Robust Network Intrusion Detection System Based on Machine-Learning With Early Classification

Kim, Tae-Hoon; Pak, Wooguil

doi:10.1109/access.2022.3145002

Cited by 19 publications

(2 citation statements)

References 33 publications

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“…Recent ML and DL algorithms are also discussed by the authors which are used for IDS purposes. Kim and Pak (2022) proposed an ML model for intrusion detection purposes. Several algorithms are used, like AdaBoost, random forest, ELM, DNN, CNN, and XGBoost.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning-Based Intrusion Detection System: An Experimental Comparison

Hidayat,

Ali,

Arshad

2022

JCCE

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Recently, networks are moving toward automation and getting more and more intelligent. With the advent of big data and cloud computing technologies, lots and lots of data are being produced on the internet. Every day, petabytes of data are produced from websites, social media sites, or the internet. As more and more data are produced, a continuous threat of network attacks is also growing. An intrusion detection system (IDS) is used to detect such types of attacks in the network. IDS inspects packet headers and data and decides whether the traffic is anomalous or normal based on the contents of the packet. In this research, ML techniques are being used for intrusion detection purposes. Feature selection is also used for efficient and optimal feature selection. The research proposes a hybrid feature selection technique composed of the Pearson correlation coefficient and random forest model. For the machine learning (ML) model, decision tree, AdaBoost, and K-nearesrt neighbor are trained and tested on the TON_IoT dataset. The dataset is new and contains new and recent attack types and features. For deep learning (DL), multilayer perceptron (MLP) and long short-term memory are trained and tested. Evaluation is done on the basis of accuracy, precision, and recall. It is concluded from the results that the decision tree for ML and MLP for DL provides optimal accuracy with fewer false-positive and false-negative rates. It is also concluded from the results that the ML techniques are effective for detecting intrusion in the networks.

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Section: Related Workmentioning

confidence: 99%

Machine Learning-Based Intrusion Detection System: An Experimental Comparison

Hidayat,

Ali,

Arshad

2022

JCCE

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“…To reduce the quality of attributes needed for the testing and training stages, feature engineering operations must be carried out. A feature reduction method inspired by filters is applied to the work presented in this research, taking into account the features produced and important metrics produced by the XGBoost algorithm [5]. K-nearest neighbour (KNN), logistic regression (LR), support vector machine (SVM), convolutional neural network (CNN), and decision tree (DT) are examples of supervised ML techniques for IDS that we used in our experimental methods.…”

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confidence: 99%

Enhancing intrusion detection with imbalanced data classification and feature selection in machine learning algorithms

2024

IJATEE

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Hackers have evolved at a faster rate in terms of their capabilities because of precise technological developments like networks and communication systems. These crooks are constantly looking for new ways to jeopardize the security of computer networks (CN). Intrusion detection systems (IDS) consequently automatically become important parts of a CN. An IDS is a piece of software or hardware that tracks a company's network for potential threats. Meanwhile, an IDS is capable of reacting to and reporting any fraudulent activity. Node-based or host-based IDS (HIDS) [1, 2] Network based IDS (NIDS) or distributed-based IDS (DIDS), and hybrid-based IDS (HYIDS) are the three basic categories into which IDSs fall based on the unique IDS operation concept, this classification was created. *Author for correspondenceThe primary fundamental design goals of IDS are a decrease in false positive (FP) alerts and an increase in detection accuracy. Subsequently, when designing and implementing any IDS, that perspective must be taken into consideration [3]. In currently decades, machine learning (ML)-based IDS have taken over as the industry standard. According to ML systems may now potentially learn from the past and improve. Broadly speaking, the two ML philosophies of supervised ML and unsupervised ML can be separated. Models are learned using labeled data in supervised ML [4]. Unsupervised ML uses unstructured data to train models.The multiclass and binary classification objectives are carefully taken into consideration when using supervised ML techniques in this study. The classification process occurs whenever a supervised ML technique is asked to identify a specific quality. The training data for the techniques in this configuration frequently has large sizes and high-

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UIoTN‐PMSE: Ubiquitous IoT network‐based predictive modeling in smart environment

Karuppiah,

Ramana,

Mohanty

et al. 2023

Int J Communication

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SummaryWe proposed a three‐stage intrusion detection system that utilizes a predictive machine learning model to identify and mitigate attacks on ubiquitous network. In the first stage, we applied Apriori‐enabled Association Rule Mining (AARM) feature selection with support vector machine (SVM) for classification of flow of network. Second, we proposed ensemble learning‐based AARM model (PAEL) for behavior analysis. Finally, for classification of multi‐task labels, we proposed swarm bat optimization‐based PAEL model. The trained model is applied to edge and fog computing devices to obtain lower resource utilization and improve the efficiency of the system. The intrusion detection process is performed in three stages: (i) at the edge devices, where abnormal data from network traffic from IoT devices were identified, (ii) the abnormal data sample is sent to fog computing deivce to confirm the attacks and abnormalities, (iii) final identified data sample is sent to cloud server. At cloud, proposed predictive machine learning (ML)‐based generalized weight sum‐enabled ensemble learning (PML‐GWEL) model is trained on sample data, including new detected samples, to continually improve its accuracy. Once the model is trained, it is published to all nodes in the network to update their primary detector models and clear out any outdated pre‐detector models. This process helps to reduce the hardware resources used by the pre‐detector models and improve the overall efficiency of the system. The proposed model is compared with other existing techniques.

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Robust Network Intrusion Detection System Based on Machine-Learning With Early Classification

Cited by 19 publications

References 33 publications

Machine Learning-Based Intrusion Detection System: An Experimental Comparison

Machine Learning-Based Intrusion Detection System: An Experimental Comparison

Enhancing intrusion detection with imbalanced data classification and feature selection in machine learning algorithms

UIoTN‐PMSE: Ubiquitous IoT network‐based predictive modeling in smart environment

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