Relevant bibliographies by topics / NSL –KDD Data set

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

Academic literature on the topic 'NSL –KDD Data set'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "NSL –KDD Data set"

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Shukla, Alok Kumar, and Pradeep Singh. "Building an Effective Approach toward Intrusion Detection Using Ensemble Feature Selection." International Journal of Information Security and Privacy 13, no. 3 (2019): 31–47. http://dx.doi.org/10.4018/ijisp.201907010102.

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The duplicate and insignificant features present in the data set to cause a long-term problem in the classification of network or web traffic. The insignificant features not only decrease the classification performance but also prevent a classifier from making accurate decisions, exclusively when substantial volumes of data are managed. In this article, the author introduced an ensemble feature selection (EFS) technique, where multiple homogeneous feature selection (FS) methods are combined to choose the optimal subset of relevant and non-redundant features. An intrusion detection system, name
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Journal, Baghdad Science. "Developing an Immune Negative Selection Algorithm for Intrusion Detection in NSL-KDD data Set." Baghdad Science Journal 13, no. 2 (2016): 278–90. http://dx.doi.org/10.21123/bsj.13.2.278-290.

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With the development of communication technologies for mobile devices and electronic communications, and went to the world of e-government, e-commerce and e-banking. It became necessary to control these activities from exposure to intrusion or misuse and to provide protection to them, so it's important to design powerful and efficient systems-do-this-purpose. It this paper it has been used several varieties of algorithm selection passive immune algorithm selection passive with real values, algorithm selection with passive detectors with a radius fixed, algorithm selection with passive detector
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Ma, Manfu, Wei Deng, Hongtong Liu, and Xinmiao Yun. "An Intrusion Detection Model based on Hybrid Classification algorithm." MATEC Web of Conferences 246 (2018): 03027. http://dx.doi.org/10.1051/matecconf/201824603027.

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Due to using the single classification algorithm can not meet the performance requirements of intrusion detection, combined with the numerical value of KNN and the advantage of naive Bayes in the structure of data, an intrusion detection model KNN-NB based on KNN and Naive Bayes hybrid classification algorithm is proposed. The model first preprocesses the NSL-KDD intrusion detection data set. And then by exploiting the advantages of KNN algorithm in data values, the model calculates the distance between the samples according to the feature items and selects the K sample data with the smallest
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Solekha, Novia Amilatus. "Analysis of NSL-KDD Dataset for Classification of Attacks Based on Intrusion Detection System Using Binary Logistics and Multinomial Logistics." Seminar Nasional Official Statistics 2022, no. 1 (2022): 507–20. http://dx.doi.org/10.34123/semnasoffstat.v2022i1.1138.

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At present, the intrusion detection system is the most developed trend in society. The intrusion detection system acts as a defense tool to detect security attacks which has been increasing steadily in recent years. Therefore, global information security is a very serious problem. As the types of attacks that emerge are constantly changing, there is a need to develop adaptive and flexible security features. Selection feature is one of the focuses of research on data mining for datasets that have relatively many attributes. In this study, the author tries to analyze the NSL-KDD data set with th
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Mr., Shobhan Kumar* Mr. Naveen D.C. "IMPROVE THE ACCURACY OF CLASSIFIERS PERFORMANCE USING MACHINE LEARNING & DATA PREPROCESSED METHODS ON NSL-KDD DATA SETS." Global Journal of Engineering Science and Research Management 3, no. 5 (2016): 136–42. https://doi.org/10.5281/zenodo.53749.

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Classification is the method of discovering a set of models that describes data classes for the purpose of being able to utilize the model to forecast the class of objects whose class label is unknown. The derived model is based on the analysis of a set of training data set. Since the class label of each training sample provides this step is referred as supervised learning. The manuscript describes a system that uses Feature Selection [17, 18] as a data pre-processing activities. Feature selection may present us with the means to reduce the number of network parameters made while still maintai
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Hashem, Soukaena, and Hafsa Adil. "Denial of Service Intrusion Detection System (IDS) Based on Naïve Bayes Classifier using NSL KDD and KDD Cup 99 Datasets." Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ), no. 2 (October 9, 2021): 206–31. http://dx.doi.org/10.55562/jrucs.v40i2.200.

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Intrusion Detection Systems (IDS) become necessary to protect data from intruders and reduce the damage of the information system and networks especially in cloud environment which is next generation Internet based computing system that supplies customizable services to the end user to work or access to the various cloud applications. This paper concentrates the views to be noted that; the attacks in cloud environment have high rates of Denial of service (DoS) attacks compared with the usual network environment. This paper will introduce Naïve Bayes (NB) Classifier supported by discrete the co
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Tribak, Hind, Olga Valenzuela, Fernando Rojas, and Ignacio Rojas. "Statistical Analysis of Different Artificial Intelligent Techniques applied to Intrusion Detection System." International Journal of Systems Applications, Engineering & Development 16 (March 10, 2022): 48–55. http://dx.doi.org/10.46300/91015.2022.16.10.

