Machine Learning Classifiers for Network Intrusion Detection System: Comparative Study

Almomani, Omar; Almaiah, Mohammed Amin; Alsaaidah, Adeeb; Smadi, Sami; Mohammad, Adel Hamdan; Althunibat, Ahmad

doi:10.1109/icit52682.2021.9491770

Cited by 45 publications

(25 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To reduce network latency (NL) and output, smart contracts (SC) and transaction checking on fog nodes (FN) is recommended. The network architecture will challenge cost reductions in the cloud and optimize cloud and FN instancing performance to boost the efficiency of the hyperledger BC network [ 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 ].…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A Lightweight Hybrid Deep Learning Privacy Preserving Model for FC-Based Industrial Internet of Medical Things

Almaiah

Ali

Hajjej

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

The Industrial Internet of Things (IIoT) is gaining importance as most technologies and applications are integrated with the IIoT. Moreover, it consists of several tiny sensors to sense the environment and gather the information. These devices continuously monitor, collect, exchange, analyze, and transfer the captured data to nearby devices or servers using an open channel, i.e., internet. However, such centralized system based on IIoT provides more vulnerabilities to security and privacy in IIoT networks. In order to resolve these issues, we present a blockchain-based deep-learning framework that provides two levels of security and privacy. First a blockchain scheme is designed where each participating entities are registered, verified, and thereafter validated using smart contract based enhanced Proof of Work, to achieve the target of security and privacy. Second, a deep-learning scheme with a Variational AutoEncoder (VAE) technique for privacy and Bidirectional Long Short-Term Memory (BiLSTM) for intrusion detection is designed. The experimental results are based on the IoT-Botnet and ToN-IoT datasets that are publicly available. The proposed simulations results are compared with the benchmark models and it is validated that the proposed framework outperforms the existing system.

show abstract

Section: Methodsmentioning

confidence: 99%

“…This includes but is not limited to the following ICS cyber attacks: 1. Stuxnet Malware [ 50 , 51 , 52 , 53 , 54 , 55 ]. Multiple attacks on the Ukraine Power Grid in 2015 and 2016 [ 56 , 57 ].…”

Section: Background and Related Studiesmentioning

confidence: 99%

A Lightweight Hybrid Deep Learning Privacy Preserving Model for FC-Based Industrial Internet of Medical Things

Almaiah

Ali

Hajjej

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Machine Learning classifiers (MLC) algorithms are used for classifying data. Different methods and techniques are used for classification, such as Naïve Bayesian, Support Vector Machines (SVM), Artificial Neural Network (ANN), Decision Tree (C4.5), or (J48) classifier, K-Nearest Neighbor, and Random Forests (RF) [5,8,11,[49][50][51][52][53][54][55]. In this research, J48 and Random Forest classifiers are used.…”

Section: Bio-inspired Optimization Algorithms and Machine Learning Cl...mentioning

confidence: 99%

Bio-inspired Hybrid Feature Selection Model for Intrusion Detection

Mohammad¹,

Alwada’n²,

Almomani³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Intrusion detection is a serious and complex problem. Undoubtedly due to a large number of attacks around the world, the concept of intrusion detection has become very important. This research proposes a multilayer bioinspired feature selection model for intrusion detection using an optimized genetic algorithm. Furthermore, the proposed multilayer model consists of two layers (layers 1 and 2). At layer 1, three algorithms are used for the feature selection. The algorithms used are Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Firefly Optimization Algorithm (FFA). At the end of layer 1, a priority value will be assigned for each feature set. At layer 2 of the proposed model, the Optimized Genetic Algorithm (GA) is used to select one feature set based on the priority value. Modifications are done on standard GA to perform optimization and to fit the proposed model. The Optimized GA is used in the training phase to assign a priority value for each feature set. Also, the priority values are categorized into three categories: high, medium, and low. Besides, the Optimized GA is used in the testing phase to select a feature set based on its priority. The feature set with a high priority will be given a high priority to be selected. At the end of phase 2, an update for feature set priority may occur based on the selected features priority and the calculated F-Measures. The proposed model can learn and modify feature sets priority, which will be reflected in selecting features. For evaluation purposes, two well-known datasets are used in these experiments. The first dataset is UNSW-NB15, the other dataset is the NSL-KDD. Several evaluation criteria are used, such as precision, recall, and F-Measure. The experiments in this research suggest that the proposed model has a powerful and promising mechanism for the intrusion detection system.

show abstract

“…Classifiers that work well to detect a particular attack may not work well for another. According to several previous research publications [9]- [11], there are still certain drawbacks, no matter the pre-processing or feature selection methods used alongside the classifiers. The ML architecture for IDS continuously grows into increasingly complex classifiers to increase its efficacy.…”

Section: Introductionmentioning

confidence: 99%

IoT Network Intrusion Detection with Ensemble Learners

Abdulkareem

Foh

Lee

et al. 2022

2022 13th International Conference on Information and Communication Technology Convergence (ICTC)

View full text Add to dashboard Cite

Protecting information systems against intruders' attacks requires utilising intrusion detection systems. Over the past several years, many open-source intrusion datasets have been made available so that academics and researchers can analyse and assess various detection classifiers' effectiveness. These datasets are made available with a full complement of illustrative network features. In this research, we investigate the issue of Network Intrusion Detection (NID) by utilising an Internet of Things (IoT) dataset called Bot-IoT to evaluate the detection efficiency and effectiveness of five different Ensemble Learning Classifiers (ELCs). Our experiment's results showed that despite all ELCs recording high classification metric scores, CatBoost emerged as the ELC that performed the best in our experiment in terms of Accuracy, Precision, F1-Score, Training and Test Time.

show abstract

Machine Learning Classifiers for Network Intrusion Detection System: Comparative Study

Cited by 45 publications

References 25 publications

A Lightweight Hybrid Deep Learning Privacy Preserving Model for FC-Based Industrial Internet of Medical Things

A Lightweight Hybrid Deep Learning Privacy Preserving Model for FC-Based Industrial Internet of Medical Things

Bio-inspired Hybrid Feature Selection Model for Intrusion Detection

IoT Network Intrusion Detection with Ensemble Learners

Contact Info

Product

Resources

About