• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.494-510
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• 2024

Internet users are exposed to sophisticated cyberattacks that intrusion detection systems have difficulty detecting. Therefore, research is increasing on intrusion detection methods that use artificial intelligence technology for detecting novel cyberattacks. Unsupervised learning-based methods are being researched that learn only from normal data and detect abnormal behaviors by finding patterns. This study developed an anomaly-detection method based on unsupervised machines and deep learning for a network intrusion detection system (NIDS). We present a hybrid anomaly detection approach based on unsupervised learning techniques using the autoencoder (AE), Isolation Forest (IF), and Local Outlier Factor (LOF) algorithms. An oversampling approach that increased the detection rate was also examined. A hybrid approach that combined deep learning algorithms and traditional machine learning algorithms was highly effective in setting the thresholds for anomalies without subjective human judgment. It achieved precision and recall rates respectively of 88.2% and 92.8% when combining two AEs, IF, and LOF while using an oversampling approach to learn more unknown normal data improved the detection accuracy. This approach achieved precision and recall rates respectively of 88.2% and 94.6%, further improving the detection accuracy compared with the hybrid method. Therefore, in NIDS the proposed approach provides high reliability for detecting cyberattacks.

• The KIPS Transactions:PartD
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• v.15D no.1
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• pp.31-40
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• 2008

We generally use lower-dimensional transformations to convert high-dimensional sequences into low-dimensional points in similar sequence matching. These traditional transformations, however, show different characteristics in indexing performance by the type of time-series data. It means that the selection of lower-dimensional transformations makes a significant influence on the indexing performance in similar sequence matching. To solve this problem, in this paper we propose a hybrid approach that integrates multiple transformations and uses them in a single multidimensional index. We first propose a new notion of hybrid lower-dimensional transformation that exploits different lower-dimensional transformations for a sequence. We next define the hybrid distance to compute the distance between the transformed sequences. We then formally prove that the hybrid approach performs the similar sequence matching correctly. We also present the index building and the similar sequence matching algorithms that use the hybrid approach. Experimental results for various time-series data sets show that our hybrid approach outperforms the single transformation-based approach. These results indicate that the hybrid approach can be widely used for various time-series data with different characteristics.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.4
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• pp.193-197
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• 2008

In today's Pay TV market, the importance of TPS for Pay TV operators is increasing rapidly. IP Hybrid services are essential for satellite broadcasters. This paper explains the various digital satellite broadcasting service trend, especially IP based hybrid service approach of satellite broadcasters(BskyB, CanalSat, DirecTV, etc.). Also this paper introduces SkyLife IP hybrid business approach to achieve a strong leadership in Pay TV market and related IP convergence market in Korea.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.6
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• pp.316-324
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• 2003

This paper applied Elite-based Simplex-GA hybrid approach combined with Muptipop-GA (ESGA) to determining the location, size and number of capacitors to improve voltage profile and minimize power losses in unbalanced distribution systems. One of the main obstacles in applying GA to complex problems has been the high computational cost due to their slow convergence rate. To alleviate this difficulty, ESGA approach was developed that combines Elite-based Simplex-GA hybrid approach with Muptipop-GA. The objective function formulated consists of two terms: cost for energy losses and cost related to capacitor purchase and capacitor installation. The cost function associated with capacitor placement is considered as a step function due to banks of standard discrete capacities. Its efficiency was proved through the application in IEEE 13 bus and 34 bus test systems and was compared with several methods using GA.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.7
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• pp.586-593
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• 2000

In this paper we propose a new approach to the autonomous wall-following of wheeled mobile robots using hybrid system reference motion synthesis and generation. The hybrid system approach is in-troduced to the motion control of nonholonomic mobile robots for the indoor navigation problems. In the dis-crete event system the discrete states are defined by the user-defined constraints and the reference mo-tion commands are specified in the abstracted motions. The hybrid control system applied for the non-holonomic mobile robots can combine the motion planning and autonomous navigation with obstacle avoid-ance for the indoor navigation problem. Simulation results show that hybrid system approach is an effective method for the autonomous navigation in indoor environments.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.364-373
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• 2022

