• Annual Conference on Human and Language Technology
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• 2010.10a
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• pp.3-8
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• 2010

본 연구에서는 일련의 연구에서 수집된 영작문 오류 유형의 정제된 자료를 토대로 연관 규칙을 생성하고, 학습을 통해서 효용성이 검증된 연관 규칙을 활용해서 영작문 데이터의 형태 통사 오류를 자동으로 탐지한다. 영작문 데이터에서 형태 통사 오류를 찾아내는 작업은 많은 시간과 자원이 소요되는 작업이므로 자동화가 필수적이다. 기존의 연구들이 통계적 모델을 활용한 어휘적 오류에 치중하거나 언어 이론적 틀에 근거한 통사 처리에 집중하는 반면에, 본 연구는 데이터 마이닝을 통해서 정제된 데이터에서 연관 규칙을 생성하고 이를 검증한 후 형태 통사 오류를 감지한다. 이전 연구들에서는 이론적 틀에 맞추어진 규칙 생성이나 언어 모델 생성을 위한 대량의 코퍼스 데이터와 같은 다량의 지식 베이스 생성이 필수적인데, 본 연구는 적은 양의 정제된 데이터를 활용한다. 영작문 오류 유형의 형태 통사 연관 규칙을 생성하기 위해서 Apriori 알고리즘을 활용하였다. 알고리즘을 통해서 생성된 연관 규칙 중 잘못된 규칙이 생성될 가능성이 있으므로, 상관성 검정, 코사인 유사도와 같은 규칙 효용성의 통계적 검증을 활용해서 타당한 규칙만을 학습하였다. 이를 통해서 축적된 연관 규칙들을 영작문 오류를 자동으로 탐지하는 실험에 활용하였다.

• The Journal of the Korea Contents Association
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• v.7 no.5
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• pp.36-46
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• 2007

This paper presents an automated English scoring system designed to help non-native speakers of English, Korean-speaking learners in particular. The system is developed to help the 3rd grade students in junior high school improve their English grammar skills. Without human's efforts, the system identifies grammar errors in English sentences, provides feedback on the detected errors, and scores the sentences. Detecting grammar errors in the system requires implementing a special type of rules in addition to the rules to parse grammatical sentences. Error production rules are implemented to analyze ungrammatical sentences and recognize syntactic errors. The rules are collected from the junior high school textbooks and real student test data. By firing those rules, the errors are detected followed by setting corresponding error flags, and the system continues the parsing process without a failure. As the final step of the process, the system scores the student sentences based on the errors detected. The system is evaluated with real English test data produced by the students and the answers provided by human teachers.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.10
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• pp.2741-2749
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• 2009

There have been many researches to detect and recover from faults on smart home environment, because these faults should lower its reliability. while, most of these researches have addressed functional defects of devices or software malfunction, few attempts have been made to deal with faults which may occur due to the inter relationships among devices. In this paper, we define the relationships among devices as rules, and propose a method for generating fault diagnosis knowledge which defines the symptoms and causes of faults. We classify the contexts of devices into two sets, depending on whether it satisfies the rules or not. when this method is applied to smart home environment, it is feasible not only to detect faults that may occur due to the relationships among devices but to identify their causes at real time.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.12
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• pp.2287-2297
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• 2007

The proposed ANIDS(Advanced Network Intrusion Detection System) which is network-based IDS using Association Rule Mining, collects the packets on the network, analyze the associations of the packets, generates the pattern graph by using the highly associated packets using Association Rule Mining, and detects the intrusion by using the generated pattern graph. ANIDS consists of PMM(Packet Management Module) collecting and managing packets, PGGM(Pattern Graph Generate Module) generating pattern graphs, and IDM(Intrusion Detection Module) detecting intrusions. Specially, PGGM finds the candidate packets of Association Rule large than $Sup_{min}$ using Apriori algorithm, measures the Confidence of Association Rule, and generates pattern graph of association rules large than $Conf_{min}$. ANIDS reduces the false positive by using pattern graph even before finalizing the new pattern graph, the pattern graph which is being generated is compared with the existing one stored in DB. If they are the same, we can estimate it is an intrusion. Therefore, this paper can reduce the speed of intrusion detection and the false positive and increase the detection ratio of intrusion.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.451-456
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• 2004

Signature based intrusion detection system (IDS), having stored rules for detecting intrusions at the library, judges whether new inputs are intrusion or not by matching them with the new inputs. However their policy has two restrictions generally. First, when they couldn't make rules against new intrusions, false negative (FN) errors may are taken place. Second, when they made a lot of rules for maintaining diversification, the amount of resources grows larger proportional to their amount. In this paper, we propose the learning algorithm which can evolve the competent of anomaly detectors having the ability to detect anomalous attacks by genetic algorithm. The anomaly detectors are the population be composed of by following the negative selection procedure of the biological immune system. To show the effectiveness of proposed system, we apply the learning algorithm to the artificial network environment, which is a computer security system.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.75-81
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• 2004