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Intrusion detection is the act of detecting unwanted traffic on a network or a device. Several types of Intrusion Detection Systems (IDS) technologies exist due to the variance of network configurations. Each type has advantages and disadvantage in detection, configuration, and cost. In general, the traditional IDS relies on the extensive knowledge of security experts, in particular, on their familiarity with the computer system to be protected. To reduce this dependence, various datamining and machine learning techniques have been used in the literature. The experiments and evaluations of the
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Deepa, Hindoliya, and Avinash Sharma Prof. "Performance Evaluation of Intrusion Detection using Linear Regression with K Nearest Neighbor." International Journal of Trend in Scientific Research and Development 4, no. 1 (2019): 255–59. https://doi.org/10.5281/zenodo.3604828.

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Starting late, the colossal proportions of data and its unfaltering augmentation have changed the essentialness of information security and data examination systems for Big Data. Interference acknowledgment structure IDS is a system that screens and analyzes data to perceive any break in the structure or framework. High volume, arrangement and quick of data made in the framework have made the data examination strategy to perceive ambushes by ordinary strategies problematic. Gigantic Data frameworks are used in IDS to oversee Big Data for exact and profitable data examination process. This work
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Shaik, Aseena Babu, Rajeswara Reddy, Nagagopi Raju Vullam, Gondi Konda Reddy, and Subhani Shaik. "An Effective Method for Detecting Cyber Attacks on Computer Networks from the NSL-KDD Data Set." ITM Web of Conferences 74 (2025): 02001. https://doi.org/10.1051/itmconf/20257402001.

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Cybercrime is rapidly increasing and exploits various vulnerabilities in these computing environments. Ethical hackers pay more attention to determining vulnerabilities and recommending mitigation methods. Due to the effectiveness of machine learning in solving cybersecurity problems, machine learning is of great importance to cybersecurity. Machine learning models are used to advance the techniques to detect and solve cybersecurity problems. Machine learning methods help detect more cyber attacks more efficiently than other software-oriented techniques, reducing the burden on security analyst
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Kejia, Shen, Hamid Parvin, Sultan Noman Qasem, Bui Anh Tuan, and Kim-Hung Pho. "A classification model based on svm and fuzzy rough set for network intrusion detection." Journal of Intelligent & Fuzzy Systems 39, no. 5 (2020): 6801–17. http://dx.doi.org/10.3233/jifs-191621.

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Intrusion Detection Systems (IDS) are designed to provide security into computer networks. Different classification models such as Support Vector Machine (SVM) has been successfully applied on the network data. Meanwhile, the extension or improvement of the current models using prototype selection simultaneous with their training phase is crucial due to the serious inefficacies during training (i.e. learning overhead). This paper introduces an improved model for prototype selection. Applying proposed prototype selection along with SVM classification model increases attack discovery rate. In th
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Dissertations / Theses on the topic "NSL –KDD Data set"

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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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Engen, Vegard. "Machine learning for network based intrusion detection : an investigation into discrepancies in findings with the KDD cup '99 data set and multi-objective evolution of neural network classifier ensembles from imbalanced data." Thesis, Bournemouth University, 2010. http://eprints.bournemouth.ac.uk/15899/.

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For the last decade it has become commonplace to evaluate machine learning techniques for network based intrusion detection on the KDD Cup '99 data set. This data set has served well to demonstrate that machine learning can be useful in intrusion detection. However, it has undergone some criticism in the literature, and it is out of date. Therefore, some researchers question the validity of the findings reported based on this data set. Furthermore, as identified in this thesis, there are also discrepancies in the findings reported in the literature. In some cases the results are contradictory.
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Book chapters on the topic "NSL –KDD Data set"

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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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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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Hota, H. S., and Akhilesh Kumar Shrivas. "Decision Tree Techniques Applied on NSL-KDD Data and Its Comparison with Various Feature Selection Techniques." In Smart Innovation, Systems and Technologies. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07353-8_24.

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Mohanty, Subrat, Satendra Kumar, and Mayank Agarwal. "Enhancing Accuracy with Recursive Feature Selection Using Multiple Machine Learning and Deep Learning Techniques on NSL-KDD Dataset." In Advances in Data-Driven Computing and Intelligent Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9518-9_18.

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Gu, Jie. "An Effective Intrusion Detection Model Based on Pls-Logistic Regression with Feature Augmentation." In Communications in Computer and Information Science. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-33-4922-3_10.

