• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권8호
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• pp.2964-2983
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• 2015

As the Android operating system has become a key target for malware authors, Android protection has become a thriving research area. Beside the proved importance of system permissions for malware analysis, there is a lot of overlapping in permissions between malware apps and goodware apps. The exploitation of them effectively in malware detection is still an open issue. In this paper, to investigate the feasibility of neuro-fuzzy techniques to Android protection based on system permissions, we introduce a self-adaptive neuro-fuzzy inference system to classify the Android apps into malware and goodware. According to the framework introduced, the most significant permissions that characterize optimally malware apps are identified using Information Gain Ratio method and encapsulated into patterns of features. The patterns of features data is used to train and test the system using stratified cross-validation methodologies. The experiments conducted conclude that the proposed classifier can be effective in Android protection. The results also underline that the neuro-fuzzy techniques are feasible to employ in the field.

• 한국표면공학회:학술대회논문집
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• 한국표면공학회 2015년도 추계학술대회 논문집
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• pp.242-243
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• 2015

Fault detection using optical emission spectra with modified K-means cluster analysis and principal component anal ysis are demonstrated for inductive coupl ed pl asma cl eaning processes. The optical emission spectra from optical emission spectroscopy (OES) are used for measurement. Furthermore, Principal component analysis and K-means cluster analysis algorithm is modified and applied to real-time detection and sensitivity enhancement for fluorocarbon cleaning processes. The proposed techniques show clear improvement of sensitivity and significant noise reduction when they are compared with single wavelength signals measured by OES. These techniques are expected to be applied to various plasma monitoring applications including fault detections as well as chamber cleaning endpoint detection.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2007년도 하계종합학술대회 논문집
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• pp.381-382
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• 2007

In this paper, crack detection technique of concrete bridge is proposed robust against shadow and noise. Our technique consists of two steps. In the first step, crack candidate region is detected by preprocessing. Preprocessing techniques such as median filter, isolated point filter and morphological techniques, use utilized for detection of crack candidate regions. In the final step, crack is detected from crack candidate region by considering any connectivity between cracks. By experimental results, performance is improved 6.8% over the existing method.

• Journal of Information Processing Systems
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• 제13권5호
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• pp.1203-1212
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• 2017

Intrusion detection systems (IDSs) are crucial in this overwhelming increase of attacks on the computing infrastructure. It intelligently detects malicious and predicts future attack patterns based on the classification analysis using machine learning and data mining techniques. This paper is devoted to thoroughly evaluate classifier ensembles for IDSs in IEEE 802.11 wireless network. Two ensemble techniques, i.e. voting and stacking are employed to combine the three base classifiers, i.e. decision tree (DT), random forest (RF), and support vector machine (SVM). We use area under ROC curve (AUC) value as a performance metric. Finally, we conduct two statistical significance tests to evaluate the performance differences among classifiers.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2019년도 추계학술발표대회
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• pp.760-761
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• 2019

Smart manufacturing is a process of integrating computer-related technologies in production and by doing so, achieving more efficient production management. The recent development of supercomputers has led to the broad utilization of artificial intelligence (AI) and machine learning techniques useful in predicting specific patterns. Despite the usefulness of AI and machine learning techniques in smart manufacturing processes, there are many fundamental issues with the direct deployment of these technologies related to data management. In this paper, we focus on solving the outlier detection issue in smart manufacturing applications. More specifically, we apply a state-of-the-art outlier detection technique, called Elliptic Envelope, to detect anomalies in simulation-based collected data.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2015년도 추계학술발표대회
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• pp.1411-1412
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• 2015

Detection of road lanes is an important technology, which is being used in autonomous vehicles from last few years. This method is very helpful and supportive for the drivers to provide them safety and to avoid road accidents. Alot of methods are being used to detect road lane markings. We can categorize them into three major categories: sensor-based, feature-based, and model-based methods. And in this study we give the comprehensive survey on lane marking techniques.

