A Survey of Intrusion Detection Models based on NSL-KDD Data Set

Thomas, Raji; Pavithran, Deepa

doi:10.1109/ctit.2018.8649498

Cited by 56 publications

(22 citation statements)

References 8 publications

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“…ML is a subset of AI that includes all the methods and algorithms which enable the machines to learn automatically using mathematical models in order to extract useful information from the large datasets 13,55 . The most common ML (also called Shallow Learning) algorithms used for IDS are Decision Tree, K ‐Nearest Neighbor (KNN), Artificial Neural Network (ANN), Support Vector Machine (SVM), K ‐Mean Clustering, Fast Learning Network, and Ensemble Methods.…”

Section: Ai Methods For Nidsmentioning

confidence: 99%

“…There are many survey papers in the literature that provide some implementation details on the IDS. Our article is different from the other review articles 11‐16 from three aspects: (i) We followed a systematic article selection process to obtain more focused articles on NIDS design considering AI tools. While the other studies reviewed the general IDS system without using the systematic approach.…”

Section: Introductionmentioning

confidence: 99%

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Network intrusion detection system: A systematic study of machine learning and deep learning approaches

Ahmad

Khan

Shiang

et al. 2020

Trans Emerging Tel Tech

643

215

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The rapid advances in the internet and communication fields have resulted in a huge increase in the network size and the corresponding data. As a result, many novel attacks are being generated and have posed challenges for network security to accurately detect intrusions. Furthermore, the presence of the intruders with the aim to launch various attacks within the network cannot be ignored. An intrusion detection system (IDS) is one such tool that prevents the network from possible intrusions by inspecting the network traffic, to ensure its confidentiality, integrity, and availability. Despite enormous efforts by the researchers, IDS still faces challenges in improving detection accuracy while reducing false alarm rates and in detecting novel intrusions. Recently, machine learning (ML) and deep learning (DL)‐based IDS systems are being deployed as potential solutions to detect intrusions across the network in an efficient manner. This article first clarifies the concept of IDS and then provides the taxonomy based on the notable ML and DL techniques adopted in designing network‐based IDS (NIDS) systems. A comprehensive review of the recent NIDS‐based articles is provided by discussing the strengths and limitations of the proposed solutions. Then, recent trends and advancements of ML and DL‐based NIDS are provided in terms of the proposed methodology, evaluation metrics, and dataset selection. Using the shortcomings of the proposed methods, we highlighted various research challenges and provided the future scope for the research in improving ML and DL‐based NIDS.

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Section: Ai Methods For Nidsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Network intrusion detection system: A systematic study of machine learning and deep learning approaches

Ahmad

Khan

Shiang

et al. 2020

Trans Emerging Tel Tech

643

215

View full text Add to dashboard Cite

show abstract

“…Using machine learning algorithms in Intrusion Detection System (IDS) is an attracting research area for cyber security researchers around the world [5][6][7][8][9][10][11][12][13][14]. In this research, experiments were made based on classifying NSL-KDD dataset to either normal or attack.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Machine Learning in Network Security Using KNIME Analytics

Abualkibash¹

2019

IJNSA

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Machine learning has more and more effect on our every day's life. This field keeps growing and expanding into new areas. Machine learning is based on the implementation of artificial intelligence that gives systems the capability to automatically learn and enhance from experiments without being explicitly programmed. Machine Learning algorithms apply mathematical equations to analyze datasets and predict values based on the dataset. In the field of cybersecurity, machine learning algorithms can be utilized to train and analyze the Intrusion Detection Systems (IDSs) on security-related datasets. In this paper, we tested different machine learning algorithms to analyze NSL-KDD dataset using KNIME analytics.

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“…The preprocessing steps are needed to prepare the data before it is analyzed using a machine learning algorithm. Therefore, the preprocessing step is an important step in Machine Learning algorithm and may impact the detection rate [31].…”

Section: Relevant Workmentioning

confidence: 99%