Relevant bibliographies by topics / NSL-KDD

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  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: 16 February 2022

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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 (April 20, 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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Sonawane, Sandip. "Rule Based Learning Intrusion Detection System Using KDD and NSL KDD Dataset." Prestige International Journal of Management & IT - Sanchayan 04, no. 02 (December 15, 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 (August 1, 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 muncul, dan yang belum terdaftar pada signature serta untuk meningkatkan akurasi pendeteksian serangan-serangan baru pada Intruison Detection System (IDS). Data yang digunakan pada penelitian ini adalah data NSL-KDD, NSL-KDD telah menyediakan data training dan data testing untuk proses penelitian klasifikasi serangan. Dari data NSL-KDD akan dilakukan klasifikasi serangan menggunakan metode naive bayes agar serangan-serangan baru dapat terklasifikasi. Penelitian yang menggunakan metode naive bayes ini telah berhasil melakukan klasifikasi serangan-serangan baru dengan akurasi kebenaran adalah sebesar 81-84,67 %.
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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 (April 11, 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 handle DoS attacks which is a data mining (DM) model used to relaxes the conditional independence assumption of Naïve Bayes classifier (NB), proposed sys-tem used HNB Classifier supported with discretization and feature selection where select the best feature enhance the performance of the system and reduce consuming time. To evaluate the per-formance of proposal system, KDD 99 CUP and NSL KDD Datasets has been used. The experi-mental results show that the HNB classifier improves the performance of NIDS in terms of accu-racy and detecting DoS attacks, where the accuracy of detect DoS is 100% in three test KDD cup 99 dataset by used only 12 feature that selected by use gain ratio while in NSL KDD Dataset the accuracy of detect DoS attack is 90 % in three Experimental NSL KDD dataset by select 10 fea-ture only.
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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 (May 27, 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 of these three classification algorithms, Random Forest was proved to produce the best results and therefore, Random Forest classification method was used to further analyze the data. The results are analyzed and depicted in this paper with the help of feature/attribute selection by applying all the possible combinations. Conclusion: There are total of eight significant attributes selected after applying various attribute selection methods on NSL KDD dataset.
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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 (July 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, named support vector machine-based IDS (SVM-IDS), is prompted using the feature selected by the proposed method. The SVM-IDS performance is evaluated using two benchmark datasets of intrusion detection, including KDD Cup 99 and NSL-KDD. Our proposed method provided more significant features for SVM-IDS and compared with the other state-of-the-art methods. The experimental results demonstrate that proposed method achieves a maximum accuracy as 98.95% in KDD Cup 99 data set and 98.12% in the NSL-KDD data set.
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Dr.R.Venkatesh, Kavitha S, Dr Uma Maheswari N,. "Network Anomaly Detection for NSL-KDD Dataset Using Deep Learning." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (March 31, 2021): 821–27. http://dx.doi.org/10.17762/itii.v9i2.419.

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Deep learning based intrusion detection cyber security methods gained increased popularity. The essential element to provide protection to the ICT infrastructure is the intrusion detection systems (IDSs). Intelligent solutions are necessary to control the complexity and increase in the new attack types. The intelligent system (DL/ML) has been widely used with its benefits to effectively deal with complex and great dimensional data. The IDS has various attack types like known, unknown, zero day attacks are attractive to and detected using unsupervised machine learning techniques. A novel methodology has been proposed that combines the benefits of Isolation forest (One Class) Support Vector Machine (OCSVM) with active learning method to detect threats without any prior knowledge. The NSL-KDD dataset has been used to evaluate the various DL methods with active learning method. The results show that this method performs better than other techniques. The design methodology inspires the efforts to emerging anomaly detection.
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Muhuri, Pramita Sree, Prosenjit Chatterjee, Xiaohong Yuan, Kaushik Roy, and Albert Esterline. "Using a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) to Classify Network Attacks." Information 11, no. 5 (May 1, 2020): 243. http://dx.doi.org/10.3390/info11050243.

