Effective Defense Research Articles

Proposed Technique for Estimating Rainwater Storage Tank Locations Using Vulnerability Analysis of Critical Flooding Nodes

Even if a pumping station, which is a representative structural flood defense facility, operates optimally, flooding occurs at certain nodes in the drainage area. In other words, even if rainwater pumping stations and rainwater storage tank, which are structural flood defense measures, operate at maximum, it is necessary to review areas where overflow occurs. Therefore, in areas where it is impossible to protect against inland flooding using structural flood defense measures alone, new flood defense measures are required. In this study, based on previous research on rainwater pumping station operation techniques to reduce critical flooding, the Daelim 3 region was divided into 45 basic areas and vulnerability analysis techniques were applied to select the location of rainwater storage tank for effective flood defense. A possible technique is presented in this paper. As a result of the study, sub-region 1 had the highest impact index value, so installing a rainwater storage tank was calculated to be more effective in preventing floods than installing a rainwater storage tank in other sub-regions. The priority-estimation technique for flood defense facilities presented in this study can be effective in establishing plans for flood control structures other than rainwater storage tank.

The Relevance of Flash Coloration Against Avian Predation in a Morpho Butterfly: A Field Experiment in a Tropical Rainforest

ABSTRACTThe flash coloration hypothesis postulates that otherwise cryptically colored animals suddenly displaying conspicuous colors during movement confuse predators, reducing capture. Morpho helenor butterflies have contrasting colors on dorsal (iridescent blue) and ventral (brown) wing surfaces, resulting in sequential blue “flashes” during flight. We tested whether this flashing pattern reduces avian predation on M. helenor in Atlantic rainforest by changing the flashing effect in three experiments. In Experiment 1, we added a blue band to the ventral wing. In Experiment 2, we covered the dorsal wing's blue band with a brown band. Control groups in each experiment were painted such that wing color patterns remained unaltered. Survivorship was evaluated through mark‐recapture censuses and beak marks on the wings. Results show that survivorship of treated butterflies in Experiment 1 decrease markedly compared to unaltered control individuals, while survivorship of treated butterflies in Experiment 2 did not differ compared to control individuals. In Experiment 3, we detected scant predation on treated (blue band added to ventral wing) and control butterflies (brown band added to ventral wing) on the forest floor (wings closed), corroborating that flash coloration is an important protective mechanism during flight. Our field experiments provide the first evidence, to our knowledge, that flash coloration in bright blue Morpho butterflies is an effective defense mechanism against avian predators in a tropical rainforest.

Balancing act: The dynamic relationship between nutrient availability and plant defence.

Plants depend heavily on soil nutrients for growth, development and defence. Nutrient availability is crucial not only for sustaining vital biochemical processes but also for mounting effective defences against a diverse array of pathogens. Macronutrients such as nitrogen, phosphorus and potassium significantly influence plant defence mechanisms by providing essential building blocks for the synthesis of defence compounds, immune signalling and physiological responses like stomatal regulation. Micronutrients like zinc, copper and iron are essential for balancing reactive oxygen species and other reactive compounds in plant immune responses. Although substantial circumstantial evidence links nutrient availability to plant defence, the molecular mechanisms underlying this process have only recently started to be understood. This review focuses on summarizing recent advances in understanding the molecular mechanisms by which nitrogen, phosphorus and iron interact with plant defence mechanisms and explores the potential for engineering nutritional immunity in crops to enhance their resilience against pathogens.

Advanced Security Framework for 6G Networks: Integrating Deep Learning and Physical Layer Security

This paper presents an advanced framework for securing 6G communication by integrating deep learning and physical layer security (PLS). The proposed model incorporates multi-stage detection mechanisms to enhance security against various attacks on the 6G air interface. Deep neural networks and a hybrid model are employed for sequential learning to improve classification accuracy and handle complex data patterns. Additionally, spoofing, jamming, and eavesdropping attacks are simulated to refine detection mechanisms. An anomaly detection system is developed to identify unusual signal patterns indicating potential attacks. The results demonstrate that machine learning (ML) and hybrid models outperform conventional approaches, showing improvements of up to 85% in bit error rate (BER) and 24% in accuracy, especially under attack conditions. This research contributes to the advancement of secure 6G communication systems, offering details on effective defence mechanisms against physical layer attacks.

