Anomaly based intrusion detection using filter based feature selection on KDD-CUP 99

Kushwaha, Prashant; Buckchash, Himanshu; Raman, Balasubramanian

doi:10.1109/tencon.2017.8227975

Cited by 37 publications

(17 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In these years, machine learning algorithms are adopted to detect cyber-attacks against infrastructure and networks. Particularly, deep learning approaches are adopted to detect cyber-attacks by training the algorithm with the KDDCUP99 [ 17 ], while random forest, decision tree and gradient boost algorithms are adopted to implement intrusion detection system with KDDCUP99 [ 18 , 19 , 20 ] and naïve bayes algorithms are adopted for cyber-protection in [ 21 ]. In order to design and validate efficient and accurate protection systems to detect ICT attacks, the availability of public datasets is a critical point in the research world.…”

Section: Related Workmentioning

confidence: 99%

MQTTset, a New Dataset for Machine Learning Techniques on MQTT

Vaccari

Chiola

Aiello

et al. 2020

Sensors

170

View full text Add to dashboard Cite

IoT networks are increasingly popular nowadays to monitor critical environments of different nature, significantly increasing the amount of data exchanged. Due to the huge number of connected IoT devices, security of such networks and devices is therefore a critical issue. Detection systems assume a crucial role in the cyber-security field: based on innovative algorithms such as machine learning, they are able to identify or predict cyber-attacks, hence to protect the underlying system. Nevertheless, specific datasets are required to train detection models. In this work we present MQTTset, a dataset focused on the MQTT protocol, widely adopted in IoT networks. We present the creation of the dataset, also validating it through the definition of a hypothetical detection system, by combining the legitimate dataset with cyber-attacks against the MQTT network. Obtained results demonstrate how MQTTset can be used to train machine learning models to implement detection systems able to protect IoT contexts.

show abstract

Section: Related Workmentioning

confidence: 99%

MQTTset, a New Dataset for Machine Learning Techniques on MQTT

Vaccari

Chiola

Aiello

et al. 2020

Sensors

170

View full text Add to dashboard Cite

show abstract

“…The proposed method utilized naive Bayes algorithm to combine the results of base classifiers in ensemble. Kushwaha et al [40] determined the most appropriate feature selection algorithm to select the relevant features of KDD Cup 99 dataset. Thirty features were successfully chosen, while various classifiers were also used for classification.…”

Section: Mapping Selected Studies By Ensemble Methodsmentioning

confidence: 99%

Ensemble learning for intrusion detection systems: A systematic mapping study and cross-benchmark evaluation

Tama

Lim

2021

Computer Science Review

107

View full text Add to dashboard Cite

Intrusion detection systems (IDSs) are intrinsically linked to a comprehensive solution of cyberattacks prevention instruments. To achieve a higher detection rate, the ability to design an improved detection framework is sought after, particularly when utilizing ensemble learners. Designing an ensemble often lies in two main challenges such as the choice of available base classifiers and combiner methods. This paper performs an overview of how ensemble learners are exploited in IDSs by means of systematic mapping study. We collected and analyzed 124 prominent publications from the existing literature. The selected publications were then mapped into several categories such as years of publications, publication venues, datasets used, ensemble methods, and IDS techniques. Furthermore, this study reports and analyzes an empirical investigation of a new classifier ensemble approach, called stack of ensemble (SoE) for anomaly-based IDS. The SoE is an ensemble classifier that adopts parallel architecture to combine three individual ensemble learners such as random forest, gradient boosting machine, and extreme gradient boosting machine in a homogeneous manner. The performance significance among classification algorithms is statistically examined in terms of their Matthews correlation coefficients, accuracies, false positive rates, and area under ROC curve metrics. Our study fills the gap in current literature concerning an up-to-date systematic mapping study, not to mention an extensive empirical evaluation of the recent advances of ensemble learning techniques applied to IDSs.

show abstract

“…The 41 features labelled as either special attack type (DOS, U2R, R2L, and Probe) or normal. It is believed that attacks can be detected with the knowledge learned from the registered attacks [23]. Although widely used, this dataset has inherent flaws [2].…”

Section: Kdd99 Datasetmentioning

confidence: 99%

A Detailed Analysis of Benchmark Datasets for Network Intrusion Detection System

Ghurab

Gaphari

Alshami

et al. 2021

AJRCoS

View full text Add to dashboard Cite

The enormous increase in the use of the Internet in daily life has provided an opportunity for the intruder attempt to compromise the security principles of availability, confidentiality, and integrity. As a result, organizations are working to increase the level of security by using attack detection techniques such as Network Intrusion Detection System (NIDS), which monitors and analyzes network flow and attacks detection. There are a lot of researches proposed to develop the NIDS and depend on the dataset for the evaluation. Datasets allow evaluating the ability in detecting intrusion behavior. This paper introduces a detailed analysis of benchmark and recent datasets for NIDS. Specifically, we describe eight well-known datasets that include: KDD99, NSL-KDD, KYOTO 2006+, ISCX2012, UNSW-NB 15, CIDDS-001, CICIDS2017, and CSE-CIC-IDS2018. For each dataset, we provide a detailed analysis of its instances, features, classes, and the nature of the features. The main objective of this paper is to offer overviews of the datasets are available for the NIDS and what each dataset is comprised of. Furthermore, some recommendations were made to use network-based datasets.

show abstract

Anomaly based intrusion detection using filter based feature selection on KDD-CUP 99

Cited by 37 publications

References 14 publications

MQTTset, a New Dataset for Machine Learning Techniques on MQTT

MQTTset, a New Dataset for Machine Learning Techniques on MQTT

Ensemble learning for intrusion detection systems: A systematic mapping study and cross-benchmark evaluation

A Detailed Analysis of Benchmark Datasets for Network Intrusion Detection System

Contact Info

Product

Resources

About