• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.170-178
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• 2024

Through the growth of the fifth-generation networks and artificial intelligence technologies, new threats and challenges have appeared to wireless communication system, especially in cybersecurity. And IoT networks are gradually attractive stages for introduction of DDoS attacks due to integral frailer security and resource-constrained nature of IoT devices. This paper emphases on detecting DDoS attack in wireless networks by categorizing inward network packets on the transport layer as either "abnormal" or "normal" using the integration of machine learning algorithms knowledge-based system. In this paper, deep learning algorithms and CNN were autonomously trained for mitigating DDoS attacks. This paper lays importance on misuse based DDOS attacks which comprise TCP SYN-Flood and ICMP flood. The researcher uses CICIDS2017 and NSL-KDD dataset in training and testing the algorithms (model) while the experimentation phase. accuracy score is used to measure the classification performance of the four algorithms. the results display that the 99.93 performance is recorded.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.79-85
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• 2022

Anomaly detection is a method to detect and block abnormal data flows in general users' data sets. The previously known method is a method of detecting and defending an attack based on a signature using the signature of an already known attack. This has the advantage of a low false positive rate, but the problem is that it is very vulnerable to a zero-day vulnerability attack or a modified attack. However, in the case of anomaly detection, there is a disadvantage that the false positive rate is high, but it has the advantage of being able to identify, detect, and block zero-day vulnerability attacks or modified attacks, so related studies are being actively conducted. In this study, we want to deal with these anomaly detection mechanisms, and we propose a new mechanism that performs both anomaly detection and classification while supplementing the high false positive rate mentioned above. In this study, the experiment was conducted with five configurations considering the characteristics of various algorithms. As a result, the model showing the best accuracy was proposed as the result of this study. After detecting an attack by applying the Extra Tree and Three-layer ANN at the same time, the attack type is classified using the Extra Tree for the classified attack data. In this study, verification was performed on the NSL-KDD data set, and the accuracy was 99.8%, 99.1%, 98.9%, 98.7%, and 97.9% for Normal, Dos, Probe, U2R, and R2L, respectively. This configuration showed superior performance compared to other models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.702-723
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• 2020

The application of machine learning (ML) in intrusion detection has attracted much attention with the rapid growth of information security threat. As an efficient multi-label classifier, kernel extreme learning machine (KELM) has been gradually used in intrusion detection system. However, the performance of KELM heavily relies on the kernel selection. In this paper, a novel multiple kernel extreme learning machine (MKELM) model combining the ReliefF with nature-inspired methods is proposed for intrusion detection. The MKELM is designed to estimate whether the attack is carried out and the ReliefF is used as a preprocessor of MKELM to select appropriate features. In addition, the nature-inspired methods whose fitness functions are defined based on the kernel alignment are employed to build the optimal composite kernel in the MKELM. The KDD99, NSL and Kyoto datasets are used to evaluate the performance of the model. The experimental results indicate that the optimal composite kernel function can be determined by using any heuristic optimization method, including PSO, GA, GWO, BA and DE. Since the filter-based feature selection method is combined with the multiple kernel learning approach independent of the classifier, the proposed model can have a good performance while saving a lot of training time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.5132-5148
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• 2017

Cyber attacks are evolving commensurate with recent developments in information security technology. Intrusion detection systems collect various types of data from computers and networks to detect security threats and analyze the attack information. The large amount of data examined make the large number of computations and low detection rates problematic. Feature selection is expected to improve the classification performance and provide faster and more cost-effective results. Despite the various feature selection studies conducted for intrusion detection systems, it is difficult to automate feature selection because it is based on the knowledge of security experts. This paper proposes a feature selection technique to overcome the performance problems of intrusion detection systems. Focusing on feature selection, the first phase of the proposed system aims at constructing a feature subset using a sequential forward floating search (SFFS) to downsize the dimension of the variables. The second phase constructs a classification model with the selected feature subset using a random forest classifier (RFC) and evaluates the classification accuracy. Experiments were conducted with the NSL-KDD dataset using SFFS-RF, and the results indicated that feature selection techniques are a necessary preprocessing step to improve the overall system performance in systems that handle large datasets. They also verified that SFFS-RF could be used for data classification. In conclusion, SFFS-RF could be the key to improving the classification model performance in machine learning.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.7 s.238
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• pp.998-1004
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• 2005

A nano-stereolithography (NSL) process has been developed for fabrication of 3D shell structures which can be applied to various nano/micro-fluidic devices. By the process, a complicated 3D shell structure on a scale of several microns can be fabricated using lamination of layers with a resolution of 150 nm in size, so it does not require the use of my sacrificial layer or any supporting structure. A layer was fabricated by means of solidifying liquid-state monomers using two-photon absorption (TPA) induced using a femtosecond laser processing. When the polymerization process is finished, unsolidified liquid state resins can be removed easily by dropping several droplets of ethanol fur developing the fabricated structure. Through this work, some 3D shell structures, which can be applied to various applications such as nano/micro-fluidic devices and MEMS system, were fabricated using the developed process.