CMDN: Pre-Trained Visual Representations Boost Adversarial Robustness for UAV Tracking

Visual object tracking is widely adopted to unmanned aerial vehicle (UAV)-related applications, which demand reliable tracking precision and real-time performance. However, UAV trackers are highly susceptible to adversarial attacks, while research on developing effective adversarial defense methods for UAV tracking remains limited. To tackle these challenges, we propose CMDN, a novel pre-processing defense network that effectively purifies adversarial perturbations by reconstructing video frames. This network learns robust visual representations from video frames, guided by meaningful features from both the search region and the template. Comprehensive experiments on three benchmarks demonstrate that CMDN is capable of enhancing a UAV tracker’s adversarial robustness in both adaptive and non-adaptive attack scenarios. In addition, CMDN maintains stable defense effectiveness when transferred to heterogeneous trackers. Real-world tests on the UAV platform also validate its reliable defense effectiveness and real-time performance, with CMDN achieving 27 FPS on NVIDIA Jetson Orin 16 GB (25 W mode).

Forest tree extracts induce resistance to Pseudomonas syringae pv. tomato in Arabidopsis.

The widespread use of conventional pesticides for plant pathogen control poses significant risks to human health and the environment, and it is therefore crucial to develop environmentally friendly, human-safe alternatives to these products that offer a sustainable approach for crop protection. Here, we examined the potential of ethanolic extracts from four forest tree species for their antibacterial activity against the bacterial pathogenPseudomonas syringaepv.tomato (Pst) and their ability to trigger effective defense responses in the model plantArabidopsis thaliana. The extracts exhibited direct toxic effects against Pst and triggered the expression of defense-related genes naturally induced by oxidative stress cues or the defense elicitor salicylic acid in leaf tissue. The direct antibacterial effects of the tree extracts, together with their defense gene-inducing effectsin planta, resulted in a strong host plant-protecting effect againstPst. These findings suggest the eventual effectiveness of forest tree extracts as plant protectants against the bacterial pathogenPst. They also suggest the potential of these extracts as a sustainable, eco-friendly alternative to conventional pesticides for the management of economically important plant pathogens.

Major Insect Pests of Sweet Potatoes in Brazil and the United States, with Information on Crop Production and Regulatory Pest Management.

The sweet potato [Ipomoea batatas (L.) Lam] is considered one of the most important crops in the world as food, fodder, and raw material for starch and alcohol production. Sweet potato consumption and demand for its value-added products have increased significantly over the past two decades, leading to new cultivars, expansion in acreage, and increased demand in the United States and its export markets. Due to its health benefits, sweet potato production has multiplied over the past decade in Brazil, promoting food security and economic development in rural areas. Their adaptability and nutritional value make them a food of great importance for Brazil. As pest attacks and disease infection are the main limiting aspects that often cause yield loss and quality degradation in sweet potatoes, there is a great demand to develop effective defense strategies to maintain productivity. There is a critical need for research into non-pesticide control approaches that can provide safe, cost-effective, sustainable, and environmentally friendly pest and disease management techniques. Pests which feed on roots have trade implications worldwide. For example, sweet potato tuber shipments infested with the sweet potato weevil are generally not allowed for trade in North and South America.

Mixed Th1/Th2/Th17 Responses Induced by Plant Oil Adjuvant-Based B. bronchiseptica Vaccine in Mice, with Mechanisms Unraveled by RNA-Seq, 16S rRNA and Metabolomics.