Image morphing methods make seamless transition changes in the image and mask the meaningful information attached to it. This can be detected by traditional machine learning algorithms and new emerging deep learning algorithms. In this research work, scope of different Hybrid learning approaches having combination of Deep learning and Machine learning are being analyzed with the public dataset CASIA V1.0, CASIA V2.0 and DVMM to find the most efficient algorithm. The simulated results with CNN (Convolution Neural Network), Hybrid approach of CNN along with SVM (Support Vector Machine) and Hybrid approach of CNN along with Random Forest algorithm produced 96.92 %, 95.98 and 99.18 % accuracy respectively with the CASIA V2.0 dataset having 9555 images. The accuracy pattern of applied algorithms changes with CASIA V1.0 data and DVMM data having 1721 and 1845 set of images presenting minimal accuracy with Hybrid approach of CNN and Random Forest algorithm. It is confirmed that the choice of best algorithm to find image forgery depends on input data type. This paper presents the combination of best suited algorithm to detect image morphing with different input datasets.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.999-1003
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• 1992

In the paper, a hybrid control approach for the swing-up control of a rotary type inverted pendulum is treated using one-chip microcomputer. The control approach is composed by a scheduling logic control for swing up control and the linear state feedback control to achieve the disired inverted-state of the pendulum. The experimental cystem has been implemented by a 16-bit one-chip microcomputer with 3096 opu as the digital controller incorporating the above mentioned control approach.

• Language and Information
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• v.14 no.2
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• pp.159-181
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• 2010

This paper presents a hybrid approach to the sentiment analysis of online texts. The sentiment of a text refers to the feelings that the author of a text has towards a certain topic. Many existing approaches employ either a pattern-based approach or a machine learning based approach. The former shows relatively high precision in classifying the sentiments, but suffers from the data sparseness problem, i.e. the lack of patterns. The latter approach shows relatively lower precision, but 100% recall. The approach presented in the current work adopts the merits of both approaches. It combines the pattern-based approach with the machine learning based approach, so that the relatively high precision and high recall can be maintained. Our experiment shows that the hybrid approach improves the F-measure score for more than 50% in comparison with the pattern-based approach and for around 1% comparing with the machine learning based approach. The numerical improvement from the machine learning based approach might not seem to be quite encouraging, but the fact that in the current approach not only the sentiment or the polarity information of sentences but also the additional information such as target of sentiments can be classified makes the current approach promising.

• Wind and Structures
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• v.8 no.6
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• pp.407-426
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• 2005

A hybrid RANS/LES approach, based on the Limited Numerical Scales concept, is applied to the numerical simulation of the flow around a square cylinder. The key feature of this approach is a blending between two eddy-viscosities, one given by the $k-{\varepsilon}$ RANS model and the other by the Smagorinsky LES closure. A mixed finite-element/finite-volume formulation is used for the numerical discretization on unstructured grids. The results obtained with the hybrid approach are compared with those given by RANS and LES simulations for three different grid resolutions; comparisons with experimental data and numerical results in the literature are also provided. It is shown that, if the grid resolution is adequate for LES, the hybrid model recovers the LES accuracy. For coarser grid resolutions, the blending criterion appears to be effective to improve the accuracy of the results with respect to both LES and RANS simulations.

• Journal of Aerospace System Engineering
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• v.1 no.2
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• pp.37-42
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• 2007

This paper proposes a numerical modeling approach to simulate the hybrid combustion phenomena. From the physical understandings of hybrid combustion, the computational domain was separated into three regions: the solid fuel, gas phase reactive flow, and the interface between solid and fluid. Moreover, for the accurate calculation, computational grids for these regions was generated at every time step considering the instantaneous moving interface which are governed by the balance equations using thermal pyrolysis. In the domain of reactive flow, by virtue of diffusion flame structure, turbulent combustion modeling was introduced using either mixture fraction approach or mean reaction rate approach.