• Journal of the Korean Data and Information Science Society
• /
• v.25 no.5
• /
• pp.971-986
• /
• 2014

When thousands of hypotheses are tested simultaneously, the probability of rejecting any true hypotheses increases, and large multiplicity problems are generated. To solve these problems, researchers have proposed different approaches to multiple testing methods, considering family-wise error rate (FWER), false discovery rate (FDR) or false nondiscovery rate (FNR) as a type I error and some test statistics. In this article, we discuss Bonferroni (1960), Holm (1979), Benjamini and Hochberg (1995) and Benjamini and Yekutieli (2001) procedures based on T statistics, modified T statistics or local-pooled-error (LPE) statistics. We also consider Sun and Cai (2007) procedure based on Z statistics. These procedures are compared in the simulation and applied to Arabidopsis microarray gene expression data to identify differentially expressed genes.

• Journal of KIISE:Computing Practices and Letters
• /
• v.14 no.1
• /
• pp.111-115
• /
• 2008

We present a simple and effective method to reduce loop-related false alarms raised by buffer-overrun static program analyzer. Interval domain buffer-overrun analyzer raise many false alarms in analyzing programs that frequently use loops and arrays. Firstly, we classified patterns of loop-related false alarms for loop-intensive programs, such as embedded programs or mathematical libraries. After that we designed a simple and effective false alarm refiner, specialized for the loop-related false alarms we classified. After the normal analysis of program in which alarms considered as false. We implemented this method on our buffer-overrun analyzer with the result that our refinement method decreased the number of false alarms by 32% of total amount the analyzer reported.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.42 no.4
• /
• pp.848-854
• /
• 2017

Recently, false reports to governmental offices such as police stations have not been decreased. As a result, if a violent crime or a fire occurs that needs to be promptly responded to and reacted to these accidents in real time, it may lead to serious results such as loss of life. Also, the waste of police enforcement and administration due to false reporting can cause serious problems. In this paper, we try to clarify the difference between the actual and false reports based on the actual sound sources which were reported to the police stations. In addition, we will intend to develop a false report discrimination system that can identifies false reports and actual reports based on this.

• Korean journal of communication and information
• /
• v.55
• /
• pp.119-139
• /
• 2011

This study investigated the general perception of government public relations practitioners in the Noh Moo Hyun and Lee Myung Bak administrations toward media relations and false reports. It examined whether these government public relations practitioners' perceptions are different according to changing government public relations circumstances. This study also investigated how the perceptions of public relations practitioners toward media relations in two different governments affect the overall perception toward false reports. The results showed that the two groups had different perspectives toward media relations, false reports and the causes of false reports. Moreover, the perspectives toward media relations influenced their opinions about false reports. However, they had similar opinions about the way of preventing false reports.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.16 no.6
• /
• pp.111-121
• /
• 2006

As Internet lastly grows, network attack techniques are transformed and new attack types are appearing. The existing network-based intrusion detection systems detect well known attack, but the false-positive or false-negative against unknown attack is appearing high. In addition, The existing network-based intrusion detection systems is difficult to real time detection against a large network pack data in the network and to response and recognition against new attack type. Therefore, it requires method to heighten the detection rate about a various large dataset and to reduce the false-positive. In this paper, we propose method to reduce the false-positive using multi-level detection algorithm, that is combine the multidimensional Apriori algorithm and the modified Negative Selection algorithm. And we apply this algorithm in intrusion detection and, to be sure, it has a good performance.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.9
• /
• pp.4183-4204
• /
• 2018

The existing en-route filtering schemes only consider some simple false data injection attacks, which results in lower safety performance. In this paper, we propose an efficient geographical information-based en-route filtering scheme (EGEFS), in which each forwarding node verifies not only the message authentication codes (MACs), but also the report identifier and the legitimacy and authenticity of locations carried in a data report. Thus, EGEFS can defend against not only the simple false data injection attacks and the replay attack, but also the collusion attack with forged locations proposed in this paper. In addition, we propose a new method for electing the center-of-stimulus (CoS) node, which can ensure that only one detecting node will be elected as the CoS node to generate one data report for an event. The simulation results show that, compared to the existing en-route filtering schemes, EGEFS has higher safety performance, because it can resist more types of false data injection attacks, and it also has higher filtering efficiency and lower energy expenditure.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2016.10a
• /
• pp.525-526
• /
• 2016

machine learning 기법 중 하나인 logistic regression을 이용하여 benign sample과 breast cancer sample을 구분할 수 있는데, 이 연구를 통해 classification의 정확도를 높이고 false positive와 false negative의 비율을 줄이려고 했다. 그래서 logistic regression의 parameter 값을 바탕으로 regression function에 영향을 많이 주는 feature 들을 선택하고, 영향력 있는 feature 들을 더한 새로운 feature를 추가했다. 그 결과 정확도와 F-score가 증가했으며, false positive, false negative의 비율이 감소했다.

• Proceedings of the Korean Information Science Society Conference
• /
• 2004.04a
• /
• pp.247-249
• /
• 2004

IDS에서 발생되는 경보의 수는 최근 인터넷 애플리케이션의 발달로 인하여 급격히 증가하고 있으며. 그로 인해 오 경보의 수도 함께 증가하고 있다. 발생된 경보들은 침입탐지 시스템의 성능저하와 alert flooding 의 원인이 된다. 따라서 이 논문에서는 다량의 경보 중에서 오 경보(False Alarm)의 발생을 감소시킬 수 있는 오 경보 분류 모델을 제안한다. 제안된 오 경보 분류 모델은 데이터 마이닝 기법들 중에서 분류 기법을 기반으로 구현되었다. 실험 을 통해서 IDS에서 발생하는 경보 중에서 정상데이터이나 공격으로 잘못 판단하여 발생하는 False Positive의 발생율이 현저히 감소됨을 확인할 수 있었다. 제안된 오 경보 분류 모델은 경보메시지 축약의 효과가 있으며 침입탐지 시스템의 탐지율을 높이는데 활용될 수 있다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2003.11c
• /
• pp.1843-1846
• /
• 2003

네트워크 기반의 공격 및 비정상 행위를 정확히 탐지하고 판단하기 위한 기존의 탐지 모델은 공격 룰셋의 패턴매칭 기반인 Misuse Detection System을 사용하고 있다. 그러나 이 시스템의 특성상 새로운 공격의 미탐지 및 공격 오인등으로 False Positive 가 높다는 단점이 있다. 본 논문은 전체 시스템의 성능을 판정하는 False Positve 에러율을 줄여 성능을 향상하기 위해 Meachine Learning기반의 Anomaly Detection System 을 결합한 새로운 탐지 모델을 제안하고자 한다. Anomaly Detection System 은 정상행위에 대한 비교적 높은 탐지율과 새로운 공격에 대한 탐지가 용이하다. 본 논문에서는 각 시스템의 탐지모델로 Snort 와 인스턴스 기반의 알고리즘인 IBL 을 사용했으며, 결합모델의 타당성을 검증하기 위해서 각 탐지 모델의 False Positive와 False Negative 에러율을 측정하였다.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.6 no.5
• /
• pp.684-688
• /
• 2002

One of the Problems, which the Intrusion Detection System has, is a False Positive. This False make to low condition of the Intrusion Detection System. The cause of the False Positive is that the learning is not enough during audit data learning steps. Therefore, in this paper, 1 propose the method of the Intrusion Detection System that be learnt audit data to agent with association rule.