The current Bordetella bronchiseptica (Bb) vaccine, when adjuvanted with alum, does not elicit adequate robust cellular immunity or effective antibody defense against Bb attacks. Unfortunately, antibiotic treatment generally represents an ineffective strategy due to the development of resistance against a broad range of antibiotics. The present study was designed to investigate the immune response, protective capabilities and underlying mechanisms of a plant oil-based adjuvant E515 formulated with inactivated Bb antigen as a potential vaccine candidate against Bordetella bronchiseptica. Immunization studies revealed that a combination of SO, VE and GS (E515) exhibited a good synergistic adjuvant effect. The E515 adjuvanted Bb vaccine was proven to be highly efficacious and induced a mixed Th1/Th2/Th17 immune response in mice, leading to a significant increase in Bb-specific IgG, IgG1 and IgG2a antibodies, proliferative lymphocyte responses and cytokine levels (by lymphocytes and serum) and effectively induced responses by CD4+ TE, TM cells and B cells. The E515 adjuvant significantly enhanced the immune protection provided by the Bb vaccine in a mice model, as indicated by a reduced bacterial burden in the lungs. Multi-omics sequencing analysis revealed that E515 functions as an adjuvant by modulating critical pathways, including cytokine-cytokine receptor interaction, the IL-17 signaling pathway and the chemokine signaling pathway. This modulation also included interactions with beneficial species of bacteria including Alistipes, Odoribacter and Colidextribacter, as well as energy and lipid-related metabolites, thus highlighting its role as an immunomodulatory agent. Collectively, our results demonstrate the huge potential of E515-Bb vaccine candidates, thus highlighting the vegetable oil original adjuvant E515 as a promising agent for the development of new veterinary vaccines.

Genotype-Dependent Variations in Oxidative Stress Markers and Bioactive Proteins in Hereford Bulls: Associations with DGAT1, LEP, and SCD1 Genes.

The objective of this study is to assess the influence of genetic polymorphisms in DGAT1, LEP, and SCD1 on the oxidative stress biomarkers and bioactive protein levels in Hereford bulls. A total of sixty-eight bulls were analyzed at 22 months of age to assess growth metrics and carcass quality, with a focus on polymorphisms in these genes. The key markers of oxidative stress, including malondialdehyde (MDA), and the activities of antioxidant enzymes such as glutathione reductase (GluRed), glutathione peroxidase (GPx), and superoxide dismutase (SOD) were measured, alongside bioactive compounds like taurine, carnosine, and anserine. The results show that the TT genotype of DGAT1 is linked to significantly higher MDA levels, reflecting increased lipid peroxidation, but is also associated with higher GluRed and GPx activities and elevated levels of taurine, carnosine, and anserine, suggesting an adaptive response to oxidative stress. The LEP gene analysis revealed that the CC genotype had the highest MDA levels but also exhibited increased GPx and SOD activities, with the CT genotype showing the highest SOD activity and the TT genotype the highest total antioxidant status (TAS). The SCD1 AA genotype displayed the highest activities of GluRed, GPx, and SOD, indicating a more effective antioxidant defence, while the VA genotype had the highest MDA levels and the VV genotype showed lower MDA levels, suggesting protective effects against oxidative damage. These findings highlight genotype specific variations in the oxidative stress markers and bioactive compound levels, providing insights into the genetic regulation of oxidative stress and antioxidant defences, which could inform breeding strategies for improving oxidative stress resistance in livestock and managing related conditions.

Adaptive sensor attack detection and defense framework for autonomous vehicles based on density

