Quantized Sampled-Data Control Tactic for T-S Fuzzy NCS Under Stochastic Cyber-Attacks and Its Application to Truck-Trailer System

Cai, Xiao; Shi, Kaibo; She, Kun; Zhong, Shouming; Tang, Yiqian

doi:10.1109/tvt.2022.3169349

Cited by 54 publications

(17 citation statements)

References 38 publications

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“…Lyapunov-Krasovskii functions (LKFs) and the secondorder weight method (SOWM) are used to estimate the error generated by T-S fuzzy networked control systems under DoS attack [41]. Reciprocally convex matrix inequality (RCMI), proper integral inequalities, and the linear convex combination method (LCCM) are used to analyze the T-S fuzzy networked control systems under stochastic cyberattacks (SCAs) [42].…”

Section: B Previous Workmentioning

confidence: 99%

MCAD: A Machine Learning Based Cyberattacks Detector in Software-Defined Networking (SDN) for Healthcare Systems

Halman

Alenazi

2023

IEEE Access

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The healthcare sector deals with sensitive and significant data that must be protected against illegitimate users. Software-defined networks (SDNs) are widely used in healthcare systems to ensure efficient resource utilization, security, optimal network control, and management. Despite such advantages, SDNs suffer from a major issue posed by a wide range of cyberattacks, due to the sensitivity of patients' data. These attacks diminish the overall network performance, and can cause a network failure that might threaten human lives. Therefore, the main goal of our work is to propose a machine learning-based cyberattack detector (MCAD) for healthcare systems, by adapting a layer three (L3) learning switch application to collect normal and abnormal traffic, and then deploy MCAD on the Ryu controller. Our findings are beneficial for enhancing the security of healthcare applications by mitigating the impact of cyberattacks. This work covers the testing of MCAD using a wide spectrum of both ML algorithms and attacks, and provides a performance comparison for every pair of ML algorithms/attacks to illustrate the strengths and weaknesses of different algorithms against a specific attack. The MCAD shows impressive performance, achieving an F1-score of 0.9998 and of 0.9882 on normal and attack classes, respectively, which implies a high level of reliability. MCAD also achieved 5,709,692 samples per second on throughput, which reflects a high-performance real-time system with respect to complexity. Additionally, it showed a positive impact on the network KPIs by increasing the throughput by 609%, and decreasing delay and jitter by 77% and 23%, respectively, compared to attack results.

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Section: B Previous Workmentioning

confidence: 99%

MCAD: A Machine Learning Based Cyberattacks Detector in Software-Defined Networking (SDN) for Healthcare Systems

Halman

Alenazi

2023

IEEE Access

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“…CDR works by following the file format specification and sanitizing/ disarming or removing all attack vectors found in the file [3] and increasing user security. An attack vector, or threat vector, is a way for attackers to enter a network or cause a denial-of-service (DoS) [18], [19]. Common attack vectors include social engineering attacks [20], credential theft [21], vulnerability exploits [22], and insufficient protection against insider threats [23].…”

Section: Introductionmentioning

confidence: 99%

Content Disarm and Reconstruction of PDF Files

Dubin

2023

IEEE Access

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Content Disarm and Reconstruction (CDR) is a zero-trust file methodology that proactively extracts threat attack vectors from documents and media files. While extensive literature on CDR emphasizes its importance, a detailed discussion of how the CDR process works, its effectiveness, and its drawbacks is not presented. Therefore, this paper presents PdfCDR, the first PDF CDR system in which the validation, the prevention rate, and the received visual similarity effect of disarming and reconstruction are presented and measured. Furthermore, PdfCDR suggests for the first time a novel method dealing with new emerging exploits by automatically converting detection rules to disarm and reconstruction rules. As a result, PdfCDR can prevent evasive attacks without any software upgrades and utilize the cyber security community knowledge to prevent cyber attacks as soon as they are advertised. The effectiveness of the novel PdfCDR against well-known PDF datasets shows that it disarmed not only the malicious components, but the reconstructed file is also usable and functional. However, since CDR relies on understanding the file format, any CDR solution should handle each supported file type separately due to the vast difference in each file format. Hence, this paper focuses on the Portable Document Format (PDF) file type that attackers commonly exploit. The results indicate that PdfCDR successfully CDR 90% of the malicious files while the remaining 10% were encrypted or had abnormal structures compared to the standard and were quarantined.

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“…To the best of our knowledge, quantization as a necessary step in networked control systems (NCSs), has been researched extensively since it involves the output acted on the controlled system and helps to accommodate limited transmission capacity 29 . Just to mention a few, the article 30 introduced quantized sampled‐data control tactic to T‐S fuzzy NCSs with stochastic cyber‐attacks. The problem of finite‐time quantized stabilization for discrete singular jump systems was presented in Reference 31 .…”

Section: Introductionmentioning

confidence: 99%

Reliable quantized sampled‐data LFC for semi‐Markov jump interconnected multi‐area power systems: Dealing with incomplete TRs

Li,

Cheng

et al. 2023

Intl J Robust & Nonlinear

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This article aims to address the reliable quantized sampled‐data load frequency control (LFC) synthesis problem for semi‐Markov jump interconnected multi‐area power systems (IMAPSs) suffering from incomplete transition rates (TRs) and actuator failures. Primarily, the semi‐Markov process configurated with incomplete TRs is utilized to model the structural and coefficient switchings of IMAPSs, which enables a more precise representation of reality and a wider range of application. Subsequently, in order to reduce the control cost and facilitate data processing, an aperiodic quantization sampling mechanism is introduced. Furthermore, a mode‐dependent and comprehensive actuator faulty model is scheduled to portray various stochastically occurring actuator failures, which is more in line with practical application. Then, by fully utilizing the state information of sampled intervals, a two‐sided looped functional with some matrices that are not required to be strictly positive is constructed to enhance the flexibility. In what follows, on the basis of stochastic analysis technique and looped functional, sufficient conditions with two scenarios are established in the form of linear matrix inequalities (LMIs) to guarantee the stochastic stability with an performance of the resultant semi‐Markov jump IMAPSs. Finally, the effectiveness of the proposed control synthesis methodology is validated through a numerical simulation.

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Quantized Sampled-Data Control Tactic for T-S Fuzzy NCS Under Stochastic Cyber-Attacks and Its Application to Truck-Trailer System

Cited by 54 publications

References 38 publications

MCAD: A Machine Learning Based Cyberattacks Detector in Software-Defined Networking (SDN) for Healthcare Systems

MCAD: A Machine Learning Based Cyberattacks Detector in Software-Defined Networking (SDN) for Healthcare Systems

Content Disarm and Reconstruction of PDF Files

Reliable quantized sampled‐data LFC for semi‐Markov jump interconnected multi‐area power systems: Dealing with incomplete TRs

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