• International Journal of Computer Science & Network Security
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• 제24권4호
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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.

• 인터넷정보학회논문지
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• 제23권5호
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• pp.79-85
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• 2022

이상 탐지는 일반적인 사용자들의 데이터 집합 속에서 비정상적인 데이터 흐름을 파악하여 미리 차단하는 방법이다. 기존에 알려진 방식은 이미 알려진 공격의 시그니처를 활용하여 시그니처 기반으로 공격을 탐지 및 방어하는 방식인데, 이는 오탐율이 낮다는 장점이 있지만 제로 데이 취약점 공격이나 변형된 공격에 대해서는 매우 취약하다는 점이 문제점이다. 하지만 이상 탐지의 경우엔 오탐율이 높다는 단점이 존재하지만 제로 데이 취약점 공격이나 변형된 공격에 대해서도 식별하여 탐지 및 차단할 수 있다는 장점이 있어 관련 연구들이 활발해지고 있는 중이다. 본 연구에서는 이 중 이상 탐지 메커니즘에 대해 다뤘다. 앞서 말한 단점인 높은 오탐율을 보완하며 그와 더불어 이상 탐지와 분류를 동시에 수행하는 새로운 메커니즘을 제안한다. 본 연구에서는 여러 알고리즘의 특성을 고려하여 5가지의 구성으로 실험을 진행하였다. 그 결과로 가장 우수한 정확도를 보이는 모델을 본 연구의 결과로 제안하였다. Extra Tree와 Three layer ANN을 동시에 적용하여 공격 여부를 탐지한 후 공격을 분류된 데이터에 대해서는 Extra Tree를 활용하여 공격 유형을 분류하게 된다. 본 연구에서는 NSL-KDD 데이터 세트에 대해서 검증을 진행하였으며, Accuracy는 Normal, Dos, Probe, U2R, R2L에 대하여 각각 99.8%, 99.1%, 98.9%, 98.7%, 97.9%의 결과를 보였다. 본 구성은 다른 모델에 비해 우수한 성능을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권2호
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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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• 제11권10호
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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.

• 대한기계학회논문집A
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• 제29권7호
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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.

• 대한지역사회영양학회지
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• 제16권6호
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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.

• 한국진공학회:학술대회논문집
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• 한국진공학회 2013년도 제45회 하계 정기학술대회 초록집
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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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 추계학술대회 논문집
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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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• 제16권12호
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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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• 제23권7호
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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.