• Smart Structures and Systems
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• 제22권6호
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• pp.711-725
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• 2018

Most of the practical engineering structures exhibit nonlinearity due to nonlinear dynamic characteristics of structural joints, nonlinear boundary conditions and nonlinear material properties. Hence, it is highly desirable to detect and characterize the nonlinearity present in the system in order to assess the true behaviour of the structural system. Further, these identified nonlinear features can be effectively used for damage diagnosis during structural health monitoring. In this paper, we focus on the detection of the nonlinearity present in the system by confining our discussion to only a few selective time-frequency analysis and multivariate analysis based techniques. Both damage induced nonlinearity and inherent structural nonlinearity in healthy systems are considered. The strengths and weakness of various techniques for nonlinear detection are investigated through numerically simulated two different classes of nonlinear problems. These numerical results are complemented with the experimental data to demonstrate its suitability to the practical problems.

• 한국정보기술학회 영문논문지
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• 제9권1호
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• pp.1-10
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• 2019

In recent years, information collection of attacks through stealth port scanning technology has become more sophisticated. The most commonly used Nmap port scanner supports a variety of stealth scanning technologies along with the existing scanning techniques. Nmap also supports Idle scan that is different from conventional stealth scans. This is a more sophisticated stealth scan technique by applying the SYN scan and ACK scan techniques. In previous studies, the detection of Idle scanning was on zombie system, but was not on victim system. In this paper, we propose an effective detection method of Idle scan on victim system. The Idle scanning is composed of two stages; they are probing the zombie and victim system and scanning the victim system. We analyzed the characteristics of the two stages. The characteristics, we captured, are that SYN and RST packets are different from normal packet. We applied them to detection method, then Idle scanning is detected effectively.

• 융합보안논문지
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• 제23권5호
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• pp.101-106
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• 2023

최근 사이버 공격은 지능적이고 지속적인 피싱사이트와 악성코드를 활용한 해킹 기법을 활용하는 사회공학적 공격이 증가하고 있다. 개인 보안이 중요해지는 만큼 웹 어플리케이션을 이용해 악성 URL 여부를 판별하는 방법과 솔루션이 요구되고 있다. 본 논문은 악성 URL를 탐지하는 정확도가 높은 기법들을 비교하여 각각의 특징과 한계를 알아가고자 한다. 웹 평판 DB 등 기반 URL 탐지 사이트와 특징을 활용한 분류알고리즘 모델과 비교하여 효율적인 URL 이상탐지 기법을 제안하고자 한다.

• 지능정보연구
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• 제18권1호
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• pp.125-141
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• 2012

본 연구는 최근 그 중요성이 한층 높아지고 있는 침입탐지시스템(IDS, Intrusion Detection System)의 침입탐지모형을 개선하기 위한 방안으로 유전자 알고리즘에 기반한 새로운 통합모형을 제시한다. 본 연구의 제안모형은 서로 상호보완적 관계에 있는 이분류 모형인 로지스틱 회귀분석(LOGIT, Logistic Regression), 의사결정나무(DT, Decision Tree), 인공신경망 (ANN, Artificial Neural Network), 그리고 SVM(Support Vector Machine)의 예측결과에 적절한 가중치를 부여해 최종 예측결과를 산출하도록 하였는데, 이 때 최적 가중치의 탐색을 위한 방법으로는 유전자 알고리즘을 사용한다. 아울러, 본 연구에서는 1차적으로 오탐지율을 최소화하는 최적의 모형을 산출한 뒤, 이어 비대칭 오류비용 개념을 반영해 오탐지로 인해 발생할 수 있는 전체 비용을 최소화할 수 있는 최적 임계치를 탐색, 최종적으로 가장 비용 효율적인 침입탐지모형을 도출하고자 하였다. 본 연구에서는 제안모형의 우수성을 확인하기 위해, 국내 한 공공기관의 보안센서로부터 수집된 로그 데이터를 바탕으로 실증 분석을 수행하였다. 그 결과, 본 연구에서 제안한 유전자 알고리즘 기반 통합모형이 인공신경망이나 SVM만으로 구성된 단일모형에 비해 학습용과 검증용 데이터셋 모두에서 더 우수한 탐지율을 보임을 확인할 수 있었다. 비대칭 오류비용을 고려한 전체 비용의 관점에서도 단일모형으로 된 비교모형에 비해 본 연구의 제안모형이 더 낮은 비용을 나타냄을 확인할 수 있었다. 이렇게 실증적으로 그 효과가 검증된 본 연구의 제안 모형은 앞으로 보다 지능화된 침입탐지시스템을 개발하는데 유용하게 활용될 수 있을 것으로 기대된다.