• Korean Journal of Community Nutrition
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• v.16 no.6
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• pp.716-729
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• 2011

This study aimed to plan nutrition support programs for the elderly living alone whose nutrition status were seriously concerned, conducted seven stages nutrition intervention program on a trial basis, and evaluated the effectiveness of the program of the Elderly Nutrition Support Project. Subjects were selected for personalized nutrition management based on nutritional risk score and nutrition intervention were tailored to the problems occurred. The elderly nutrition support program targets were 44 senior citizens who lived alone with low income. The 33 (as Type 1) of the subjects with whom milk, tofu, seaweed, eggs, black beans have been supported, and also provide nutrition education, and the rest 11 persons (as Type 2) to whom food was not supported but provide nutrition education programs. As a result, all subjects showed that compared with pre and post program implementation, their daily exercise time and milk and protein consumption level were increased and some improvement was observed regular meals consumption and low-salt diets. Their nutrient intake level such as calories, protein, calcium, iron improved after implementation. In addition, NSL DETERMINE scores significantly improved from 13.21 to 7.24 in Type 1 and 11.27 to 9.91 in Type 2. As positive dietary behavioral changes were observed as in that they purchased more protein and calcium rich foods.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.08a
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• pp.127.2-127.2
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• 2013

오늘날 유기고분자기반 태양전지는 다른 태양전지와 비교될 정도로 낮은 광변환효율로 인해 효율향 상을 위한 많은 연구들이 진행되어 왔다. 그중 패터닝을 통한 광포집률과 charge carrier 수집효율이 증가되었다는 많은 보고들이 있었다. 따라서 우리는 200~1,400 nm polystyrene bead를 합성하여 air-liquid interfacial 방법을 이용해 2차원 육방조밀구조를 갖는 template를 형성하고 Nanosphere lithography (NSL)를 이용하여 대면적으로 균일한 poly(3,4-ethylenedioxythiophene) poly(styrenesulfonate) (PEDOT:PSS)를 패턴화하였다. 균일한 패턴형성을 측정하기위해 Field Emission Scanning Electron Microscopy (FE-SEM), image를 얻었으며, Atomic Force Microscopy (AFM)를 통해 형성된 패턴의 낙차 높이를 얻었고, Near IR-UV-Vis을 통해 bead size 변화에따라 얻어진 PEDOT:PSS 패턴의 반사율을 측 정하였다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.492-497
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• 2005

In this study, we propose an advanced nanoindentaion test, Nano Pillar Compression Test (NPCT) to measure a stress-strain relation for micro scale polymer structures. Firstly, FEM analysis is performed to research behavior of micro polymer pillars in several specimen aspect ratios and different friction conditions between specimen and tip. Based on the FEM results, micro scale UV-curable polymer pillars are fabricated on a substrate by Nano Stereo Lithography (NSL). To measure their mechanical properties, uniaxial compression test is performed using nanoindentation apparatus with flat-ended diamond tip. In addition, the dependency of compression properties on loading condition and specimen size are discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3889-3903
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• 2022

To address the problem of low detection accuracy due to training noise caused by mislabeling when Tri-training for network intrusion detection (NID), we propose a Tri-training algorithm based on cross entropy and K-nearest neighbors (TCK) for network intrusion detection. The proposed algorithm uses cross-entropy to replace the classification error rate to better identify the difference between the practical and predicted distributions of the model and reduce the prediction bias of mislabeled data to unlabeled data; K-nearest neighbors are used to remove the mislabeled data and reduce the number of mislabeled data. In order to verify the effectiveness of the algorithm proposed in this paper, experiments were conducted on 12 UCI datasets and NSL-KDD network intrusion datasets, and four indexes including accuracy, recall, F-measure and precision were used for comparison. The experimental results revealed that the TCK has superior performance than the conventional Tri-training algorithms and the Tri-training algorithms using only cross-entropy or K-nearest neighbor strategy.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.131-140
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• 2023

Network security situational awareness systems helps in better managing the security concerns of a network, by monitoring for any anomalies in the network connections and recommending remedial actions upon detecting an attack. An Intrusion Detection System helps in identifying the security concerns of a network, by monitoring for any anomalies in the network connections. We have proposed a CRF based IDS system using genetic search feature selection algorithm for network security situational awareness to detect any anomalies in the network. The conditional random fields being discriminative models are capable of directly modeling the conditional probabilities rather than joint probabilities there by achieving better classification accuracy. The genetic search feature selection algorithm is capable of identifying the optimal subset among the features based on the best population of features associated with the target class. The proposed system, when trained and tested on the bench mark NSL-KDD dataset exhibited higher accuracy in identifying an attack and also classifying the attack category.