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

Abdelmoumin, Ghada; Rawat, Danda B.; Rahman, Abdul

doi:10.1109/jiot.2021.3103829

Cited by 57 publications

(26 citation statements)

References 26 publications

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“…IoT Intrusion Dataset: UNSW-TONIOT The UNSW-TONIOT dataset [15] was another popular IoT intrusion dataset [48] released in 2021. It contains IoT intrusion data that comply with the protocols, standards and technologies commonly used by current IoT devices.…”

Section: A Dataset and Setupmentioning

confidence: 99%

Joint Semantic Transfer Network for IoT Intrusion Detection

Wang

Xie

et al. 2023

IEEE Internet Things J.

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In this paper, we propose a Joint Semantic Transfer Network (JSTN) towards effective intrusion detection for large-scale scarcely labelled IoT domain. As a multi-source heterogeneous domain adaptation (MS-HDA) method, the JSTN integrates a knowledge rich network intrusion (NI) domain and another small-scale IoT intrusion (II) domain as source domains, and preserves intrinsic semantic properties to assist target II domain intrusion detection. The JSTN jointly transfers the following three semantics to learn a domain-invariant and discriminative feature representation. The scenario semantic endows source NI and II domain with characteristics from each other to ease the knowledge transfer process via a confused domain discriminator and categorical distribution knowledge preservation. It also reduces the source-target discrepancy to make the shared feature space domain-invariant. Meanwhile, the weighted implicit semantic transfer boosts discriminability via a fine-grained knowledge preservation, which transfers the source categorical distribution to the target domain. The source-target divergence guides the importance weighting during knowledge preservation to reflect the degree of knowledge learning. Additionally, the hierarchical explicit semantic alignment performs centroid-level and representative-level alignment with the help of a geometric similarity-aware pseudo-label refiner, which exploits the value of unlabelled target II domain and explicitly aligns feature representations from a global and local perspective in a concentrated manner. Comprehensive experiments on various tasks verify the superiority of the JSTN against state-of-the-art comparing methods, on average a 10.3% of accuracy boost is achieved. The statistical soundness of each constituting component and the computational efficiency are also verified.

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Section: A Dataset and Setupmentioning

confidence: 99%

Joint Semantic Transfer Network for IoT Intrusion Detection

Wang

Xie

et al. 2023

IEEE Internet Things J.

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“…Following [29], we utilise 10000 data records. The original dataset utilises a 46-dimensional feature IoT Intrusion Dataset: UNSW-TONIOT The UNSW-TONIOT dataset [33] serves as one of the latest IoT intrusion datasets [34], released in 2021. It reflects modern IoT standards, protocols, and is captured on modern testbed consists of 7 types of IoT devices, such as smart fridge, modbus sensor, GPS tracker, etc.…”

Section: A Experimental Datasetsmentioning

confidence: 99%

Heterogeneous Domain Adaptation for IoT Intrusion Detection: A Geometric Graph Alignment Approach

Wu¹,

Hao²,

Wang³

et al. 2023

Preprint

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Data scarcity hinders the usability of datadependent algorithms when tackling IoT intrusion detection (IID). To address this, we utilise the data rich network intrusion detection (NID) domain to facilitate more accurate intrusion detection for IID domains. In this paper, a Geometric Graph Alignment (GGA) approach is leveraged to mask the geometric heterogeneities between domains for better intrusion knowledge transfer. Specifically, each intrusion domain is formulated as a graph where vertices and edges represent intrusion categories and category-wise interrelationships, respectively. The overall shape is preserved via a confused discriminator incapable to identify adjacency matrices between different intrusion domain graphs. A rotation avoidance mechanism and a centre point matching mechanism is used to avoid graph misalignment due to rotation and symmetry, respectively. Besides, categorywise semantic knowledge is transferred to act as vertex-level alignment. To exploit the target data, a pseudo-label election mechanism that jointly considers network prediction, geometric property and neighbourhood information is used to produce finegrained pseudo-label assignment. Upon aligning the intrusion graphs geometrically from different granularities, the transferred intrusion knowledge can boost IID performance. Comprehensive experiments on several intrusion datasets demonstrate state-ofthe-art performance of the GGA approach and validate the usefulness of GGA constituting components.

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“…A CNN is a deep learning approach that uses a convolution layer to automatically extract useful features from the authentic data feature plane through convolution layer. Choosing optimal hyperparameters for deep learning models is crucial [ 60 ]. There are two main kinds of techniques for optimizing hyperparameters: automatic search methods and manual search methods.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel hybrid optimization enabled robust CNN algorithm for an IoT network intrusion detection approach

et al. 2022

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Due to the huge number of connected Internet of Things (IoT) devices within a network, denial of service and flooding attacks on networks are on the rise. IoT devices are disrupted and denied service because of these attacks. In this study, we proposed a novel hybrid meta-heuristic adaptive particle swarm optimization–whale optimizer algorithm (APSO-WOA) for optimization of the hyperparameters of a convolutional neural network (APSO-WOA-CNN). The APSO–WOA optimization algorithm’s fitness value is defined as the validation set’s cross-entropy loss function during CNN model training. In this study, we compare our optimization algorithm with other optimization algorithms, such as the APSO algorithm, for optimization of the hyperparameters of CNN. In model training, the APSO–WOA–CNN algorithm achieved the best performance compared to the FNN algorithm, which used manual parameter settings. We evaluated the APSO–WOA–CNN algorithm against APSO–CNN, SVM, and FNN. The simulation results suggest that APSO–WOA–CNf[N is effective and can reliably detect multi-type IoT network attacks. The results show that the APSO–WOA–CNN algorithm improves accuracy by 1.25%, average precision by 1%, the kappa coefficient by 11%, Hamming loss by 1.2%, and the Jaccard similarity coefficient by 2%, as compared to the APSO–CNN algorithm, and the APSO–CNN algorithm achieves the best performance, as compared to other algorithms.

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

Cited by 57 publications

References 26 publications

Joint Semantic Transfer Network for IoT Intrusion Detection

Joint Semantic Transfer Network for IoT Intrusion Detection

Heterogeneous Domain Adaptation for IoT Intrusion Detection: A Geometric Graph Alignment Approach

A novel hybrid optimization enabled robust CNN algorithm for an IoT network intrusion detection approach

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