Relevant bibliographies by topics / KDD Cup 1999

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'KDD Cup 1999'

Author: Grafiati
Published: 30 May 2022
Last updated: 31 July 2025

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Journal articles on the topic "KDD Cup 1999"

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Lee, Sukjoon, and Donghee Shim. "Analysis for the KDD Cup 1999 Data Using the Convolutional Neural Network." Journal of Next-generation Convergence Information Services Technology 10, no. 2 (2021): 123–32. http://dx.doi.org/10.29056/jncist.2021.04.02.

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Eniodunmo, Oluwapelumi, and Raid Al-Aqtash. "A Predictive Model to Predict a Cyberattack Using Self Normalizing Neural Networks." International Journal of Statistics and Probability 12, no. 6 (2023): 60. http://dx.doi.org/10.5539/ijsp.v12n6p60.

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A cyberattack is an unauthorized access and a threat to information systems. Intelligent intrusion systems rely on advancements in technology to detect cyberattacks. In this article, the KDD CUP 99 dataset, from the Third International Knowledge Discovery and Data mining Tools Competition that was held in 1999, is considered, and a class of neural networks, known as Self-Normalizing Neural Networks, is utilized to build a predictive model to detect cyberattacks in the KDD CUP 99 dataset. The accuracy and the precision of the self-normalizing neural network is compared with that of the k-neares
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Xia, Yong Xiang, Zhi Cai Shi, Yu Zhang, and Jian Dai. "A SVM Intrusion Detection Method Based on GPU." Applied Mechanics and Materials 610 (August 2014): 606–10. http://dx.doi.org/10.4028/www.scientific.net/amm.610.606.

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To optimize training procedure of IDS based on SVM and reduce time consumption, a SVM intrusion detection method based on GPU is proposed in the study. During the simulation experiments with KDD Cup 1999 data, GPU-based parallel computing model is adopted. Results of the simulation experiments demonstrate that time consumption in the training procedure of IDS is reduced, and performance of IDS is kept as usual.
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Seo, Jae-Hyun, and Yong-Hyuk Kim. "Machine-Learning Approach to Optimize SMOTE Ratio in Class Imbalance Dataset for Intrusion Detection." Computational Intelligence and Neuroscience 2018 (November 1, 2018): 1–11. http://dx.doi.org/10.1155/2018/9704672.

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The KDD CUP 1999 intrusion detection dataset was introduced at the third international knowledge discovery and data mining tools competition, and it has been widely used for many studies. The attack types of KDD CUP 1999 dataset are divided into four categories: user to root (U2R), remote to local (R2L), denial of service (DoS), and Probe. We use five classes by adding the normal class. We define the U2R, R2L, and Probe classes, which are each less than 1% of the total dataset, as rare classes. In this study, we attempt to mitigate the class imbalance of the dataset. Using the synthetic minori
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Mathan, Pinaki Shashishekhar. "Intrusion Detection Using Machine Learning Classification and Regression." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42130.

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An Intrusion Detection System (IDS) is a crucial security mechanism designed to protect computer networks from unauthorized access and cyber threats. With the rapid expansion of Internet-based data transmission, ensuring network security has become increasingly challenging. IDS continuously monitors and analyzes network traffic to detect malicious activities, relying on datasets like KDD Cup 1999 for training and evaluation. Effective IDS development involves preprocessing steps such as feature selection, normalization, and addressing data imbalance to enhance detection accuracy. Various machi
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Serinelli, Benedetto Marco, Anastasija Collen, and Niels Alexander Nijdam. "Training Guidance with KDD Cup 1999 and NSL-KDD Data Sets of ANIDINR: Anomaly-Based Network Intrusion Detection System." Procedia Computer Science 175 (2020): 560–65. http://dx.doi.org/10.1016/j.procs.2020.07.080.

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Sharma, Srishti, Yogita Gigras, Rita Chhikara, and Anuradha Dhull. "Analysis of NSL KDD Dataset Using Classification Algorithms for Intrusion Detection System." Recent Patents on Engineering 13, no. 2 (2019): 142–47. http://dx.doi.org/10.2174/1872212112666180402122150.

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Background: Intrusion detection systems are responsible for detecting anomalies and network attacks. Building of an effective IDS depends upon the readily available dataset. This dataset is used to train and test intelligent IDS. In this research, NSL KDD dataset (an improvement over original KDD Cup 1999 dataset) is used as KDD’99 contains huge amount of redundant records, which makes it difficult to process the data accurately. Methods: The classification techniques applied on this dataset to analyze the data are decision trees like J48, Random Forest and Random Trees. Results: On comparison
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Cheng, Guo Zhen, Dong Nian Cheng, and He Lei. "A Novel Network Traffic Anomaly Detection Based on Multi-Scale Fusion." Applied Mechanics and Materials 48-49 (February 2011): 102–5. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.102.

