Relevant bibliographies by topics / NSL-KDD

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'NSL-KDD'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "NSL-KDD"

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Bala, Ritu. "A REVIEW ON KDD CUP99 AND NSL-KDD DATASET." International Journal of Advanced Research in Computer Science 10, no. 2 (2019): 64–67. http://dx.doi.org/10.26483/ijarcs.v10i2.6395.

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Protić, Danijela. "Review of KDD Cup '99, NSL-KDD and Kyoto 2006+ datasets." Vojnotehnicki glasnik 66, no. 3 (2018): 580–96. http://dx.doi.org/10.5937/vojtehg66-16670.

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Sahli, Yassine. "comparison of the NSL-KDD dataset and its predecessor the KDD Cup ’99 dataset." International Journal of Scientific Research and Management 10, no. 04 (2022): 832–39. http://dx.doi.org/10.18535/ijsrm/v10i4.ec05.

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This study examines three datasets, notably the KDD Cup '99 and the NSL-KDD datasets, which are commonly used in intrusion detection research in computer networks. The KDD Cup '99 dataset contains five million records, each with 41 attributes that may be used to categorize malicious assaults into four categories: Probe, DoS, U2R, and R2L. Because it was developed by simulation over a virtual computer network, the KDD Cup '99 dataset cannot reflect real traffic statistics. Duplicate and redundant records from the KDD Cup '99 dataset are eliminated from the training and test sets, respectively,
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NIKITENKO, Andrii, and Yevhen SOBOL. "AN EFFECTIVE MODEL FOR DETECTING NETWORK INTRUSIONS USING MACHINE LEARNING METHODS." cientific papers of Donetsk National Technical University. Series: Informatics, Cybernetics and Computer Science 2, no. 39 (2024): 57–65. https://doi.org/10.31474/1996-1588-2024-2-39-57-65.

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The article is devoted to the study of machine learning algorithms used to create network intrusion detection systems. As part of the study, we reviewed the most common machine learning algorithms and tested their effectiveness on two datasets: NSL-KDD and CSE-CIC-IDS2018. When working with the NSL-KDD dataset, the test dataset was used to evaluate the accuracy, while for the CSE-CIC-IDS2018 dataset, the data was divided into training (80%) and test (20%). Studies have shown that data preprocessing significantly affects the final accuracy of models and can improve the performance of even tradi
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Sonawane, Sandip. "Rule Based Learning Intrusion Detection System Using KDD and NSL KDD Dataset." Prestige International Journal of Management & IT - Sanchayan 04, no. 02 (2015): 135–45. http://dx.doi.org/10.37922/pijmit.2015.v04i02.009.

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Prasetyo, Arief, Luqman Affandi, and Dedi Arpandi. "IMPLEMENTASI METODE NAIVE BAYES UNTUK INTRUSION DETECTION SYSTEM (IDS)." Jurnal Informatika Polinema 4, no. 4 (2018): 280. http://dx.doi.org/10.33795/jip.v4i4.220.

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IDS berfungsi untuk mengidentifikasi traffic atau lalu-lintas data pada sebuah jaringan komputer dimana IDS dapat menentukan apakah traffic aman, mencurigakan atau bahkan terindikasi merupakan serangan. Permasalahan muncul ketika ada aktifitas-aktifitas yang mencurigakan atau bahkan aktifitas tersebut merupakan serangan namun tidak terdaftar pada rule atau aturan yang diinputkan sehingga hal itu sangat membahayakan sebuah jaringan komputer. Tujuan dari penellitian ini adalah membangun sistem deteksi pola serangan baru menggunakan metode naive bayes untuk mengatasi serangan-serangan baru yang m
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Jang, Jinhyeok, Yoonsoo An, Dowan Kim, and Daeseon Choi. "Feature Importance-Based Backdoor Attack in NSL-KDD." Electronics 12, no. 24 (2023): 4953. http://dx.doi.org/10.3390/electronics12244953.