The security of autonomous vehicles heavily depends on localization systems that integrate multiple sensors, which are vulnerable to sensor attacks and increase the risk of accidents. Given the diversity of sensor attacks and the dynamic changing of driving scenarios of autonomous vehicles, an adaptive and effective attack detection and defense framework faces a considerable challenge. This paper proposes a novel real-time adaptive attack detection and defense framework based on density, which can detect and identify attacked sensors and effectively recover data. We first develop a reinforcement learning multi-armed Bandit-based Density-Based Spatial Clustering of Applications with Noise (BDBSCAN) algorithm that selects hyperparameters adaptively. The Adaptive Extended Kalman Filter (AEKF) combines with the vehicle dynamic model on the localization system and extracts data features used for the BDBSCAN algorithm to monitor potential sensor attacks. If attack detection indicates possible system compromise, AEKF is further employed on localization sensors with anomalies identified through the BDBSCAN algorithm of the attacked sensors. To ensure precision and reliability, the data recovery incorporates a redundancy mechanism to apply a decision tree to select the optimal state estimation between AEKF and Extended Kalman Filter (EKF) to replace corrupted sensor data. To evaluate the effectiveness and adaptability of the proposed framework, we conducted 15,000 experiments using the real-world KITTI and V2V4Real datasets across various driving and sensor attack scenarios. The results demonstrate that our proposed framework achieves 100% accuracy and 0% false alarm rate in various driving scenarios for attack detection within 0.15 s, with a recovery time of 0.08 s.

Differential transcriptome reprogramming induced by the soybean cyst nematode Type 0 and Type 1.2.5.7 during resistant and susceptible interactions.

Soybean cyst nematode (SCN, Heterodera glycines) is a serious root parasite of soybean (Glycine max) that induces extensive gene expression changes associated with pleiotropic biological activities in infected cells. However, the impacts of various SCN Hg Types on host transcriptome reprogramming remain largely unknown. Here, we developed and used two recombinant inbred lines (RIL-72 and RIL-137) to profile transcriptome reprogramming in the infection sites during the resistant and susceptible interactions with SCN Hg Type 1.2.5.7 and Type 0. SCN bioassays indicated that RIL-72 was susceptible to Type 1.2.5.7 but resistant to Type 0, whereas RIL-137 was resistant to both types. Comparative analysis of gene expression changes induced by Type 1.2.5.7 in the resistant and susceptible lines revealed distinct transcriptome regulation with a number of similarly and oppositely regulated genes. The expression levels of similarly regulated genes in the susceptible line appeared to be insufficient to mount an effective defense against SCN. The functional importance of oppositely regulated genes was confirmed using virus induced gene silencing and overexpression approaches. Further transcriptome comparisons revealed shared as well as Hg Type- and genotype-specific transcriptome reprogramming. Shared transcriptome responses were mediated through common SCN-responsive genes and conserved immune signaling, whereas genotype-specific responses were derived from genetic variability, metabolic and hormonal differences, and varied regulation of protein phosphorylation and ubiquitination. The conserved defense mechanisms together with genotype-specific responses would enable plants to trigger effective and tailored immune responses to various Hg types and adapt the defense response to their genetic backgrounds.

Ecological adaptations of amphibians to environmental changes along an altitudinal gradient (Case Study: Bufo gargarizans) from phenotypic and genetic perspectives

BackgroundOrganisms have evolved a range of phenotypic and genetic adaptations to live in different environments along an altitudinal gradient. Herein, we studied the widely distributed Chinese toad, Bufo gargarizans, as a model and used an integrated phenotype-genotype approach to assess adaptations to different altitudinal environments.ResultsComparison of populations from four altitudes (50 m, 1200 m, 2300 m, and 3400 m) showed more effective defenses among high-altitude toads. These included thickened epidermis, more epidermal capillaries and granular glands, greater gland size in skin, and higher antioxidant enzyme activities in plasma. High-altitude toads also showed increased erythrocytes and hematocrit and elevated hemoglobin concentration, potentially improving oxygen delivery. Elevated altitude led to a metabolic shift from aerobic to anaerobic metabolism, and high-altitude populations favored carbohydrates over fatty acids to fuel for energy metabolism. Differentially expressed genes were associated with adaptive phenotypic changes. For instance, expression of genes associated with fatty acid metabolism showed greater suppression at high altitude (3400 m), consistent with decreased flux of β-hydroxybutyric acid and lower free fatty acids levels. Moreover, down-regulation of genes involved in carbon metabolism processes at high altitude (3400 m) were coincident with reduced TCA cycle flux. These results suggest that high-altitude toads adopt a metabolic suppression strategy for survival under harsh environmental conditions. Moreover, the hypoxia-inducible factor signaling cascade was activated at high altitude.ConclusionsCollectively, these results advance our comprehension of adaptation to high-altitude environments by revealing physiological and genetic mechanisms at work in Chinese toads living along altitudinal gradients.