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An intrusion detection system (IDS) identifies whether the network traffic behavior is normal or abnormal or identifies the attack types. Recently, deep learning has emerged as a successful approach in IDSs, having a high accuracy rate with its distinctive learning mechanism. In this research, we developed a new method for intrusion detection to classify the NSL-KDD dataset by combining a genetic algorithm (GA) for optimal feature selection and long short-term memory (LSTM) with a recurrent neural network (RNN). We found that using LSTM-RNN classifiers with the optimal feature set improves intrusion detection. The performance of the IDS was analyzed by calculating the accuracy, recall, precision, f-score, and confusion matrix. The NSL-KDD dataset was used to analyze the performances of the classifiers. An LSTM-RNN was used to classify the NSL-KDD datasets into binary (normal and abnormal) and multi-class (Normal, DoS, Probing, U2R, and R2L) sets. The results indicate that applying the GA increases the classification accuracy of LSTM-RNN in both binary and multi-class classification. The results of the LSTM-RNN classifier were also compared with the results using a support vector machine (SVM) and random forest (RF). For multi-class classification, the classification accuracy of LSTM-RNN with the GA model is much higher than SVM and RF. For binary classification, the classification accuracy of LSTM-RNN is similar to that of RF and higher than that of SVM.
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Choudhary, Sarika, and Nishtha Kesswani. "Analysis of KDD-Cup’99, NSL-KDD and UNSW-NB15 Datasets using Deep Learning in IoT." Procedia Computer Science 167 (2020): 1561–73. http://dx.doi.org/10.1016/j.procs.2020.03.367.

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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 these algorithms are compared with accuracy, error rate and average cost on modified versions of NSL-KDD train and test dataset where the instances are classified into normal and four cyber-attack categories: DoS, Probing, R2L and U2R. Additionally the most important features to detect cyber-attacks in all categories and in each category are evaluated with Weka’s Attribute Evaluator and ranked according to Information Gain. The results show that the classification algorithm with best performance on the dataset is the k-NN algorithm. The most important features to detect cyber-attacks are basic features such as the number of seconds of a network connection, the protocol used for the connection, the network service used, normal or error status of the connection and the number of data bytes sent. The most important features to detect DoS, Probing and R2L attacks are basic features and the least important features are content features. Unlike U2R attacks, where the content features are the most important features to detect attacks.
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Book chapters on the topic "NSL-KDD"

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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, 207–18. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63645-0_23.

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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, 137–53. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23204-1_15.

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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, 452–57. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7605-3_74.

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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, 519–31. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6001-5_43.

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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, 223–41. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6876-3_17.

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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, 111–22. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27192-3_9.

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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), 866–75. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43192-1_94.

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Panwar, Shailesh Singh, and Y. P. Raiwani. "Improving the Performance of Classification Algorithms with Supervised Filter Discretization Using WEKA on NSL-KDD Dataset." In Springer Transactions in Civil and Environmental Engineering, 217–27. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0954-4_16.

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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, 205–11. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07353-8_24.

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Vinutha, H. P., and B. Poornima. "Analysis of NSL-KDD Dataset Using K-Means and Canopy Clustering Algorithms Based on Distance Metrics." In Integrated Intelligent Computing, Communication and Security, 193–200. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8797-4_21.

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Conference papers on the topic "NSL-KDD"

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Ingre, Bhupendra, and Anamika Yadav. "Performance analysis of NSL-KDD dataset using ANN." In 2015 International Conference on Signal Processing And Communication Engineering Systems (SPACES). IEEE, 2015. http://dx.doi.org/10.1109/spaces.2015.7058223.

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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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Meena, Gaurav, and Ravi Raj Choudhary. "A review paper on IDS classification using KDD 99 and NSL KDD dataset in WEKA." In 2017 International Conference on Computer, Communications and Electronics (Comptelix). IEEE, 2017. http://dx.doi.org/10.1109/comptelix.2017.8004032.

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Ding, Yalei, and Yuqing Zhai. "Intrusion Detection System for NSL-KDD Dataset Using Convolutional Neural Networks." In the 2018 2nd International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3297156.3297230.

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Deshmukh, Datta H., Tushar Ghorpade, and Puja Padiya. "Improving classification using preprocessing and machine learning algorithms on NSL-KDD dataset." In 2015 International Conference on Communication, Information & Computing Technology (ICCICT). IEEE, 2015. http://dx.doi.org/10.1109/iccict.2015.7045674.

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Abrar, Iram, Zahrah Ayub, Faheem Masoodi, and Alwi M. Bamhdi. "A Machine Learning Approach for Intrusion Detection System on NSL-KDD Dataset." In 2020 International Conference on Smart Electronics and Communication (ICOSEC). IEEE, 2020. http://dx.doi.org/10.1109/icosec49089.2020.9215232.

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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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Pervez, Muhammad Shakil, and Dewan Md Farid. "Feature selection and intrusion classification in NSL-KDD cup 99 dataset employing SVMs." In 2014 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA). IEEE, 2014. http://dx.doi.org/10.1109/skima.2014.7083539.

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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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Zhang, Chongzhen, Fangming Ruan, Lan Yin, Xi Chen, Lidong Zhai, and Feng Liu. "A Deep Learning Approach for Network Intrusion Detection Based on NSL-KDD Dataset." In 2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID). IEEE, 2019. http://dx.doi.org/10.1109/icasid.2019.8925239.

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