Mechanism Of Attack Research Articles

Degradation of steel 20 during cathodic polarization and hydrogen embrittlement of steam boiler screen pipes during operation

The paper examines the differences in the properties of new pipes, pipes artificially saturated with hydrogen and boiler screen pipes after long-term operation. The embrittlement process was studied by observing changes in the microstructural structure, mechanical properties and identifying the mechanism of hydrogen attack on the objects under study. It was discovered that there was no decarbonization characteristic of natural saturation with hydrogen and a decrease in the ultimate tensile strength of artificially saturated pipes. The nature of cracking was also different — transcrystalline with artificial saturation and intercrystalline with natural saturation. At the same time, the nature of the fractures — with areas of hydrogen fragility — and the average concentrations of accumulated hydrogen in pipes artificially hydrogenated and hydrogenated during operation were identical. Significant differences in the physical and mechanical properties of all three types of pipes have been recorded, which makes it impossible to transfer the results obtained with model samples artificially saturated with hydrogen to actually operated objects.

Hardware Security Breaches: Alarming Exploits Beyond Tampering

Side Channel Attacks (SCAs) and Fault Injection Attacks have emerged as significant threats to the security of electronic devices. This paper explores these hardware attack vectors in the context of recent incidents [11] involving pagers and walkie-talkies. We shall deep dive into the possible mechanisms of these attacks, how they can be combined for increased efficacy, and their implications for device security. This paper discusses only the possible ways [1] the adversaries could have exploited and may include other attack vectors such as supply chain attack and few others. The aim of this paper as we discuss the potential attack mechanisms is only to focus on mitigations and awareness of the reader to perform advanced security testing.

Mechanism and Performance Control Methods of Sulfate Attack on Concrete: A Review

For concrete structures in marine or groundwater environments, sulfate attack is a major factor contributing to the degradation of concrete performance. This paper analyzes the existing literature on the chemical reactions and physical crystallization effects of sulfate attack on cement-based materials, summarizing the degradation mechanisms of corroded concrete. Experiments have been conducted to study the performance evolution of concrete under sulfate attack, considering both external environmental factors and internal factors of the cement-based materials. External environmental factors, such as the temperature, humidity, concentration, and type of sulfate solutions, wet-dry cycles, freeze-thaw cycles, chloride coupling effects, and stray currents significantly impact sulfate attack on concrete. Internal factors, including internal sources of corrosion, the chemical composition of the cement, water-cement ratio, and the content of C-S-H gel and Ca(OH)2, influence the density and sulfate resistance of the cement-based materials. Additionally, five typical methods for enhancing the sulfate resistance of concrete are summarized. Finally, the paper identifies current challenges in the study of corroded concrete and proposes directions for future research.

Study on the influence of early frost attack on the performance of cement mortar

The loss of strength and durability caused by early frost attack is the macro performance of internal microstructure deterioration. Understanding the influence mechanism of early frost attack at the micro level is necessary to avoid or minimize the early frost attack to macro performance. In this study, the influence of early frost attack on the strength, water sorptivity, chloride permeability, and microstructure of cement mortar was investigated. The fresh mortars were pre-cured for 10, 12, 14, and 16 h and then frozen at −10 °C for 7 days to simulate an early frost attack. After freezing, the standard curing (20 °C) was carried out until the testing. It is found that after standard curing of 28 days, the strength of early-frozen mortar can reach more than 95 % of the 28-day strength of unfrozen mortar. Interestingly, the sorptivity coefficient and total charge passed of early-frozen mortar are much higher than the level of unfrozen mortar, and the loss of resistance to water penetration and chloride ion penetration exceeds 20 %, indicating that early frost attack causes more severe damage to the durability than to the strength of mortar. Mercury intrusion porosimetry (MIP) results show that early frost attack can increase the pore volume of >50 nm and coarsen the pores within 10–50 nm. Correspondingly, key pore parameters (critical pore diameter, threshold pore diameter, and tortuosity) that affect the durability of mortar are also negatively affected. Energy dispersive spectroscopy (EDS) analysis also shows that after suffering early frost attack, the interfacial transition zone (ITZ) thickness of mortar is increased from 4 to 10 μm to 8–13 μm, and the probability of high Ca/Si ratios (4–10) in the ITZ is also increased. Finally, it is concluded that the coupling effect of pore structure coarsening and ITZ degradation leads to more severe durability loss.