Unsupervised Learning for Lateral-Movement-Based Threat Mitigation in Active Directory Attack Graphs

Cybersecurity threats, particularly those involving lateral movement within networks, pose significant risks to critical infrastructures such as Microsoft Active Directory. This study addresses the need for effective defense mechanisms that minimize network disruption while preventing attackers from reaching key assets. Modeling Active Directory networks as a graph in which the nodes represent the network components and the edges represent the logical interactions between them, we use centrality metrics to derive the impact of hardening nodes in terms of constraining the progression of attacks. We propose using Unsupervised Learning techniques, specifically density-based clustering algorithms, to identify those nodes given the information provided by their metrics. Our approach includes simulating attack paths using a snowball model, enabling us to analytically evaluate the impact of hardening on delaying Domain Administration compromise. We tested our methodology on both real and synthetic Active Directory graphs, demonstrating that it can significantly slow down the propagation of threats from reaching the Domain Administration across the studied scenarios. Additionally, we explore the potential of these techniques to enable flexible selection of the number of nodes to secure. Our findings suggest that the proposed methods significantly enhance the resilience of Active Directory environments against targeted cyber-attacks.

Link Stealing Attacks Against Inductive Graph Neural Networks

A graph neural network (GNN) is a type of neural network that is specifically designed to process graph-structured data. Typically, GNNs can be implemented in two settings, including the transductive setting and the inductive setting. In the transductive setting, the trained model can only predict the labels of nodes that were observed at the training time. In the inductive setting, the trained model can be generalized to new nodes/graphs. Due to its flexibility, the inductive setting is the most popular GNN setting at the moment. Previous work has shown that transductive GNNs are vulnerable to a series of privacy attacks. However, a comprehensive privacy analysis of inductive GNN models is still missing. This paper fills the gap by conducting a systematic privacy analysis of inductive GNNs through the lens of link stealing attacks. We propose two types of link stealing attacks, i.e., posterior-only attacks and combined attacks. We define threat models of the posterior-only attacks with respect to node topology and the combined attacks by considering combinations of posteriors, node attributes, and graph features. Extensive evaluation on six real-world datasets demonstrates that inductive GNNs leak rich information that enables link stealing attacks with advantageous properties. Even attacks with no knowledge about graph structures can be effective. We also show that our attacks are robust to different node similarities and different graph features. As a counterpart, we investigate two possible defenses and discover they are ineffective against our attacks, which calls for more effective defenses.

Evaluating the Application of the SIR Model to a Localized Population

Objective Using data collected from the Johns Hopkins COVID-19 Repository, I investigated the reliability of the SIR (Susceptible-Infected-Removed) model. Summary of Background Data Modern pandemic responses have evolved into effective defenses when implemented correctly but present issues in epidemiological modeling efforts. Intervention strategies such as quarantining and masking limit population size (N), which affects the accuracy of modeling population-based rate systems especially in highly transmissible diseases. Methods I begin by reviewing the SIR model retroactively to the initial SARS-CoV-2 Wuhan strain. I compared the parameters available in the published literature (N = 2,717,000, β = 2, γ = 1/14) to the best-fitting SIR-yielded values by minimizing the root mean squared error function. Subsequently, I evaluated its predictive capabilities on the Delta variant using early surge data, which was later compared against a retroactive analysis. Results Using a least-squares error best-fit analysis allowed me to retroactively define remarkably accurate model parameters for the Wuhan waves. Parameters including N = 730, β = 0.46, γ = 0.043 in the first wave and N = 11,200, β = 0.198, γ = 0.07 in the second reflected effective intervention strategies. I show it is an effective predictive tool regarding the Delta variant, yielding parameters N = 50,900, β = 0.87, γ = 1/3.7 that proved accurate when compared with parameters from a full retroactive analysis (N = 60,000, β = 0.94, γ = 1/3.6). Conclusion The similarity of the yielded parameters in my results supports the SIR model’s utility in epidemiological monitoring of high-transmissibility, low-mortality outbreaks vis-à-vis various containment measures.

