• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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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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• v.14 no.7
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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.

• Bulletin of the Korean Mathematical Society
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• v.46 no.1
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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.

• Honam Mathematical Journal
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• v.44 no.3
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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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• v.12 no.12
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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.

• The Transactions of the Korea Information Processing Society
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• v.4 no.10
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• pp.2521-2532
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• 1997

The next generation internet protocol, IPv6, have been developed by the IETF according to the requirements of enhancement of classic IP protocols to satisfy the lack of Internet address space as well as the support of multimedia applications. This paper presents an implementation of IPv6 protocols on the Windows NT kernel environment. In this work, we developed and also tested the basic functions, required for operating as an IPv6 host, such as IPv6 header processing, IPv6 address handling, control message processing, group membership processing and neighbor discovery functions. The implemented IPv6 protocol driver module is connected to the lower network interface card through NDIS, a standard network interface. And this driver module that operates within kernel, is implemented as it is connected to upper user applications and lower NDIS using dispatch and lower-edge functions. The developed IPv6 protocol driver can provide not only enhanced performance because it is implemented in kernel mode, but also convenience of usage to the application developers because it gives user interface as a dynamic link library.

• East Asian mathematical journal
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• v.13 no.2
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• pp.221-225
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• 1997

• East Asian mathematical journal
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• v.9 no.1
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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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• v.21 no.1
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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.

• Communications of the Korean Mathematical Society
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• v.11 no.2
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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 $(\lambda I - \frac{1}{2} \tilde{\Xi}_{0,2}(\kappa))_u = f$.