Signature based intrusion detection system (IDS), having stored rules for detecting intrusions at the library, judges whether new inputs are intrusion or not by matching them with the new inputs. However their policy has two restrictions generally. First, when they couldn`t make rules against new intrusions, false negative (FN) errors may are taken place. Second, when they made a lot of rules for maintaining diversification, the amount of resources grows larger proportional to their amount. In this paper, we propose the learning algorithm which can evolve the competent of anomaly detectors having the ability to detect anomalous attacks by genetic algorithm. The anomaly detectors are the population be composed of by following the negative selection procedure of the biological immune system. To show the effectiveness of proposed system, we apply the learning algorithm to the artificial network environment, which is a computer security system.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.954-957
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• 2019

한국어 맞춤법 검사기는 현재 여러 곳에서 개발하여 서비스를 제공하고 있으며 다양한 목적으로 사용되고 있다. 하지만 이들은 맞춤법이나 띄어쓰기를 검사할 뿐, 존댓말과 같은 높임표현이 잘못 사용되었는지는 검사할 수 없다. 최근에는 맞춤법 검사를 위해 통계적 방식과 딥러닝을 사용하고 있지만, 본 연구에서는 규칙 기반을 활용하여 사용자가 텍스트를 입력하면 잘못된 높임표현을 탐지하여 그에 대한 오류 정보를 제공하고, 올바른 표현으로 교정하는 프로그램을 개발하였다. 구문 분석기를 사용하여 주어-서술어 구조를 파악하고, 형태소 분석기를 활용하여 높임표현을 탐지한 후 격틀사전의 정보를 이용하여 존댓말 오류를 탐지한다. 본 연구는 격식이 있는 문서를 검사할 때나, 한국어 높임말에 익숙하지 않은 외국인들에게 도움이 될 수 있을 것이다.

• The KIPS Transactions:PartB
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• v.14B no.3 s.113
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• pp.223-230
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• 2007

The purpose of developing an automated scoring system for English composition is to score the tests for writing English sentences and to give feedback on them without human's efforts. This paper presents an automated system to score English composition, whose input is a single sentence, not an essay. Dealing with a single sentence as an input has some advantages on comparing the input with the given answers by human teachers and giving detailed feedback to the test takers. The system has been developed and tested with the real test data collected through English tests given to the third grade students in junior high school. Two steps of the process are required to score a single sentence. The first process is analyzing the input sentence in order to detect possible errors, such as spelling errors, syntactic errors and so on. The second process is comparing the input sentence with the given answer to identify the differences as errors. The results produced by the system were then compared with those provided by human raters.

• Journal of KIISE:Information Networking
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• v.30 no.3
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• pp.397-406
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• 2003

As the information technology grows interests in the intrusion detection system (IDS), which detects unauthorized usage, misuse by a local user and modification of important data, has been raised. In the field of anomaly-based IDS several artificial intelligence techniques such as hidden Markov model (HMM), artificial neural network, statistical techniques and expert systems are used to model network rackets, system call audit data, etc. However, there are undetectable intrusion types for each measure and modeling method because each intrusion type makes anomalies at individual measure. To overcome this drawback of single-measure anomaly detector, this paper proposes a multiple-measure intrusion detection method. We measure normal behavior by systems calls, resource usage and file access events and build up profiles for normal behavior with hidden Markov model, statistical method and rule-base method, which are integrated with a rule-based approach. Experimental results with real data clearly demonstrate the effectiveness of the proposed method that has significantly low false-positive error rate against various types of intrusion.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1043-1057
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• 2015

In order to recognize intelligent threats quickly and detect and respond to them actively, major public bodies and private institutions operate and administer an Intrusion Detection Systems (IDS), which plays a very important role in finding and detecting attacks. However, most IDS alerts have a problem that they generate false positives. In addition, in order to detect unknown malicious codes and recognize and respond to their threats in advance, APT response solutions or actions based systems are introduced and operated. These execute malicious codes directly using virtual technology and detect abnormal activities in virtual environments or unknown attacks with other methods. However, these, too, have weaknesses such as the avoidance of the virtual environments, the problem of performance about total inspection of traffic and errors in policy. Accordingly, for the effective detection of intrusion, it is very important to enhance security monitoring, consequentially. This study discusses a plan for the reduction of false positives as a plan for the enhancement of security monitoring. As a result of an experiment based on the empirical data of G, rules were drawn in three types and 11 kinds. As a result of a test following these rules, it was verified that the overall detection rate decreased by 30% to 50%, and the performance was improved by over 30%.