T6SS in plant pathogens: unique mechanisms in complex hosts.

Type VI secretion systems (T6SSs) are complex molecular machines that allow bacteria to deliver toxic effector proteins to neighboring bacterial and eukaryotic cells. Although initial work focused on the T6SS as a virulence mechanism of human pathogens, the field shifted to examine the use of T6SSs for interbacterial competition in various environments, including in the plant rhizosphere. Genes encoding the T6SS are estimated to be found in a quarter of all Gram-negative bacteria and are especially highly represented in Proteobacteria, a group which includes the most important bacterial phytopathogens. Many of these pathogens encode multiple distinct T6SS gene clusters which can include the core components of the apparatus as well as effector proteins. The T6SS is deployed by pathogens at multiple points as they colonize their hosts and establish an infection. In this review, we describe what is known about the use of T6SS by phytopathogens against plant hosts and non-plant organisms, keeping in mind that the structure of plants requires unique mechanisms of attack that are distinct from the mechanisms used for interbacterial interactions and against animal hosts. While the interactions of specific effectors (such as phospholipases, endonucleases, peptidases, and amidases) with targets have been well described in the context of interbacterial competition and in some eukaryotic interactions, this review highlights the need for future studies to assess the activity of phytobacterial T6SS effectors against plant cells.

Quantifying the effects of attacks on health facilities on health service use in Northwest Syria: a case time series study from 2017 to 2019

BackgroundThroughout the Syrian conflict, the Syrian government has intentionally attacked health facilities, violating International Humanitarian Law. Previous studies have qualitatively described health system disruptions following attacks on healthcare or established...

Whole-genome sequencing and identification of antimicrobial peptide coding genes in parsley (Petroselinum crispum), an important culinary and medicinal Apiaceae species.

Parsley is a commonly cultivated Apiaceae species of culinary and medicinal importance. Parsley has several recognized health benefits and the species has been utilized in traditional medicine since ancient times. Although parsley is among the most commonly cultivated members of Apiaceae, no systematic genomic research has been conducted on parsley. In the present work, parsley genome was sequenced using the long-read HiFi (high fidelity) sequencing technology and a draft contig assembly of 1.57 Gb that represents 80.9% of the estimated genome size was produced. The assembly was highly repeat-rich with a repetitive DNA content of 81%. The assembly was phased into a primary and alternate assembly in order to minimize redundant contigs. Scaffolds were constructed with the primary assembly contigs, which were used for the identification of AMP (antimicrobial peptide) genes. Characteristic AMP domains and 3D structures were used to detect and verify antimicrobial peptides. As a result, 23 genes (PcAMP1-23) representing defensin, snakin, thionin, lipid transfer protein and vicilin-like AMP classes were identified. Bioinformatic analyses for the characterization of peptide physicochemical properties indicated that parsley AMPs are extracellular peptides, therefore, plausibly exert their antimicrobial effects through the most commonly described AMP action mechanism of membrane attack. AMPs are attracting increasing attention since they display their fast antimicrobial effects in small doses on both plant and animal pathogens with a significantly reduced risk of resistance development. Therefore, identification and characterization of AMPs is important for their incorporation into plant disease management protocols as well as medicinal research for the treatment of multi-drug resistant infections.

Adversarial Missingness Attacks on Causal Structure Learning

Causality-informed machine learning has been proposed as an avenue for achieving many of the goals of modern machine learning, from ensuring generalization under domain shifts to attaining fairness, robustness, and interpretability. A key component of causal machine learning is the inference of causal structures from observational data; in practice, this data may be incompletely observed. Prior work has demonstrated that adversarial perturbations of completely observed training data may be used to force the learning of inaccurate causal structural models (SCMs). However, when the data can be audited for correctness (e.g., it is cryptographically signed by its source), this adversarial mechanism is invalidated. This work introduces a novel attack methodology wherein the adversary deceptively omits a portion of the true training data to bias the learned causal structures in a desired manner (under strong signed sample input validation, this behavior seems to be the only strategy available to the adversary). Under this model, theoretically sound attack mechanisms are derived for the case of arbitrary SCMs, and a sample-efficient learning-based heuristic is given. Experimental validation of these approaches on real and synthetic data sets, across a range of SCMs from the family of additive noise models (linear Gaussian, linear non-Gaussian, and non-linear Gaussian) demonstrates the effectiveness of adversarial missingness attacks at deceiving popular causal structure learning algorithms.

