SmartIDS: A Comparative Study of Intelligent Intrusion Detection Systems for Internet of Things

Abdelmoumin, Ghada; Rawat, Danda B.

doi:10.1007/978-3-030-89906-6_28

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…Given the rare event case, the worst-case AMiDS model refers to an AMiDS model that did not perform well under regular learning. In doing so, we consider the performance of the AMiDS models when applying regular learning, as presented by the comparative study in [70], to quantify the improvement gained from applying imbalanced learning. Further, we used the two-class neural network IDS (tNN IDS) model used by the authors of [70] as a baseline to evaluate the performance of the AMiDS models under regular learning conditions.…”

Section: Performance Evaluation and Discussionmentioning

confidence: 99%

“…The study presented in [69] provides detailed descriptions and performance analyses of the PCA-and oSVM-based AMiDS, referred to by the paper as AN-Intel-IDS models, using regular learning. To generate datasets for imbalanced learning using the sampling methods, we used the pre-processed IoT-Botnet (PiB) datasets used in [70]. The PiB dataset has two classes: the normal minority class and the anomalous majority class.…”

Section: Methodsmentioning

confidence: 99%

“…To understand the impact of imbalanced learning on the performances of the AMiDS models in the rare event prediction case, we considered the UNSW_2018_IoT_Botnet dataset, which is available from the IEEE dataport [71]. However, we used the PiB datasets used in [70]. The dataset is a sample from one of the extracted dataset's files but with fewer features.…”

Section: Dataset For Amids Imbalanced Learningmentioning

confidence: 99%

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Studying Imbalanced Learning for Anomaly-Based Intelligent IDS for Mission-Critical Internet of Things

Abdelmoumin,

Rawat,

Rahman

2023

JCP

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Training-anomaly-based, machine-learning-based, intrusion detection systems (AMiDS) for use in critical Internet of Things (CioT) systems and military Internet of Things (MioT) environments may involve synthetic data or publicly simulated data due to data restrictions, data scarcity, or both. However, synthetic data can be unrealistic and potentially biased, and simulated data are invariably static, unrealistic, and prone to obsolescence. Building an AMiDS logical model to predict the deviation from normal behavior in MioT and CioT devices operating at the sensing or perception layer due to adversarial attacks often requires the model to be trained using current and realistic data. Unfortunately, while real-time data are realistic and relevant, they are largely imbalanced. Imbalanced data have a skewed class distribution and low-similarity index, thus hindering the model’s ability to recognize important features in the dataset and make accurate predictions. Data-driven learning using data sampling, resampling, and generative methods can lessen the adverse impact of a data imbalance on the AMiDS model’s performance and prediction accuracy. Generative methods enable passive adversarial learning. This paper investigates several data sampling, resampling, and generative methods. It examines their impacts on the performance and prediction accuracy of AMiDS models trained using imbalanced data drawn from the UNSW_2018_IoT_Botnet dataset, a publicly available IoT dataset from the IEEEDataPort. Furthermore, it evaluates the performance and predictability of these models when trained using data transformation methods, such as normalization and one-hot encoding, to cover a skewed distribution, data sampling and resampling methods to address data imbalances, and generative methods to train the models to increase the model’s robustness to recognize new but similar attacks. In this initial study, we focus on CioT systems and train PCA-based and oSVM-based AMiDS models constructed using low-complexity PCA and one-class SVM (oSVM) ML algorithms to fit an imbalanced ground truth IoT dataset. Overall, we consider the rare event prediction case where the minority class distribution is disproportionately low compared to the majority class distribution. We plan to use transfer learning in future studies to generalize our initial findings to the MioT environment. We focus on CioT systems and MioT environments instead of traditional or non-critical IoT environments due to the stringent low energy, the minimal response time constraints, and the variety of low-power, situational-aware (or both) things operating at the sensing or perception layer in a highly complex and open environment.

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Section: Performance Evaluation and Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Dataset For Amids Imbalanced Learningmentioning

confidence: 99%

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Studying Imbalanced Learning for Anomaly-Based Intelligent IDS for Mission-Critical Internet of Things

Abdelmoumin,

Rawat,

Rahman

2023

JCP

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A Comprehensive Analysis of the Machine Learning Algorithms in IoT IDS Systems

Ozdogan

2024

IEEE Access

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In this study, we comprehensively compare machine learning algorithms in IoT IDS (Internet of Things Intrusion Detection System) systems from various aspects. We evaluate accuracy, precision, and training time. We examine the effects of data preprocessing techniques including normalization, outlier removal, standardization, and regularization on the datasets. Furthermore, we investigate the impact of dataset balancing, considering both balanced and imbalanced scenarios, on machine learning performance. We also analyzed the contribution of feature selection on the four different datasets. Based on our findings, we observe that certain preprocessing operations provide significant advantages in various ML algorithms, whereas others have very low impact, and their performance varies depending on the dataset and feature selection. The aim of this study is to facilitate the complexity and lengthiness of machine learning processes and algorithm selection, providing insights for future academic research. By addressing this objective, we aim to shed light on simplifying the utilization of machine learning algorithms. The study addresses the challenges arising from the complexity of machine learning processes in IoT IDS systems. This contribution can greatly benefit researchers in their academic endeavors. This multifaceted approach proves beneficial when comparing the methods under consideration, fostering a scientific discourse on their efficacy within contexts.

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On the Performance of Machine Learning Models for Anomaly-Based Intelligent Intrusion Detection Systems for the Internet of Things

Abdelmoumin

Rawat

Rahman

2022

IEEE Internet Things J.

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SmartIDS: A Comparative Study of Intelligent Intrusion Detection Systems for Internet of Things

Cited by 4 publications

References 27 publications

Studying Imbalanced Learning for Anomaly-Based Intelligent IDS for Mission-Critical Internet of Things

Studying Imbalanced Learning for Anomaly-Based Intelligent IDS for Mission-Critical Internet of Things

A Comprehensive Analysis of the Machine Learning Algorithms in IoT IDS Systems

On the Performance of Machine Learning Models for Anomaly-Based Intelligent Intrusion Detection Systems for the Internet of Things

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