Adversarial artifact detection in EEG-based brain–computer interfaces

Objective. machine learning has achieved significant success in electroencephalogram (EEG) based brain-computer interfaces (BCIs), with most existing research focusing on improving the decoding accuracy. However, recent studies have shown that EEG-based BCIs are vulnerable to adversarial attacks, where small perturbations added to the input can cause misclassification. Detecting adversarial examples is crucial for both understanding this phenomenon and developing effective defense strategies.Approach. this paper, for the first time, explores adversarial detection in EEG-based BCIs. We extend several popular adversarial detection approaches from computer vision to BCIs. Two new Mahalanobis distance based adversarial detection approaches, and three cosine distance based adversarial detection approaches, are also proposed, which showed promising performance in detecting three kinds of white-box attacks.Main results. we evaluated the performance of eight adversarial detection approaches on three EEG datasets, three neural networks, and four types of adversarial attacks. Our approach achieved an area under the curve score of up to 99.99% in detecting white-box attacks. Additionally, we assessed the transferability of different adversarial detectors to unknown attacks.Significance. through extensive experiments, we found that white-box attacks may be easily detected, and differences exist in the distributions of different types of adversarial examples. Our work should facilitate understanding the vulnerability of existing BCI models and developing more secure BCIs in the future.

ВЗАЄМОЗВ’ЯЗОК КІБЕРБЕЗПЕКИ ТА ІННОВАЦІЙНОГО ПОТЕНЦІАЛУ КРАЇНИ ПІД ЧАС ТРАНСФЕРУ ТА ВПРОВАДЖЕННЯ ІННОВАЦІЙ

The article is devoted to the investigation of the relationship between innovation and cyber security in the context of forecasting and reducing risks related to cyber security during the implementation of innovations. The purpose of the study is to confirm and model the interconnection between the levels of innovation development and cyber security of the country. The work describes the concept of innovation risk and the importance of cyber security in the modern world as one of the important factors in overcoming innovation risks. The current state of cyber security was analysed based on various indices, and the impact of cyber threats on innovation processes was investigated based on a sample from 26 countries of the world. This made it possible to identify leaders and outsiders in this field, as well as trends in the development of cyber security in dynamics. It is well-founded that cyber security is a key factor for the development and implementation of innovations. To confirm the existing relationship between cyber security and innovation, the multiple correlation coefficient was calculated, and an econometric model was built using the built-in functions of MS Excel (the estimation of the model parameters was carried out using the method of least squares using the built-in "Data Analysis" package of the MS spreadsheet editor Excel for a multivariable linear model). The significance of the model was confirmed by the coefficient of determination, Fisher's test, and the level of significance of the p-value. The results of the study can be used to develop effective cyber defence strategies and contribute to the stable development of technologies in the face of growing cyber threats.

IUAC: Inaudible Universal Adversarial Attacks Against Smart Speakers

Intelligent voice systems are widely utilized to control smart home applications, which raises significant privacy and security concerns. Recent studies have revealed their vulnerability to adversarial attacks, replay attacks, etc. However, these attacks rely on the victim’s voice data. In our work, we investigate a stealthy and command-independent attack that does not necessitate collecting victims’ voices. Our proposed attack, IUAC, misleads the voice system to go against the victim’s will, regardless of the commands delivered. Our core concept is to train highly robust attack commands through the construction of diverse data, rendering the user’s commands negligible. To achieve stealthy attacks, we leverage a high-frequency carrier to construct an inaudible universal adversarial command. Extensive experiments conducted with real-world datasets demonstrate that our attack system attains an average attack success rate of 96% while resisting environmental interference. Moreover, our attack success rate against real-world voice systems is 4.52 × higher than the state-of-the-art. Finally, we propose an effective defense mechanism and provide experimental tests to validate its efficacy.