Mechanistic insights into two-stage expansion of concrete under external sulfate attack

Despite extensive academic research on durability issues caused by sulfate attack, deterioration reasons and expansion mechanisms of concrete remain controversial, with descriptions of expansion development still focusing on phenomenological observations. The present study analyzes the macro-micro property changes of concrete and establishes a time-varying expansion prediction model considering environmental and material parameters. The micropore evolution in different pore categories is investigated, and the primary type and precipitation location of expansive products, as well as the expansion mechanism of concrete, are clarified. The results indicate the possibility of thaumasite sulfate attack (TSA) in concrete at low temperatures. The expansion development of concrete under sulfate attack can be divided into two stages: the expansion latency stage explained by crystallization pressure theory, and the significant expansion stage associated with volume increase theory. The expansion level at the inflection point of the expansion rate was solely related to the intrinsic micropore properties of concrete.

Molecular-level regulating intersystem crossing of polyphenols: Engineering high-efficiency phytochemical photosensitizer for MRSA elimination

Natural polyphenols-mediated green photosterilization is promising but remains challenging in practical applications due to their unsatisfactory performance with insufficient intrinsic energy level and ROS generation. In this work, the molecular-level engineering strategy of natural polyphenols (quercetin, QC) by p-Methoxybezaldehyde (MB)-induced nucleophilic substitution is constructed for enhanced photodynamic MRSA therapy. Both the experimental and theoretical results disclose that the unprecedented H-aggregates of QC-MB supramolecular realized accelerated ISC with minimizing the energy level difference (ΔEst), achieving enhanced photodynamic performance with 2.2-fold enhancement on 1O2 quantum yield (75 %) compared to free quercetin (33 %). The well-designed photosensitizer realizes efficient MRSA elimination: with enhanced killing efficacy from 30.44 % to 99.95 % in vitro and effective eradication of mature biofilms. Mechanism study and gene transcription analysis reveal that the combined therapy of optical stimuli with QC-MB achieves the dual mechanism of ROS attack and virulence regulation against MRSA infection during the whole infection process. Moreover, the excellent biocompatibility and anti-infectious in vivo demonstrate the engineered QC-MB as a promising strategy for MRSA therapy.

Defense Management Mechanism for Primary User Emulation Attack Based on Evolutionary Game in Energy Harvesting Cognitive Industrial Internet of Things

Defense Management Mechanism for Primary User Emulation Attack Based on Evolutionary Game in Energy Harvesting Cognitive Industrial Internet of Things

Study on the optimal conductivity titration parameters for SO42- in cement-based materials

Accurately obtaining the SO42- content in concrete is a prerequisite for revealing mechanism of sulfate attack, establishing model of sulfate attack, proposing durability design methods. The feasibility of testing SO42- in cement-based materials by conductivity titration was investigated by the author in previous research. Therefore, this study further investigated the optimal conductivity titration parameters and standard for determining the endpoint of titration curve by establishing a conductivity titration kinetic model (CTKM). Based on the proposed CTKM, optimal range of titration parameters such as the volume of measured solution, solution concentration standard titrant and titration rate were obtained, and the effects of other ions on optimal range of titration parameters were also discussed. The results show that among the six stages on the titration curve of SO42- (undersaturation stage, crystal nucleation stage, crystal growth stage, quasi reaction end stage, reaction end stage and infinite titration stage), crystal growth stage and reaction end stage are essential. The titration curves calculated by the CTKM are in good agreement with the measured curves. The included angle between the left and right tangents of a point on the normalized conductivity titration curve can be a good criterion for determining the titration end point. When the included angle exceeds 10°, the titration end point is easily determined. The optimal range of titration parameters are confined by the minimum measured solution volume to submerge the conductivity electrode (about 5 mL), the maximum measured solution volume to be stirred evenly (about 10 mL), and the appropriate added volume of standard titrant to ensure titration accuracy (between 1 and 1.5 mL), and the included angle to be easily determined (greater than 10°). The influence of H+, OH–, Cl-, and Na+ on SO42- titration gradually weakens. In composite solution containing 0.01 mol/L SO42-, when the concentration of H+ exceeds 0.1 mol/L, or the concentration of OH– exceeds 0.2 mol/L, directly titrating SO42- is difficult and a deionization process is required.

