Performance Evaluation of a Smart Intrusion Detection System (IDS) Model

Istiaque, Shah Md.; Khan, Asif Iqbal; Hassan, Zaber Al; Waheed, Sajjad

doi:10.24018/ejers.2021.6.2.2371

Cited by 2 publications

(2 citation statements)

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“…97.52% accuracy is achieved in this research. Istiaque et al (2021) uses KDD CUP 99 datasets for training. Fifteen features are used along with the MLP algorithm.…”

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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“…97.52% accuracy is achieved in this research. Istiaque et al (2021) uses KDD CUP 99 datasets for training. Fifteen features are used along with the MLP algorithm.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning-Based Intrusion Detection System: An Experimental Comparison

Hidayat,

Ali,

Arshad

2022

JCCE

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“…In today's landscape, the optimization of processing and training procedures is imperative for constructing models that can effectively safeguard systems against dubious and spyware activities [12]. However, it's worth noting that many contemporary ML-IDS solutions tend to be limited by their reliance on small, outdated and balanced datasets for model development [17][18][19]. The focus on these smaller, often outdated datasets, coupled with imbalances in the data distribution, while facilitating preprocessing and training with diverse ML algorithms, raises questions regarding the practical applicability of these models in real-world scenarios, specifically when dealing with big data.…”

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confidence: 99%

Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction

Talukder,

Islam,

Uddin

et al. 2024

J Big Data

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Cybersecurity has emerged as a critical global concern. Intrusion Detection Systems (IDS) play a critical role in protecting interconnected networks by detecting malicious actors and activities. Machine Learning (ML)-based behavior analysis within the IDS has considerable potential for detecting dynamic cyber threats, identifying abnormalities, and identifying malicious conduct within the network. However, as the number of data grows, dimension reduction becomes an increasingly difficult task when training ML models. Addressing this, our paper introduces a novel ML-based network intrusion detection model that uses Random Oversampling (RO) to address data imbalance and Stacking Feature Embedding based on clustering results, as well as Principal Component Analysis (PCA) for dimension reduction and is specifically designed for large and imbalanced datasets. This model’s performance is carefully evaluated using three cutting-edge benchmark datasets: UNSW-NB15, CIC-IDS-2017, and CIC-IDS-2018. On the UNSW-NB15 dataset, our trials show that the RF and ET models achieve accuracy rates of 99.59% and 99.95%, respectively. Furthermore, using the CIC-IDS2017 dataset, DT, RF, and ET models reach 99.99% accuracy, while DT and RF models obtain 99.94% accuracy on CIC-IDS2018. These performance results continuously outperform the state-of-art, indicating significant progress in the field of network intrusion detection. This achievement demonstrates the efficacy of the suggested methodology, which can be used practically to accurately monitor and identify network traffic intrusions, thereby blocking possible threats.

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Performance Evaluation of a Smart Intrusion Detection System (IDS) Model

Cited by 2 publications

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Machine Learning-Based Intrusion Detection System: An Experimental Comparison

Machine Learning-Based Intrusion Detection System: An Experimental Comparison

Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction

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