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In this study, we explore the implications of advancing AI technology on the safety of machine learning models, specifically in decision-making across diverse applications. Our research delves into the domain of network intrusion detection, covering rule-based and anomaly-based detection methods. There is a growing interest in anomaly detection within network intrusion detection systems, accompanied by an increase in adversarial attacks using maliciously crafted examples. However, the vulnerability of intrusion detection systems to backdoor attacks, a form of adversarial attack, is frequently
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Abedzadeh, Najmeh, and Matthew Jacobs. "A Reinforcement Learning Framework with Oversampling and Undersampling Algorithms for Intrusion Detection System." Applied Sciences 13, no. 20 (2023): 11275. http://dx.doi.org/10.3390/app132011275.

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Intrusion detection systems (IDSs) play a pivotal role in safeguarding networks and systems against malicious activities. However, the challenge of imbalanced datasets significantly impacts IDS research, skewing learning models towards the majority class and diminishing accuracy for the minority class. This study introduces the Reinforcement Learning (RL) Framework with Oversampling and Undersampling Algorithm (RLFOUA) to address imbalanced datasets. RLFOUA combines RL with diverse resampling algorithms, creating an adaptive learning environment. It integrates the novel True False Rate Synthet
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Mahmood, Hafza A. "Network Intrusion Detection System (NIDS) in Cloud Environment based on Hidden Naïve Bayes Multiclass Classifier." Al-Mustansiriyah Journal of Science 28, no. 2 (2018): 134. http://dx.doi.org/10.23851/mjs.v28i2.508.

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Cloud Environment is next generation internet based computing system that supplies customiza-ble services to the end user to work or access to the various cloud applications. In order to provide security and decrease the damage of information system, network and computer system it is im-portant to provide intrusion detection system (IDS. Now Cloud environment are under threads from network intrusions, as one of most prevalent and offensive means Denial of Service (DoS) attacks that cause dangerous impact on cloud computing systems. This paper propose Hidden naïve Bayes (HNB) Classifier to hand
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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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Dissertations / Theses on the topic "NSL-KDD"

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Petersen, Rebecca. "Data Mining for Network Intrusion Detection : A comparison of data mining algorithms and an analysis of relevant features for detecting cyber-attacks." Thesis, Mittuniversitetet, Avdelningen för informations- och kommunikationssystem, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-28002.

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Data mining can be defined as the extraction of implicit, previously un-known, and potentially useful information from data. Numerous re-searchers have been developing security technology and exploring new methods to detect cyber-attacks with the DARPA 1998 dataset for Intrusion Detection and the modified versions of this dataset KDDCup99 and NSL-KDD, but until now no one have examined the performance of the Top 10 data mining algorithms selected by experts in data mining. The compared classification learning algorithms in this thesis are: C4.5, CART, k-NN and Naïve Bayes. The performance of t
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Book chapters on the topic "NSL-KDD"

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Ji, Hyunjung, Donghwa Kim, Dongkyoo Shin, and Dongil Shin. "A Study on Comparison of KDD CUP 99 and NSL-KDD Using Artificial Neural Network." In Advances in Computer Science and Ubiquitous Computing. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7605-3_74.

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Wutyi, Khaing Shwe, and Mie Mie Su Thwin. "Heuristic Rules for Attack Detection Charged by NSL KDD Dataset." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23204-1_15.

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Ingre, Bhupendra, Anamika Yadav, and Atul Kumar Soni. "Decision Tree Based Intrusion Detection System for NSL-KDD Dataset." In Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 2. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63645-0_23.

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Negandhi, Prashil, Yash Trivedi, and Ramchandra Mangrulkar. "Intrusion Detection System Using Random Forest on the NSL-KDD Dataset." In Emerging Research in Computing, Information, Communication and Applications. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6001-5_43.

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Ever, Yoney Kirsal, Boran Sekeroglu, and Kamil Dimililer. "Classification Analysis of Intrusion Detection on NSL-KDD Using Machine Learning Algorithms." In Mobile Web and Intelligent Information Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27192-3_9.

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Singh, Kuldeep, Lakhwinder Kaur, and Raman Maini. "Comparison of Principle Component Analysis and Stacked Autoencoder on NSL-KDD Dataset." In Computational Methods and Data Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6876-3_17.

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Philo Prasanna, I., and M. Suguna. "Detection of Distributed Denial of Service Attack Using NSL-KDD Dataset - A Survey." In Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2019). Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43192-1_94.