A method for recovering adversarial samples with both adversarial attack forensics and recognition accuracy

Adversarial samples deceive machine learning models through small but elaborate modifications that lead to erroneous outputs. The severity of the adversarial sample problem has come to the forefront with the widespread use of machine learning in areas such as security systems, autonomous driving, speech recognition, finance, and medical diagnostics. Malicious attackers can use adversarial samples to circumvent security detection systems, interfere with autonomous driving perception, mislead speech recognition, defraud financial systems, and even cause medical diagnosis errors. The emergence of adversarial samples exposes the vulnerability of existing models and poses challenges for information tracing and forensics after the incident. The main goal of current adversarial sample restoration methods is to improve model robustness. Traditional approaches focus only on improving the model’s classification accuracy, ignoring the importance of adversarial information, which is crucial for understanding the attack mechanism and strengthening future defenses. To address this issue, we propose an adversarial sample restoration method based on the similarity between clean and adversarial sample blocks to balance the needs of adversarial forensics and recognition accuracy. We implement the Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), and Momentum Iterative Attack (MIA) attacks on MNIST, F-MNIST, and EMNIST datasets and perform experimental validation. The results demonstrate that our restoration method significantly enhances the model’s classification accuracy across various datasets and attack scenarios. Comparative analysis shows that the restored samples maintain a high similarity with the original adversarial samples, proving the method’s effectiveness. In addition, we performed performance tests on pre- and post-recovery samples. Taking the MNIST dataset as an example, we observed that the model performance metrics, such as MAPE, MAE, RMSE, and VAPE, of the restored samples improved by 88%, 88%, 65%, and 82%, respectively, after using the FGSM attack. This indicates that our restoration method successfully preserves the information of the generation mechanism of the adversarial samples and improves the model’s performance. This approach balances forensic capability and prediction accuracy, demonstrates a new direction in adversarial sample research, and substantially impacts security defense in practical applications.

Research on Privacy Issues in Smart Metering System: An Improved TCN-Based NILM Attack Method and Practical DRL-Based Rechargeable Battery Assisted Privacy Preserving Method

Smart meters, as a key component of Advanced Metering Infrastructure (AMI), collect fine-grained electricity consumption data for demand response in smart grids. While this data improves the grid’s accuracy, it also poses significant threats to users’ privacy. In this paper, we study the privacy issue in smart metering systems from both attacker and defender perspectives. First, we propose an improved Temporal Convolutional Network (TCN) based Non-Intrusive Load Monitoring (NILM) attack method, which infers electrical appliance usage from public load curves, addressing the gradient vanishing, gradient exploding, and other problems while improving attack accuracy. Second, we develop a rechargeable battery-assisted energy management system to hide load characteristics of electrical appliances by adding physical noise, thus resisting NILM attacks. To address the privacy-cost trade-off optimization problem, we propose a Practical Deep Reinforcement Learning-based Rechargeable Battery assist Privacy Preserving Method (PRoP) that learns optimal battery charging/discharging policies. We design a novel privacy measurement method and constraints to ensure the feasibility of system deployment and prove PRoP’s effectiveness in resisting NILM attacks. Comprehensive evaluations demonstrate that our improved TCN-based NILM method achieves an attack success rate of over 80% on various electrical appliances, improving attack performance (MAE, RMSE) by 20% compared to existing methods while reducing model training time. Moreover, our proposed PRoP achieves a better trade-off between privacy protection and electricity cost than existing battery-assisted methods, reducing costs by 5% and the attack success ratio to 36%, while increasing the MAE and RMAE obtained by NILM by 3 times. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Smart grid, which can support bidirectional information transmission, has a series of advantages, such as high efficiency and high stability. However, it also brings a significant threat to users’ electricity privacy. Although encryption-based privacy protection methods have been deployed on terminal devices of smart grids to prevent privacy leaks, this method can often only defend against intrusive attacks and has little effect on non-intrusive attacks. To this end, this paper studies the privacy issues caused by non-intrusive attacks. Specifically, to better study the protection method, we first investigate the attack mechanism and design an improved TCN-based Non-Intrusive Load Monitoring method. Then, we propose a Practical Reinforcement learning-based rechargeable battery-assisted Privacy preserving method (PRoP) to defend against this attack physically. The most practical contribution of this paper is that, compared with existing battery-assisted privacy protection methods, we do not blindly pursue algorithm performance but fully consider the practical factors of deployment, such as limiting battery capacity and constraining battery charging and discharging behavior. This can guide practitioners to better apply this technology in practice.