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Detecting network traffic anomaly is very important for network security. But it has high false alarm rate, low detect rate and that can’t perform real-time detection in the backbone very well due to its nonlinearity, nonstationarity and self-similarity. Therefore we propose a novel detection method—EMD-DS, and prove that it can reduce mean error rate of anomaly detection efficiently after EMD. On the KDD CUP 1999 intrusion detection evaluation data set, this detector detects 85.1% attacks at low false alarm rate which is better than some other systems.
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Mohammed, Mohammed, and Khattab M. Ali Alheeti. "AI-Driven Features for Intrusion Detection and Prevention Using Random Forest." Journal of Cybersecurity and Information Management 16, no. 1 (2025): 01–14. https://doi.org/10.54216/jcim.160101.

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In this research, we investigate sophisticated methods for Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS), leveraging AI-based feature optimization and diverse machine learning strategies to bolster network intrusion detection and prevention. The study primarily utilizes the NSL-KDD dataset, an enhanced version of the KDD Cup 1999 dataset, chosen for its realistic portrayal of various attack types and for addressing the shortcomings of the original dataset. The methodology includes AI-based feature optimization using Particle Swarm Optimization and Genetic Algorithm,
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Kim, Jiyeon, Jiwon Kim, Hyunjung Kim, Minsun Shim, and Eunjung Choi. "CNN-Based Network Intrusion Detection against Denial-of-Service Attacks." Electronics 9, no. 6 (2020): 916. http://dx.doi.org/10.3390/electronics9060916.

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As cyberattacks become more intelligent, it is challenging to detect advanced attacks in a variety of fields including industry, national defense, and healthcare. Traditional intrusion detection systems are no longer enough to detect these advanced attacks with unexpected patterns. Attackers bypass known signatures and pretend to be normal users. Deep learning is an alternative to solving these issues. Deep Learning (DL)-based intrusion detection does not require a lot of attack signatures or the list of normal behaviors to generate detection rules. DL defines intrusion features by itself thro
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Dissertations / Theses on the topic "KDD Cup 1999"

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Вашеняк, Артем Миколайович. "Методи виявлення прихованих атак на інформаційні системи із застосуванням нечіткої логіки". Магістерська робота, Хмельницький національний університет, 2021. http://elar.khnu.km.ua/jspui/handle/123456789/11104.

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Метою кваліфікаційної роботи є розробка комп’ютерної системи на основі нечіткої логіки для виявлення прихованих атак. Дана кваліфікаційна робота присвячена удосконаленню методу реалізації систем ідентифікації вторгнень на основі нечіткої логіки.
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Books on the topic "KDD Cup 1999"

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Červinka, Stanislav. Kdo zabil Arconu? 2nd ed. Tempo, 2010.

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Červinka, Stanislav. Kdo zabil Arconu? Tempo, 1991.

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Find and Fit Baby Animals: (Quarto 1999). Ben's Books, 1999.

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Book chapters on the topic "KDD Cup 1999"

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Wang, Yun, and Lee Seidman. "Risk Factors to Retrieve Anomaly Intrusion Information and Profile User Behavior." In Information Security and Ethics. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-937-3.ch159.

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The use of network traffic audit data for retrieving anomaly intrusion information and profiling user behavior has been studied previously, but the risk factors associated with attacks remain unclear. This study aimed to identify a set of robust risk factors via the bootstrap resampling and logistic regression modeling methods based on the KDD-cup 1999 data. Of the 46 examined variables, 16 were identified as robust risk factors, and the classification showed similar performances in sensitivity, specificity, and correctly classified rate in comparison with the KDD-cup 1999 winning results that
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Zhang, Ji. "A Subspace-Based Analysis Method for Anomaly Detection in Large and High-Dimensional Network Connection Data Streams." In Data Mining. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2455-9.ch026.