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AbstractComputer network is playing a significantly important role in our society, including commerce, communication, consumption and entertainment. Therefore, network security has become increasingly important. Intrusion detection systems have received considerable attention, which not only can detect known attacks or intrusions, but also can detect unknown attacks. Among the various methods applied to intrusion detection, logistic regression is the most widely used, which can achieve good performances and have good interpretability at the same time. However, intrusion detection systems usual
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Krivchenkov, Aleksandr, Boriss Misnevs, and Alexander Grakovski. "Structural Analysis of the NSL-KDD Data Sets for Solving the Problem of Attacks Detection Using ML/DL Methods." In Lecture Notes in Networks and Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96196-1_1.

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Priyalakshmi, V., and R. Devi. "Analysis and Implementation of Normalisation Techniques on KDD’99 Data Set for IDS and IPS." In Proceedings of International Conference on Data Science and Applications. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6634-7_5.

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Sammy. F, Dr, Dr Meenakshi, Dr Umesh Kumar Singh, Dr Malik Jawarneh, and Dr Abhishek Raghuvanshi. "MACHINE LEARNING TECHNIQUES FOR DESIGN OF INTRUSION DETECTION SYSTEM FOR BIG DATA NETWORKS." In Futuristic Trends in Network & Communication Technologies Volume 3 Book 3. Iterative International Publishers, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/v3binc3p4ch1.

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As digital technology advances, gigabytes and terabytes of data are now generated every second. Businesses in a variety of industries are finding that using the internet to manage their resources and transactions is useful. Given the value of data and the need to safeguard its security and privacy, securing big data remains a major challenge for all solutions. Due to the exponential expansion of network data, intrusion detection is becoming increasingly important, and manual analysis would be either impossible or take the same amount of time as analysing it. As a result, there is an urgent nee
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Yu, Yan. "Application and Effectiveness Analysis of Deep Reinforcement Learning in Computer Network Traffic Management." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2025. https://doi.org/10.3233/faia250314.

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In order to solve the problem of network intrusion detection in the field of network security, the application and effect analysis of deep reinforcement learning in computer network traffic management were proposed. By constructing deep Q network (DQN) as the main model, it is applied to network traffic analysis and intrusion detection tasks. In the experiment, NSL-KDD data set was used to train and test the model. The experimental results show that DQN is significantly improved compared with DNN in terms of accuracy, reaching 0 94, DQN can more accurately predict network intrusion behavior an
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Conference papers on the topic "NSL –KDD Data set"

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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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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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Thomas, Rajesh, and Deepa Pavithran. "A Survey of Intrusion Detection Models based on NSL-KDD Data Set." In 2018 Fifth HCT Information Technology Trends (ITT). IEEE, 2018. http://dx.doi.org/10.1109/ctit.2018.8649498.

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Alvarez Almeida, Luis Alfredo, and Juan Carlos Martinez Santos. "Evaluating Features Selection on NSL-KDD Data-Set to Train a Support Vector Machine-Based Intrusion Detection System." In 2019 IEEE Colombian Conference on Applications in Computational Intelligence (ColCACI). IEEE, 2019. http://dx.doi.org/10.1109/colcaci.2019.8781803.

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Roy, Sandipan, Apurbo Mandal, and Debraj Dey. "Intelligent Intrusion Detection System using Supervised Learning." In Intelligent Computing and Technologies Conference. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.115.3.

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Going digital involves networking with so many connected devices, so network security becomes a critical task for everyone. But an intrusion detection system can help us to detect malicious activity in a system or network. But generally, intrusion detection systems (IDS) are not reliable and sustainable also they require more resources. In recent years so many machine learning methods are proposed to give higher accuracy with minimal false alerts. But analyzing those huge traffic data is still challenging. So, in this article, we proposed a technique using the Support Vector Machine & Naiv
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"Feature Selection using Attribute Ratio in NSL-KDD data." In International Conference Data Mining, Civil and Mechanical Engineering. International Institute of Engineers, 2014. http://dx.doi.org/10.15242/iie.e0214081.

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Paulauskas, Nerijus, and Juozas Auskalnis. "Analysis of data pre-processing influence on intrusion detection using NSL-KDD dataset." In 2017 Open Conference of Electrical, Electronic and Information Sciences (eStream). IEEE, 2017. http://dx.doi.org/10.1109/estream.2017.7950325.

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Shah, Muhammad Huzaifa, Muhammad Abu Bakar, Raja Hashim Ali, et al. "Investigating novel machine learning based intrusion detection models for NSL-KDD data sets." In 2023 International Conference on IT and Industrial Technologies (ICIT). IEEE, 2023. http://dx.doi.org/10.1109/icit59216.2023.10335831.

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Shehadeh, Aseel, Hanan ALTaweel, and Abdallah Qusef. "Analysis of Data Mining Techniques on KDD-Cup'99, NSL-KDD and UNSW-NB15 Datasets for Intrusion Detection." In 2023 24th International Arab Conference on Information Technology (ACIT). IEEE, 2023. http://dx.doi.org/10.1109/acit58888.2023.10453884.

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