A method of using modern endpoint detection and response (EDR) systems to protect against complex attacks

The subject of the research in this article is the architecture of Endpoint Detection and Response and the EDR agent as their base parts in terms of mechanisms for detecting and countering complex attacks on information and communication systems (ICS). The aim of the work is to develop of method for improving the efficiency of using Endpoint Detection and Response (EDR) to reduce the risks of compromising ICS information, industrial, and infrastructure objects by effectively redistributing and utilizing the available EDR mechanisms, the cybersecurity team, and other resources available for implementing security measures in an enterprise, institution, or organization. The article addresses the following tasks: reviewing and analyzing existing EDR systems, analyzing the architecture of EDR solutions and EDR agents, the features of their use, the logic behind the construction of methods and mechanisms for detecting threats to the system from malicious actors and malicious code. The task of providing recommendations for the organization of ICS is also separately addressed in terms of the need to protect the entire ICS and its individual elements, as well as in terms of the available resources (the cybersecurity team, their qualifications and level of awareness of the architecture of EDR solutions) and means (available EDR system elements) for organizing protection. The following methods are used: modeling attack mechanisms, modeling attacker behavior. The following results were obtained: general and specific recommendations were formulated for optimizing the operation of EDR systems and ensuring the effective use of EDR system elements in the information and communication networks of enterprises, organizations, and institutions of various types and orientations depending on the available resources and the information requiring protection. Conclusions: The identified recommendations for the application of EDR mechanisms for protecting information systems and networks allow optimizing the costs of creating a protection infrastructure and implementing security measures, taking into account the characteristics of the available tools and the training and awareness of the cybersecurity team both in terms of response time to threats and the complexity and cost of performing protection tasks.

Highly efficient degradation of tetracycline in groundwater by nanoscale zero-valent iron-copper bimetallic biochar: active [H] attack and direct electron transfer mechanism.

Development of carbon materials with high activity was important for rapid degradation of emerging pollutants. In this paper, a novel nanoscale zero-valent iron-copper bimetallic biochar (nZVIC-BC) was synthesized by carbothermal reduction of waste pine wood and copper-iron layered double hydroxides (LDHs). Characterization and analysis of its structural, elemental, crystalline, and compositional aspects using XRD, FT-IR, SEM, and TEM confirmed the successful preparation of nZVIC-BC and the high dispersion of Fe-Cu nanoparticles in an ordered carbon matrix. The experimental results showed that the catalytic activity of nZVIC-BC (Kobs of 0.0219 min-1) in the degradation of tetracycline (TC) in anoxic water environment was much higher than that of Fe-BC and Cu-BC; the effective degradation rate reached 85%. It was worth noting that the negative effects of Ca2+, Mg2+, and H2PO4- on TC degradation at ionic strengths greater than 15 mg/L were due to competition for active sites. Good stability and reusability were demonstrated in five consecutive cycle tests for low leaching of iron and copper. Combined with free radical quenching experiments and XPS analyses, the degradation of TC under air conditions was only 62%, with hydroxyl radicals (·OH) playing a dominant role. The synergistic interaction between Fe2+/Fe3+ and Cu0/Cu+/Cu2+ under nitrogen atmosphere enhances the redox cycling process; π-π adsorption, electron transfer processes, and active [H] were crucial for the degradation of TC; and possible degradation pathways of TC were deduced by LC-MS, which identified seven major aromatic degradation by-products. This study will provide new ideas and materials for the treatment of TC.

Federated Learning: Attacks and Defenses, Rewards, Energy Efficiency: Past, Present and Future

