheme that considers the age of user profiles.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.3
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• pp.544-551
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• 2003

Internet as being generalized, intrusion detection system is needed to protect computer system from intrusions synthetically. We propose a criterion on profiling for intrusion detection system using anomaly detection. We present the cause of false positive on profiling and propose anomaly method to control this. Finally, we propose similarity function to decide whether anomaly action or not for user pattern using pattern database.

• Journal of Chest Surgery
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• v.12 no.2
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• pp.135-139
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• 1979

Interruption of the aortic arch may be defined as discontinuity of the aortic arch in which either an aortic branch vessel or a patent ductus arteriosus supplies the descending aorta. This uncommon lesion was described first by Raphe Steidele in 1778 and was later classified into 3 types by Celoria and Patton. This anomaly rarely occurs as an isolated anomaly. Most commonly, a ventricular septal defect, patent ductus arteriosus, and abnormal arrangement of the brachiocephalic arteries occurs together with arch anomaly. Rarely, more complex anomaly, such as transposition of the great vessel, or single ventricle, is coexistent. We present the case of an 6 year-old boy with D-transposition of great vessel single ventricle, patent ductus arteriosus and patent foramen ovale with interruption of the aortic arch (Type A).

• Journal of Chest Surgery
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• v.35 no.11
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• pp.817-821
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• 2002

There are various surgical techniques in repairing Ebstein anomaly, but residual tricuspid regurgitation and compromized right heart function may ensue in some cases. We report our clinical experience of Ebstein anomaly and atrial flutter in a 19-year-old male patient who underwent simple modified tricuspid annuloplasty, hi-directional cavopulmonary shunt and cryoablation of cavotricuspid isthmus.

• Journal of Communications and Networks
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• v.10 no.1
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• pp.89-97
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• 2008

Anomaly detection systems playa significant role in protection mechanism against attacks launched on a network. The greatest challenge in designing systems detecting anomalous exploits is defining what to measure. Effective yet simple, Shannon entropy metrics have been successfully used to detect specific types of malicious traffic in a number of commercially available IDS's. We believe that Renyi entropy measures can also adequately describe the characteristics of a network as a whole as well as detect abnormal traces in the observed traffic. In addition, Renyi entropy metrics might boost sensitivity of the methods when disambiguating certain anomalous patterns. In this paper we describe our efforts to understand how Renyi mutual information can be applied to anomaly detection as an offline computation. An initial analysis has been performed to determine how well fast spreading worms (Slammer, Code Red, and Welchia) can be detected using our technique. We use both synthetic and real data audits to illustrate the potentials of our method and provide a tentative explanation of the results.