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Chakrawarti, Ankit, and Shiv Shakti Shrivastava. "Intrusion Classification and Detection System Using Machine Learning Models on NSL-KDD Dataset." In Proceedings of Fifth International Conference on Computer and Communication Technologies. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9707-7_8.

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Sharna, Nadia Ahmed, and Emamul Islam. "Comparative Analysis of CatBoost Against Machine Learning Algorithms for Classification of Altered NSL-KDD." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1923-5_24.

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Cholakoska, Ana, Martina Shushlevska, Zdravko Todorov, and Danijela Efnusheva. "Analysis of Machine Learning Classification Techniques for Anomaly Detection with NSL-KDD Data Set." In Lecture Notes in Networks and Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90321-3_21.

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Conference papers on the topic "NSL-KDD"

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Barach, Jay. "Enhancing Intrusion Detection with CNN Attention Using NSL-KDD Dataset." In 2024 Artificial Intelligence for Business (AIxB). IEEE, 2024. https://doi.org/10.1109/aixb62249.2024.00009.

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E, Elakkiya, Bhavana Chukka, Krishna Sai Teja Kadiyam, Poojitha Pulagam, and S. Antony Raj. "Hybrid Models for Ehanced Intrusion Detection on NSL KDD and KDD CUP 99 Data Set." In 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0. IEEE, 2025. https://doi.org/10.1109/otcon65728.2025.11070981.

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Hegde, Ramakrishna, S. Likitha, and M. Natesh. "Intrusion Detection System Using Machine Learning Based on NSL KDD Dataset." In 2024 International Conference on Recent Advances in Science and Engineering Technology (ICRASET). IEEE, 2024. https://doi.org/10.1109/icraset63057.2024.10895295.

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Koranga, Ravindra Singh, Arun Kumar Rai, Shobhit Kumar, and Hradesh Kumar. "Comparative Analysis of ML Algorithms for Detecting Intrusion Using the NSL-KDD Dataset." In 2025 3rd International Conference on Disruptive Technologies (ICDT). IEEE, 2025. https://doi.org/10.1109/icdt63985.2025.10986545.

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Bindra, Shivam, Aruna Malik, and Samayveer Singh. "Improving Intrusion Detection for IoT Networks Using SMOTE and PCA on NSL-KDD Dataset." In 2025 3rd International Conference on Disruptive Technologies (ICDT). IEEE, 2025. https://doi.org/10.1109/icdt63985.2025.10986567.

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G. Pardeshi, Nilesh, and Dipak V. Patil. "Binary and Multiclass Classification Intrusion Detection System using Benchmark NSL-KDD and Machine Learning Models." In 2024 International Conference on Data Science and Network Security (ICDSNS). IEEE, 2024. http://dx.doi.org/10.1109/icdsns62112.2024.10691256.

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Saranya, R., and S. Silvia Priscila. "Enhancing Network Security: An Intrusion Detection Approach Using Artificial Neural Networks and NSL-KDD Dataset." In 2024 International Conference on Data Science and Network Security (ICDSNS). IEEE, 2024. http://dx.doi.org/10.1109/icdsns62112.2024.10691303.

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Mishra, Nilamadhab, and Sarojananda Mishra. "NSL-KDD Dataset Analysis: A Machine Learning Implementation to Detect Intrusions in the Computer Network." In 2024 2nd International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES). IEEE, 2024. https://doi.org/10.1109/scopes64467.2024.10990794.

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Kumar, M. Nirmal, T. Vijayan, and B. Karthik. "Enhancing Intrusion Detection with CNN-RF Hybrid Model: A High-Performance Approach Using NSL-KDD Dataset." In 2025 3rd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA). IEEE, 2025. https://doi.org/10.1109/icaeca63854.2025.11012645.

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Mehrotra, Parth, and Uttkarsh Dwivedi. "An Efficient Deep Learning Framwork for Classification and Detection of Anomaly Based Network Intrusion using NSL-KDD Dataset." In 2025 3rd International Conference on Disruptive Technologies (ICDT). IEEE, 2025. https://doi.org/10.1109/icdt63985.2025.10986548.

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