Federated Learning (FL) was first introduced as an idea by Google in 2016, in which multiple devices jointly train a machine learning model without sharing their data under the supervision of a central server. This offers big opportunities in critical areas like healthcare, industry, and finance, where sharing information with other organizations’ devices is completely prohibited. The combination of Federated Learning with Blockchain technology has led to the so-called Blockchain Federated learning (B.F.L.) which operates in a distributed manner and offers enhanced trust, improved security and privacy, improved traceability and immutability and at the same time enables dataset monetization through tokenization. Unfortunately, vulnerabilities of the blockchain-based solutions have been identified while the implementation of blockchain introduces significant energy consumption issues. There are many solutions that also offer personalized ideas and uses. In the field of security, solutions such as security against model-poisoning backdoor assaults with poles and modified algorithms are proposed. Defense systems that identify hostile devices, Against Phishing and other social engineering attack mechanisms that could threaten current security systems after careful comparison of mutual systems. In a federated learning system built on blockchain, the design of reward mechanisms plays a crucial role in incentivizing active participation. We can use tokens for rewards or other cryptocurrency methods for rewards to a federated learning system. Smart Contracts combined with proof of stake with performance-based rewards or (and) value of data contribution. Some of them use games or game theory-inspired mechanisms with unlimited uses even in other applications like games. All of the above is useless if the energy consumption exceeds the cost of implementing a system. Thus, all of the above is combined with algorithms that make simple or more complex hardware and software adjustments. Heterogeneous data fusion methods, energy consumption models, bandwidth, and controls transmission power try to solve the optimization problems to reduce energy consumption, including communication and compute energy. New technologies such as quantum computing with its advantages such as speed and the ability to solve problems that classical computers cannot solve, their multidimensional nature, analyze large data sets more efficiently than classical artificial intelligence counterparts and the later maturity of a technology that is now expensive will provide solutions in areas such as cryptography, security and why not in energy autonomy. The human brain and an emerging technology can provide solutions to all of the above solutions due to the brain's decentralized nature, built-in reward mechanism, negligible energy use, and really high processing power In this paper we attempt to survey the currently identified threats, attacks and defenses, the rewards and the energy efficiency issues of BFL in order to guide the researchers and the designers of FL based solution to adopt the most appropriate of each application approach.

Security-Communication-Computation Tradeoff of Split Decisions for Edge Intelligence

Security and privacy are of paramount importance to data-driven edge intelligence services. By offloading computation-intensive model portions to the edge server, split inference can empower low-latency and energy-efficient model inference at resource-constrained devices. The split decision is critical to the communication and computation performances of split inference. However, its impact on security issues has yet to be studied. This article first investigates the security-communicationcomputation tradeoff of split decisions in the inference phase. With the emphasis on security concerns, we summarize the threat models of split inference and illustrate two passive attack mechanisms for recovering the input data and private labels. Case studies are shown to validate the security-communicationcomputation tradeoff and investigate the selection of optimal candidate split points according to the specific device capabilities and service requirements.

Evaluation of the active compound and antibacterial activity of a Salvia rosmarinus extract against pathogenic bacteria

When bacteria attach to the human body, they have many defense mechanisms. These mechanisms pose a global health risk. Medicinal plants can be used to control such bacterial attack mechanisms. The aim of this study was to assess the antimicrobial potential of an aqueous extract of Salvia rosmarinus and other plant extracts against some clinical bacterial isolates. The antimicrobial activity against Gram-positive and Gram-negative bacteria (S. aureus, E. coli, P. aeruginosa, Proteus spp., and Klebsiella spp.) was determined by using the agar well diffusion method. When compared to an alcoholic extract, the aqueous extract of Salvia rosmarinus inhibited the studied bacterial isolates with greater efficiency.

Important-Data-Based DoS Attack Mechanism and Resilient H∞ Filter Design for Networked T-S Fuzzy Systems.

This article is concerned with the security problems for networked Takagi-Sugeno (T-S) fuzzy systems with asynchronous premise constraints. The primary objective of this article is twofold. First, a novel important-data-based (IDB) denial-of-service (DoS) attack mechanism is proposed from the perspective of the adversary for the first time to reinforce the destructive effect of the DoS attacks. Different from most existing DoS attack models, the proposed attack mechanism can utilize the information of packets, evaluate the importance degree of packets, and only attack the most "important" ones. As such, a larger system performance degradation can be expected. Second, corresponding to the proposed IDB DoS mechanism, a resilient H∞ fuzzy filter is designed from the defender's point of view to alleviate the negative effect of the attack. Furthermore, since the defender does not know the attack parameter, an algorithm is designed to estimate it. In a word, a unified attack-defense framework is developed in this article for networked T-S fuzzy systems with asynchronous premise constraints. With the help of the Lyapunov functional method, sufficient conditions are successfully established to compute the desired filtering gains and ensure the H∞ performance of the filtering error system. Finally, two examples are exploited to demonstrate the destructiveness of the proposed IDB DoS attack and the usefulness of the developed resilient H∞ filter.