STEWART: STacking Ensemble for White-Box AdversaRial Attacks Towards more resilient data-driven predictive maintenance

Güngör, Onat; Rosing, Tajana; Akşanlı, Barış

doi:10.1016/j.compind.2022.103660

Cited by 21 publications

(6 citation statements)

References 23 publications

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“…In the aspect of nanocomposite materials, Gradient Boosted Trees is applied [69]. Applying bagging, majority voting to fault detection in the electronics, Catboost, XGBoost in electrical [70,71], In computer science, various ensemble learning methods, like a machine learning-based intrusion detection system (ML-IDS) [72], are also working to solve problems on local explanation [73], Cyber-Attacks [72,74]. In the monitoring of environmental pollutants PM2.5, the existing studies integrated EL with the satellite high-dimensional visualization method to realize the monitoring and prediction of pollutants [75,76].…”

Section: Application In M Categorymentioning

confidence: 99%

Ensemble Learning Applications in Multiple Industries: A Review

Lin¹,

Huang²

2023

IDA

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This study proposes a systematic review of the application of Ensemble learning (EL) in multiple industries. This study aims to review prevailing application in multiple industries to guide for the future landing application. This study also proposes a research method based on Systematic Literature Review (SLR) to address EL literature and help advance our understanding of EL for future optimization. The literature is divided three categories by the National Bureau of Statistics of China (NBSC): the primary industry, the secondary industry and the tertiary industry. Among existing problems in industrial management systems, the frequently discussed are quality control, prediction, detection, efficiency and satisfaction. In addition, given the huge potential in various fields, the gap and further directions are also suggested. This study is essential to industry managers and cross-disciplinary scholars to lead a guideline to solve the issues in practical work, as it provided a panorama of application domains and current problems. This is the first review of the application of EL in multiple industries in the literature. The paper has potential values to broaden the application area of EL, and to proposed a novel research method based SLR to sort out literature.

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Section: Application In M Categorymentioning

confidence: 99%

Ensemble Learning Applications in Multiple Industries: A Review

Lin¹,

Huang²

2023

IDA

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“…Model defense is the last category of defenses which strengthen the model itself against adversarial attacks, i.e., increasing the robustness. This is the most heavily studied defense approach where there are numerous approaches such as gradient masking [36], defensive distillation [37], generative adversarial network [2], ensemble learning [18], certified defenses [42], and adversarial training [3]. Adversarial training proves to be the most effective against adversarial attacks [35].…”

Section: Related Workmentioning

confidence: 99%

“…Industrial Internet of Things (I-IoT) is a typical CPS enabling the operation, interconnection, and intelligence of industrial systems [57]. I-IoT's small-scale devices, with their limited computation and communication capabilities, make them vulnerable to potential cyber attacks [18]. An adversary can exploit these vulnerabilities to sabotage communication, prevent asset availability, and corrupt monitoring data which may have serious financial consequences [51].…”

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confidence: 99%

DODEM: DOuble DEfense Mechanism Against Adversarial Attacks Towards Secure Industrial Internet of Things Analytics

Güngör¹,

Rosing²,

Akşanlı³

2023

Preprint

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Industrial Internet of Things (I-IoT) is a collaboration of devices, sensors, and networking equipment to monitor and collect data from industrial operations. Machine learning (ML) methods use this data to make high-level decisions with minimal human intervention. Data-driven predictive maintenance (PDM) is a crucial ML-based I-IoT application to find an optimal maintenance schedule for industrial assets. The performance of these ML methods can seriously be threatened by adversarial attacks where an adversary crafts perturbed data and sends it to the ML model to deteriorate its prediction performance. The models should be able to stay robust against these attacks where robustness is measured by how much perturbation in input data affects model performance. Hence, there is a need for effective defense mechanisms that can protect these models against adversarial attacks. In this work, we propose a double defense mechanism to detect and mitigate adversarial attacks in I-IoT environments. We first detect if there is an adversarial attack on a given sample using novelty detection algorithms. Then, based on the outcome of our algorithm, marking an instance as attack or normal, we select adversarial retraining or standard training to provide a secondary defense layer. If there is an attack, adversarial retraining provides a more robust model, while we apply standard training for regular samples. Since we may not know if an attack will take place, our adaptive mechanism allows us to consider irregular changes in data. The results show that our double defense strategy is highly efficient where we can improve model robustness by up to 64.6% and 52% compared to standard and adversarial retraining, respectively.

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“…Let 0 y , 1 y , 2 y , and 3 y represent the four n-bit outputs of S-box. Let i t denote the 4-bit temporary variables, i =0, 1, 2, 3,4,5,6,7,8,9,10,12,13,14,15,16,18,19,20,21. The bit-slice implementation codes of the IIoTBC cipher S-box are shown in Algorithm2.…”

Section: Algebraic Cryptanalysismentioning

confidence: 99%

“…Sengupta et al reported the industrial IoT architecture, security issues, and types of attacks encountered in detail [7]. A variety of security protection schemes for the industrial IoT have been proposed [8][9][10][11]. Protecting data in transit is necessary, and many attacks more often target terminal devices.…”

Section: Introductionmentioning

confidence: 99%

IIoTBC: A Lightweight Block Cipher for Industrial IoT Security

2023

KSII TIIS

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The number of industrial Internet of Things (IoT) users is increasing rapidly. Lightweight block ciphers have started to be used to protect the privacy of users. Hardware-oriented security design should fully consider the use of fewer hardware devices when the function is fully realized. Thus, this paper designs a lightweight block cipher IIoTBC for industrial IoT security. IIoTBC system structure is variable and flexibly adapts to nodes with different security requirements. This paper proposes a 4×4 S-box that achieves a good balance between area overhead and cryptographic properties. In addition, this paper proposes a preprocessing method for 4×4 S-box logic gate expressions, which makes it easier to obtain better area, running time, and power data in ASIC implementation. Applying it to 14 classic lightweight block cipher S-boxes, the results show that is feasible. A series of performance tests and security evaluations were performed on the IIoTBC. As shown by experiments and data comparisons, IIoTBC is compact and secure in industrial IoT sensor nodes. Finally, IIoTBC has been implemented on a temperature state acquisition platform to simulate encrypted transmission of temperature in an industrial environment.

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STEWART: STacking Ensemble for White-Box AdversaRial Attacks Towards more resilient data-driven predictive maintenance

Cited by 21 publications

References 23 publications

Ensemble Learning Applications in Multiple Industries: A Review

Ensemble Learning Applications in Multiple Industries: A Review

DODEM: DOuble DEfense Mechanism Against Adversarial Attacks Towards Secure Industrial Internet of Things Analytics

IIoTBC: A Lightweight Block Cipher for Industrial IoT Security

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