Securing Smart Manufacturing by Integrating Anomaly Detection With Zero-Knowledge Proofs

Salam, Abdu; Abrar, Mohammad; Amin, Farhan; Ullah, Faizan; Khan, Izaz Ahmad; Alkhamees, Bader Fahad; AlSalman, Hussain

doi:10.1109/access.2024.3373697

IEEE Access

2024

DOI: 10.1109/access.2024.3373697

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Securing Smart Manufacturing by Integrating Anomaly Detection With Zero-Knowledge Proofs

Abdu Salam,

Mohammad Abrar,

Farhan Amin

et al.

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

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Cited by 4 publications

References 33 publications

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The role of energy management technologies for cyber resilient smart homes in sustainable urban development

Um-e-Habiba,

Ahmed,

Alqahtani

et al. 2024

Energy Strategy Reviews

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The role of energy management technologies for cyber resilient smart homes in sustainable urban development

Um-e-Habiba,

Ahmed,

Alqahtani

et al. 2024

Energy Strategy Reviews

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Deep Learning in Cybersecurity: A Hybrid BERT–LSTM Network for SQL Injection Attack Detection

Liu,

Dai

2024

IET Information Security

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In the past decade, cybersecurity has become increasingly significant, driven largely by the increase in cybersecurity threats. Among these threats, SQL injection attacks stand out as a particularly common method of cyber attack. Traditional methods for detecting these attacks mainly rely on manually defined features, making these detection outcomes highly dependent on the precision of feature extraction. Unfortunately, these approaches struggle to adapt to the increasingly sophisticated nature of these attack techniques, thereby necessitating the development of more robust detection strategies. This paper presents a novel deep learning framework that integrates Bidirectional Encoder Representations from Transformers (BERT) and Long Short-Term Memory (LSTM) networks, enhancing the detection of SQL injection attacks. Leveraging the advanced contextual encoding capabilities of BERT and the sequential data processing ability of LSTM networks, the proposed model dynamically extracts word and sentence-level features, subsequently generating embedding vectors that effectively identify malicious SQL query patterns. Experimental results indicate that our method achieves accuracy, precision, recall, and F1 scores of 0.973, 0.963, 0.962, and 0.958, respectively, while ensuring high computational efficiency.

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A Comprehensive Investigation of Anomaly Detection Methods in Deep Learning and Machine Learning: 2019–2023

Kumari,

Prabha,

Karim

et al. 2024

IET Information Security

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Almost 85% of companies polled said they were looking into anomaly detection (AD) technologies for their industrial image anomalies. The present problem concerns detecting anomalies often occupied by redundant data. It can be either in images or in videos. Finding a correct pattern is a challenging task. AD is crucial for various applications, including network security, fraud detection, predictive maintenance, fault diagnosis, and industrial and healthcare monitoring. Many researchers have proposed numerous methods and worked in the area of AD. Multiple anomalies and considerable intraclass variation make industrial datasets tough. Further, research is needed to create robust, efficient techniques that generalize datasets and detect anomalies in complex industrial images. The outcome of this study focuses on various AD methods from 2019 to 2023. These techniques are categorized further into machine learning (ML), deep learning (DL), and federated learning (FL). It explores AD approaches, datasets, technologies, complexities, and obstacles, emphasizing the requirement for effective detection across domains. It explores the results achieved in various ML, DL, and FL AD methods, which helps researchers explore these techniques further. Future research directions include improving model performance, leveraging multiple validation techniques, optimizing resource utilization, generating high‐quality datasets, and focusing on real‐world applications. The paper addresses the changing environment of AD methods and emphasizes the importance of continuing research and innovation. Each ML and DL AD model has strengths and shortcomings, concentrating on accuracy and performance while applying quality parameters for evaluation. FL provides a collaborative way to improve AD using distributed data sources and data privacy.

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Securing Smart Manufacturing by Integrating Anomaly Detection With Zero-Knowledge Proofs

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 4 publications

References 33 publications

The role of energy management technologies for cyber resilient smart homes in sustainable urban development

The role of energy management technologies for cyber resilient smart homes in sustainable urban development

Deep Learning in Cybersecurity: A Hybrid BERT–LSTM Network for SQL Injection Attack Detection

A Comprehensive Investigation of Anomaly Detection Methods in Deep Learning and Machine Learning: 2019–2023

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