Clustering and Cooperative Guidance of Multiple Decoys for Defending a Naval Platform against Salvo Threats

The threat to naval platforms from missile systems is increasing due to recent advancements in radar seeker technology, which have significantly enhanced the accuracy and effectiveness of missile targeting. In scenarios where a naval platform with limited maneuverability faces salvo attacks, the importance of an effective defense strategy becomes crucial to ensuring the protection of the platform. In this study, we present a multi-agent reinforcement learning-based decoy deployment approach that employs six decoys to increase the survival likelihood of a naval platform against salvo missile strikes. Our approach entails separating the decoys into two clusters, each consisting of three decoys. Subsequently, every cluster is allocated to a related missile threat. This is accomplished by training the decoys with the multi-agent deep reinforcement learning algorithm. To compare the proposed approach across different algorithms, we use two distinct algorithms to train the decoys; multi-agent deep deterministic policy gradient (MADDPG) and multi-agent twin-delayed deep deterministic policy gradient (MATD3). Following training, the decoys learn to form groups and establish effective formation configurations within each group to ensure optimal coordination. We assess the proposed decoy deployment strategy using parameters including decoy deployment angle and maximum decoy speed. Our findings indicate that decoys positioned on the same side outperform those positioned on different sides relative to the target platform. In general, MATD3 performs slightly better than MADDPG. Decoys trained with MATD3 succeed in more successful formation configurations than those trained with the MADDPG method, which accounts for this enhancement.

Ozone stress-induced DNA methylation variations and their transgenerational inheritance in foxtail millet.

Elevated near-surface ozone (O3) concentrations have surpassed the tolerance limits of plants, significantly impacting crop growth and yield. To mitigate ozone pollution, plants must evolve a rapid and effective defense mechanism to alleviate ozone-induced damage. DNA methylation, as one of the most crucial epigenetic modifications, plays a pivotal role in maintaining gene stability, regulating gene expression, and enhancing plant resilience to environmental stressors. However, the epigenetic response of plants to O3 stress, particularly DNA methylation variations and their intergenerational transmission, remains poorly understood. This study aims to explore the epigenetic mechanisms underlying plant responses to ozone stress across generations and to identify potential epigenetic modification sites or genes crucial in response to ozone stress. Using Open Top Chambers (OTCs), we simulated ozone conditions and subjected foxtail millet to continuous ozone stress at 200 nmol mol-1 for two consecutive generations (S0 and S1). Results revealed that under high-concentration ozone stress, foxtail millet leaves exhibited symptoms ranging from yellowing and curling to desiccation, but the damage in the S1 generation was not more severe than that in the S0 generation. Methylation Sensitive Amplified Polymorphism (MSAP) analysis of the two generations indicated that ozone stress-induced methylation variations ranging from 10.82% to 13.59%, with demethylation events ranged from 0.52% to 5.58%, while hypermethylation occurred between 0.35% and 2.76%. Reproductive growth stages were more sensitive to ozone than vegetative stages. Notably, the S1 generation exhibited widespread demethylation variations, primarily at CNG sites, compared to S0 under similar stress conditions. The inheritance pattern between S0 and S1 generations was mainly of the A-A-B-A type. By recovering and sequencing methylation variant bands, we identified six stress-related differential amplification sequences, implicating these variants in various biological processes. These findings underscore the potential significance of DNA methylation variations as a critical mechanism in plants' response to ozone stress, providing theoretical insights and references for a comprehensive understanding of plant adaptation mechanisms to ozone stress and the epigenetic role of DNA methylation in abiotic stress regulation.