GAAINet: A Generative Adversarial Artificial Immune Network Model for Intrusion Detection in Industrial IoT Systems

Sithungu, Siphesihle; Ehlers, Elizabeth M.

doi:10.12720/jait.13.5.456-461

Cited by 20 publications

(5 citation statements)

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“…Additionally, Sithungu and Ehlers 56 introduced an intrusion detection model designed for Industrial Internet of Things (IIoT) systems. Their model, called GAAINet, integrated concepts from GANs and artificial immune networks.…”

Section: Nature‐inspired Algorithmsmentioning

confidence: 99%

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The applications of nature‐inspired algorithms in Internet of Things‐based healthcare service: A systematic literature review

Amiri,

Heidari,

Zavvar

et al. 2024

Trans Emerging Tel Tech

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Nature‐inspired algorithms revolve around the intersection of nature‐inspired algorithms and the IoT within the healthcare domain. This domain addresses the emerging trends and potential synergies between nature‐inspired computational approaches and IoT technologies for advancing healthcare services. Our research aims to fill gaps in addressing algorithmic integration challenges, real‐world implementation issues, and the efficacy of nature‐inspired algorithms in IoT‐based healthcare. We provide insights into the practical aspects and limitations of such applications through a systematic literature review. Specifically, we address the need for a comprehensive understanding of the applications of nature‐inspired algorithms in IoT‐based healthcare, identifying gaps such as the lack of standardized evaluation metrics and studies on integration challenges and security considerations. By bridging these gaps, our paper offers insights and directions for future research in this domain, exploring the diverse landscape of nature‐inspired algorithms in healthcare. Our chosen methodology is a Systematic Literature Review (SLR) to investigate related papers rigorously. Categorizing these algorithms into groups such as genetic algorithms, particle swarm optimization, cuckoo algorithms, ant colony optimization, other approaches, and hybrid methods, we employ meticulous classification based on critical criteria. MATLAB emerges as the predominant programming language, constituting 37.9% of cases, showcasing a prevalent choice among researchers. Our evaluation emphasizes adaptability as the paramount parameter, accounting for 18.4% of considerations. By shedding light on attributes, limitations, and potential directions for future research and development, this review aims to contribute to a comprehensive understanding of nature‐inspired algorithms in the dynamic landscape of IoT‐based healthcare services.

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Section: Nature‐inspired Algorithmsmentioning

confidence: 99%

“…and firefly algorithm-optimized routing scheme for ensuring secure and reliable data communication in IoT-based healthcare environments.Additionally, Sithungu and Ehlers56 introduced an intrusion detection model designed for Industrial Internet of Things (IIoT) systems. Their model, called GAAINet, integrated concepts from GANs and artificial immune networks.…”

mentioning

confidence: 99%

The applications of nature‐inspired algorithms in Internet of Things‐based healthcare service: A systematic literature review

Amiri,

Heidari,

Zavvar

et al. 2024

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…Deep neural networks (DNNs), a key branch of artificial intelligence [1], have achieved considerable success in diverse domains. DNNs have found applications in a variety of critical systems including image recognition [2], speech recognition [3], vehicle detection [4], image coloring [5] and Intrusion Detection [6]. However, studies have revealed the susceptibility of DNNs to adversarial attacks [7], involving the introduction of subtle perturbations to the input.…”

Section: Introductionmentioning

confidence: 99%

Purifying adversarial perturbations based on a text-guided diffusion model

Zhu,

Lyu

2024

Fifth International Conference on Computer Vision and Computational Intelligence (CVCI 2024)

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Deep neural networks (DNNs) have found wide applications in various domains. However, recent research has revealed the vulnerability of DNNs to adversarial examples. Existing adversarial attack methods can easily mislead the models. Moreover, while traditional denoising techniques effectively deter certain attacks, they have limitations. To address this, We propose Text-guided Diffusion Model Purification (TGDP), an adversarial perturbation purification method based on diffusion models.This method preprocesses input images to purify adversarial perturbations. TGDP employs Protogen x3.4 (Photorealism) Official Release as a diffusion model for conditional image generation. During the generation process, text information is incorporated to enhance control over the diffusion model instead of relying entirely on the internal randomness of the model. By iteratively adding Gaussian noise to disrupt the adversarial examples and reversing the noise addition process to restore the image afterward, we can completely eliminate carefully crafted perturbations, achieving the purification objective. Extensive experiments on the ImageNet dataset against common adversarial attacks demonstrate that TGDP outperforms other defense methods applied to the ImageNet dataset.

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“…However, the high degree of heterogeneity [3] that characterizes this environment makes this operation very difficult, due to both the continuous efforts of the attackers to violate the systems with more and more sophisticated techniques (a case in point is the difficulty of detecting the zero-day attacks [4]), and the problem that many attacks are often characterized by a behavior very similar to that of a legitimate network activity [5], making it difficult to detect them. To face these problems, researchers are constantly looking for more and more efficient Intrusion Detection Systems (IDSs) [6], which are designed using various techniques such as, just to name a few, those based on Machine Learning and Deep Learning [7,8], Artificial Intelligence [9], Artificial Neural Networks [10][11][12][13], Fuzzy Logic [14], often combining more than one to define hybrid solutions [15]. Starting from the consideration that most of the approaches and strategies in the literature related to the IDS domain exploit the entire training set to define the classification model [5,[16][17][18], we have trivially observed that a training dataset refers to single events in terms of data rows and to the different features that characterize each event in terms of data columns.…”

Section: Introductionmentioning

confidence: 99%

Leveraging the Training Data Partitioning to Improve Events Characterization in Intrusion Detection Systems

Saia,

Carta,

Fenu

et al. 2023

JAIT

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The ever-increasing use of services based on computer networks, even in crucial areas unthinkable until a few years ago, has made the security of these networks a crucial element for anyone, also in consideration of the increasingly sophisticated techniques and strategies available to attackers. In this context, Intrusion Detection Systems (IDSs) play a primary role since they are responsible for analyzing and classifying each network activity as legitimate or illegitimate, allowing us to take the necessary countermeasures at the appropriate time. However, these systems are not infallible due to several reasons, the most important of which are the constant evolution of the attacks (e.g., zero-day attacks) and the problem that many of the attacks have behavior similar to those of legitimate activities, and therefore they are very hard to identify. This work relies on the hypothesis that the subdivision of the training data used for the IDS classification model definition into a certain number of partitions, in terms of events and features, can improve the characterization of the network events, improving the system performance. The non-overlapping data partitions train independent classification models, classifying the event according to a majority-voting rule. A series of experiments conducted on a benchmark real-world dataset support the initial hypothesis, showing a performance improvement with respect to a canonical training approach.

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GAAINet: A Generative Adversarial Artificial Immune Network Model for Intrusion Detection in Industrial IoT Systems

Cited by 20 publications

References 0 publications

The applications of nature‐inspired algorithms in Internet of Things‐based healthcare service: A systematic literature review

The applications of nature‐inspired algorithms in Internet of Things‐based healthcare service: A systematic literature review

Purifying adversarial perturbations based on a text-guided diffusion model

Leveraging the Training Data Partitioning to Improve Events Characterization in Intrusion Detection Systems

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