• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권5호
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• pp.2084-2100
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• 2020

Operating system (OS) kernel function call graphs have been widely used in OS analysis and defense. However, most existing methods and tools for generating function call graphs are designed for application programs, and cannot be used for generating OS kernel function call graphs. This paper proposes a virtualization-based call graph generation method called Acquire in Trap (AIT). When target kernel functions are called, AIT dynamically initiates a system trap with the help of a virtualization technique. It then analyzes and records the calling relationships for trap handling by traversing the kernel stacks and the code space. Our experimental results show that the proposed method is feasible for both Linux and Windows OSs, including 32 and 64-bit versions, with high recall and precision rates. AIT is independent of the source code, compiler and OS kernel architecture, and is a universal method for generating OS kernel function call graphs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권7호
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• pp.3076-3092
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• 2020

An IKPCA-ELM-based intrusion detection method is developed to address the problem of the low accuracy and slow speed of intrusion detection caused by redundancies and high dimensions of data in the network. First, in order to reduce the effects of uneven sample distribution and sample attribute differences on the extraction of KPCA features, the sample attribute mean and mean square error are introduced into the Gaussian radial basis function and polynomial kernel function respectively, and the two improved kernel functions are combined to construct a hybrid kernel function. Second, an improved particle swarm optimization (IPSO) algorithm is proposed to determine the optimal hybrid kernel function for improved kernel principal component analysis (IKPCA). Finally, IKPCA is conducted to complete feature extraction, and an extreme learning machine (ELM) is applied to classify common attack type detection. The experimental results demonstrate the effectiveness of the constructed hybrid kernel function. Compared with other intrusion detection methods, IKPCA-ELM not only ensures high accuracy rates, but also reduces the detection time and false alarm rate, especially reducing the false alarm rate of small sample attacks.

• 대한수학회보
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• 제46권1호
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• pp.87-97
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• 2009

We prove the Hyers-Ulam stability of the sine and cosine functional equations in the spaces of generalized functions such as Schwartz distributions, Fourier hyperfunctions, and Gelfand generalized functions.

• 호남수학학술지
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• 제44권3호
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• pp.447-460
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• 2022

We consider a closed subspace ${\tilde{A}}^{{\alpha},m}_q$ (ℂ) of the Fock space A^α,m_q (ℂ) of q-analytic functions with the weight ϕ(z) = -α log |z|²+|z|^2m for any positive integer m. We obtain the corresponding reproducing kernel K^ℂ_α,q,m(z, w) using the weighted Laguerre polynomials and the Mittag-Leffler functions. Finally, we investigate the necessary and sufficient condition on (α, q, m) such that K^ℂ_α,q,m(z, w) is zero-free.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권12호
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• pp.5765-5781
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• 2018

Extreme learning machine (ELM) is emerging as a powerful machine learning method in a variety of application scenarios due to its promising advantages of high accuracy, fast learning speed and easy of implementation. However, how to select the optimal hidden layer of ELM is still an open question in the ELM community. Basically, the number of hidden layer nodes is a sensitive hyperparameter that significantly affects the performance of ELM. To address this challenging problem, we propose to adopt multiple kernel learning (MKL) to design a multi-hidden-layer-kernel ELM (MHLK-ELM). Specifically, we first integrate kernel functions with random feature mapping of ELM to design a hidden-layer-kernel ELM (HLK-ELM), which serves as the base of MHLK-ELM. Then, we utilize the MKL method to propose two versions of MHLK-ELMs, called sparse and non-sparse MHLK-ELMs. Both two types of MHLK-ELMs can effectively find out the optimal linear combination of multiple HLK-ELMs for different classification and regression problems. Experimental results on seven data sets, among which three data sets are relevant to classification and four ones are relevant to regression, demonstrate that the proposed MHLK-ELM achieves superior performance compared with conventional ELM and basic HLK-ELM.

• 한국정보처리학회논문지
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• 제4권10호
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• pp.2521-2532
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• 1997

월드 와이드 웹(WWW)과 MBone 응용 등의 출현으로 인터넷 이용자가 급속히 증가되면서 인터넷 주소 공간의 확장과 멀티미디어 응용을 지원할 수 있도록 기존 인터넷 프로토콜의 개선이 요구됨에 따라 IETF에서는 차세대 인터넷 망 계층 프로토콜로써 IPv6를 개발하였다. 이 논문에서는 IPv6 프로토콜을 Windows NT의 커널 내부에 프로토콜 드라이버로 구현한 내용을 기술하고 있다. 본 연구에서는 IPv6 호스트로 동작되기 위해 필수적으로 요구되는 IPv6 헤더 처리기능, IPv6 주소처리기능, 제어메세지 처리기능, 그룹관리 메세지 처리기능, 이웃탐색 기능이 구현 및 시험되었다. 구현된 IPv6 프로토콜 드라이버 모듈은 하부 통신망 접속 카드와 표준 인터페이스인 NDIS를 통해 접속되며, 디스패치 함수화 Lower-Edge 함수를 이용하여 커널 내부에서 동작하는 드라이버 모듈을 상위 사용자 응용 및 하부 NDIS와 접속시키는 형태로 구현하였다. 구현된 IPv6 프로토콜 드라이버는 커널 모드에서 구현됨으로써 향상된 성능을 제공하며, 다이나믹 링크 라이브러리 형태로 사용자 인터페이스를 제공하므로 응용 프로그램 개발자들이 손쉽게 사용할 수 있도록 하였다.

• East Asian mathematical journal
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• 제13권2호
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• pp.221-225
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• 1997

• East Asian mathematical journal
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• 제9권1호
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• pp.187-199
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• 1993

• Journal of the Korean Data and Information Science Society
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• 제21권1호
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• pp.155-162
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• 2010

In this paper we propose a doubly penalized kernel method which estimates both the mean function and the variance function simultaneously by kernel machines for heteroscedastic autoregressive data. We also present the model selection method which employs the cross validation techniques for choosing the hyper-parameters which aect the performance of proposed method. Simulated examples are provided to indicate the usefulness of proposed method for the estimation of mean and variance functions.

• 대한수학회논문집
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• 제11권2호
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• pp.367-375
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• 1996

Suppose the kernel function $\kappa$ belongs to $S(R^2)$ and is symmetric such that $ < \otimes x, \kappa >\geq 0$ for all $x \in S'(R)$. Let A be the class of functions f such that the function f is measurable on $S'(R)$ with $\int_{S'(R)}$\mid$f((I + tK)^{\frac{1}{2}}x$\mid$^2d\mu(x) < M$ for some $M > 0$ and for all t > 0, where K is the integral operator with kernel function $\kappa$. We show that the \lambda$-potential $G_Kf$ of f is a weak solution of $(\lambda I - \frac{1}{2} \tilde{\Xi}_{0,2}(\kappa))_u = f$.