An end-to-end learning approach for enhancing intrusion detection in Industrial-Internet of Things

Hassini, Karima; Khalis, Safae; Habibi, Omar; Chemmakha, Mohammed; Lazaar, Mohamed

doi:10.1016/j.knosys.2024.111785

Cited by 7 publications

(1 citation statement)

References 17 publications

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“…End-to-end learning: these models can undergo end-to-end training, seamlessly integrating feature extraction and classification stages to streamline the detection process [32]. • Adaptability: deep learning models demonstrate remarkable adaptability to diverse manipulation techniques, rendering them adept at detecting evolving forms of deepfake content [33,34].…”

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confidence: 99%

An Approach to Deepfake Video Detection Based on ACO-PSO Features and Deep Learning

Alhaji,

Celik,

Goel

2024

Electronics

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The rapid advancement of deepfake technology presents significant challenges in detecting highly convincing fake videos, posing risks such as misinformation, identity theft, and privacy violations. In response, this paper proposes an innovative approach to deepfake video detection by integrating features derived from ant colony optimization–particle swarm optimization (ACO-PSO) and deep learning techniques. The proposed methodology leverages ACO-PSO features and deep learning models to enhance detection accuracy and robustness. Features from ACO-PSO are extracted from the spatial and temporal characteristics of video frames, capturing subtle patterns indicative of deepfake manipulation. These features are then used to train a deep learning classifier to automatically distinguish between authentic and deepfake videos. Extensive experiments using comparative datasets demonstrate the superiority of the proposed method in terms of detection accuracy, robustness to manipulation techniques, and generalization to unseen data. The computational efficiency of the approach is also analyzed, highlighting its practical feasibility for real-time applications. The findings revealed that the proposed method achieved an accuracy of 98.91% and an F1 score of 99.12%, indicating remarkable success in deepfake detection. The integration of ACO-PSO features and deep learning enables comprehensive analysis, bolstering precision and resilience in detecting deepfake content. This approach addresses the challenges involved in facial forgery detection and contributes to safeguarding digital media integrity amid misinformation and manipulation.

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confidence: 99%

An Approach to Deepfake Video Detection Based on ACO-PSO Features and Deep Learning

Alhaji,

Celik,

Goel

2024

Electronics

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Enhanced Deep Learning for IIoT Threat Intelligence: Revealing Advanced Persistent Threat Attack Patterns

Priya,

Glory,

Sriram

2024

Communications in Computer and Information Science

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Development of lightweight intrusion model in Industrial Internet of Things using deep learning technique

Sinha,

Thakur,

Gupta

et al. 2024

Discov Appl Sci

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Nowadays, the IDS is being used in conjunction with the IIoT system to reduce the security risk, but on the other hand, the false rate of the IDS is very high. Therefore, in this work, a pre-training method, making use of both a deep neural network and a deep auto-encoder, has been proposed for the quick prediction of assaults with increased accuracy and a reduced false rate. The replicas were expanded using hyperparameter optimization (HPO) techniques. The proposed model delivers an alternative to deep learning construction replicas through an HPO procedure incorporating the Archimedes optimization algorithm. This optimization technique can be used to determine the hyperparameter value and the ideal categorical hyperparameter combination for improved detection performance. The DS2OS dataset is used alongside numerous other indicators to evaluate the efficacy of the developed model. The various existing techniques of assault detection have also been considered to show the effectiveness of the proposed model. Through the comparative evaluation of the outcomes, it is shown that the developed model provides better performance than the other existing models. Eventually, it is discovered that the suggested security paradigm is successful in fending off a variety of internal and external threats.

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An end-to-end learning approach for enhancing intrusion detection in Industrial-Internet of Things

Cited by 7 publications

References 17 publications

An Approach to Deepfake Video Detection Based on ACO-PSO Features and Deep Learning

An Approach to Deepfake Video Detection Based on ACO-PSO Features and Deep Learning

Enhanced Deep Learning for IIoT Threat Intelligence: Revealing Advanced Persistent Threat Attack Patterns

Development of lightweight intrusion model in Industrial Internet of Things using deep learning technique

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