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A great deal of research attention has been paid to data mining on data streams in recent years. In this chapter, the authors carry out a case study of anomaly detection in large and high-dimensional network connection data streams using Stream Projected Outlier deTector (SPOT) that is proposed in (Zhang et al. 2009) to detect anomalies from data streams using subspace analysis. SPOT is deployed on the 1999 KDD CUP anomaly detection application. Innovative approaches for training data generation, anomaly classification, and false positive reduction are proposed in this chapter as well. Experim
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Zhang, Ji. "A Dynamic Subspace Anomaly Detection Method Using Generic Algorithm for Streaming Network Data." In Handbook of Research on Emerging Developments in Data Privacy. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-7381-6.ch018.

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A great deal of research attention has been paid to data mining on data streams in recent years. In this chapter, the authors carry out a case study of anomaly detection in large and high-dimensional network connection data streams using Stream Projected Outlier deTector (SPOT) that is proposed in Zhang et al. (2009) to detect anomalies from data streams using subspace analysis. SPOT is deployed on 1999 KDD CUP anomaly detection application. Innovative approaches for training data generation, anomaly classification, false positive reduction, and adoptive detection subspace generation are propo
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Clarke, A. K. "Synechococcus sp. PCC 7942 CIpC." In Guidebook to Molecular Chaperones and Protein-Folding Catalysts. Oxford University PressOxford, 1997. http://dx.doi.org/10.1093/oso/9780198599494.003.0095.

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Abstract The Synechococcus sp. PCC 7942 CIpC (Clarke, Eriksson, 1996) is an 87 kDa polypeptide (840 amino acids) containing the two conserved ATP-binding domains characteristic of all larger Clp proteins (i.e. CIpA, B, C; see overview of HSP100/Clp proteins, p. 231). The length of the intervening region between the ATP-binding domains (i.e. 100-110 amino acids) is also representative of proteins within the CIpC subfamily, as are the two highly conserved repeat motifs located within the N-terminal region (Squires, Squires, 1992).
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Conference papers on the topic "KDD Cup 1999"

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Zeng, Xiulian. "Unmasking Intruders: An In-Depth Analysis of Anomaly Detection Using the KDD Cup 1999 Dataset." In 2024 3rd International Conference on Artificial Intelligence and Computer Information Technology (AICIT). IEEE, 2024. http://dx.doi.org/10.1109/aicit62434.2024.10729979.

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Shah, Bhavin, and Bhushan H. Trivedi. "Reducing Features of KDD CUP 1999 Dataset for Anomaly Detection Using Back Propagation Neural Network." In 2015 Fifth International Conference on Advanced Computing & Communication Technologies (ACCT). IEEE, 2015. http://dx.doi.org/10.1109/acct.2015.131.

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Wilson, Ryan, and Charlie Obimbo. "Self-organizing feature maps for User-to-Root and Remote-to-Local network intrusion detection on the KDD Cup 1999 dataset." In 2011 World Congress on Internet Security (WorldCIS-2011). IEEE, 2011. http://dx.doi.org/10.1109/worldcis17046.2011.5749879.

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Fank, Elias Augusto, Geomar A. Schreiner, and Denio Duarte. "Estudo comparativo de plataformas de Deep Learning: Apache Singa, Graphlab e H2O." In Escola Regional de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/erbd.2021.17234.

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Técnicas de Deep learning vêm mostrando avanços em várias tarefas de aprendizado de máquina. Porém a implementação dessas técnicas é muito complexa. Assim, para ajudar na implementação de projetos de Deep Learning, plataformas estão sendo criados. Já existe uma quantidade considerável destas plataformas disponível. Isso acaba trazendo uma dificuldade na escolha de quem procura começar um projeto. Com o objetivo de auxiliar nesta escolha, este trabalho faz um estudo comparativo entre algumas plataformas: Apache Singa, Graphlab e H2O. Experimentos são conduzidos utilizando os conjunto de dados M
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Reports on the topic "KDD Cup 1999"

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Epel, Bernard L., Roger N. Beachy, A. Katz, et al. Isolation and Characterization of Plasmodesmata Components by Association with Tobacco Mosaic Virus Movement Proteins Fused with the Green Fluorescent Protein from Aequorea victoria. United States Department of Agriculture, 1999. http://dx.doi.org/10.32747/1999.7573996.bard.

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The coordination and regulation of growth and development in multicellular organisms is dependent, in part, on the controlled short and long-distance transport of signaling molecule: In plants, symplastic communication is provided by trans-wall co-axial membranous tunnels termed plasmodesmata (Pd). Plant viruses spread cell-to-cell by altering Pd. This movement scenario necessitates a targeting mechanism that delivers the virus to a Pd and a transport mechanism to move the virion or viral nucleic acid through the Pd channel. The identity of host proteins with which MP interacts, the mechanism
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