Fine-grained wildfire spread prediction is crucial for enhancing emergency response efficacy and decision-making precision. However, existing research predominantly focuses on coarse spatiotemporal scales and relies on low-resolution satellite data, capturing only macroscopic fire states while fundamentally constraining high-precision localized fire dynamics modeling capabilities. To bridge this gap, we present FireSentry, a provincial-scale multi-modal wildfire dataset characterized by sub-meter spatial and sub-second temporal resolution. Collected using synchronized UAV platforms, FireSentry provides visible and infrared video streams, in-situ environmental measurements, and manually validated fire masks. Building on FireSentry, we establish a comprehensive benchmark encompassing physics-based, data-driven, and generative models, revealing the limitations of existing mask-only approaches. Our analysis proposes FiReDiff, a novel dual-modality paradigm that first predicts future video sequences in the infrared modality, and then precisely segments fire masks in the mask modality based on the generated dynamics. FiReDiff achieves state-of-the-art performance, with video quality gains of 39.2% in PSNR, 36.1% in SSIM, 50.0% in LPIPS, 29.4% in FVD, and mask accuracy gains of 3.3% in AUPRC, 59.1% in F1 score, 42.9% in IoU, and 62.5% in MSE when applied to generative models. The FireSentry benchmark dataset and FiReDiff paradigm collectively advance fine-grained wildfire forecasting and dynamic disaster simulation. The processed benchmark dataset is publicly available at: https://github.com/Munan222/FireSentry-Benchmark-Dataset.

Cross-site scripting (XSS) attacks pose a significant threat to web applications and user privacy, with the number of such attacks rapidly increasing. Although existing machine learning and deep learning-based XSS attack detection models are effective against common XSS attacks, these models all overlook their own security and often fail to defend against adversarial samples that exploit model vulnerabilities, allowing attackers to successfully bypass these models by using XSS adversarial samples. To address this challenge, in this paper, we propose a novel XSS attack detection model based on two-stage Abstract Syntax Tree (AST) analysis and Long Short-Term Memory (LSTM) neural networks, effectively mitigating the impact of adversarial samples. Our model leverages the ability of AST parsing and analysis of HTML and JavaScript code to effectively eliminate redundant information and adversarial perturbations introduced by adversarial samples. The two-stage process first extracts JavaScript code from the HTML AST, then identifies malicious code fragments from the JavaScript AST. Finally, the LSTM neural network is trained to classify samples as malicious or benign. By analyzing the HTML and JavaScript components of web pages, our model identifies and eliminates adversarial perturbations that interfere with detection, significantly enhancing the security and reliability of the detection process. Extensive experiments on real datasets demonstrate our model's superior performance, achieving an accuracy rate of 0.991 and an F1 score of 0.998 against standard XSS samples, outperforming existing models. More importantly, when facing adversarial XSS samples, most existing detection models exhibit severe robustness degradation with the detection rate (DR) below 0.880, whereas our model maintains a detection rate of over 0.982, significantly higher than state-of-the-art models and demonstrating its significant effectiveness in defending against XSS adversarial attacks.

Fine-grained air pollution forecasting is crucial for urban management and the development of healthy buildings. Deploying portable sensors on mobile platforms such as cars and buses offers a low-cost, easy-to-maintain, and wide-coverage data collection solution. However, due to the random and uncontrollable movement patterns of these non-dedicated mobile platforms, the resulting sensor data are often incomplete and temporally inconsistent. By exploring potential training patterns in the reverse process of diffusion models, we proposeSpatio-TemporalPhysics-InformedDiffusion Models (STeP-Diff). STeP-Diff leverages DeepONet to model the spatial sequence of measurements along with a PDE-informed diffusion model to forecast the spatio-temporal field from incomplete and time-varying data. Through a PDE-constrained regularization framework, the denoising process asymptotically converges to the convection-diffusion dynamics, ensuring that predictions are both grounded in real-world measurements and aligned with the fundamental physics governing pollution dispersion. To assess the performance of the system, we deployed 59 self-designed portable sensing devices in two cities, operating for 14 days to collect air pollution data. Compared to the second-best performing algorithm, our model achieved improvements of up to 89.12% in MAE, 82.30% in RMSE, and 25.00% in MAPE, with extensive evaluations demonstrating that STeP-Diff effectively captures the spatio-temporal dependencies in air pollution fields.

Recent researches have shown that non-sequential tasks based on deep neural networks (DNN), such as image classification and object detection, are vulnerable to backdoor attacks, leading to incorrect model predictions. As a crucial task in computer vision, Scene Text Recognition (STR) is widely used in IoT fields such as intelligent transportation systems and intelligent surveillance. Given its importance, ensuring the security and accuracy of STR models is critical. However, there are currently no studies on STR backdoor attacks. In this paper, we make the first attempt to validate backdoor threats on STR models by using a Patch-Based Attack method. Our experimental results confirm that STR models can be successfully compromised with attack success rate (ASR) of over 80% on most datasets. However, we also reveal a critical flaw: the Patch-Based attack lacks robustness due to the specific preprocessing in STR models (such as resizing and TPS rectification), which distort or eliminate the backdoor triggers. To address this, we further propose BadSTR, a novel backdoor attack method that uses semantic text sequences as triggers. Extensive experiments on eight benchmark datasets show that our proposed BadSTR achieves ASR of over 90% for most model-dataset combinations with significantly improved robustness.

Mobile air pollution sensing methods have emerged to collect air quality data with improved spatial and temporal resolutions. However, existing methodologies struggle to effectively process spatially mixed gas samples due to the highly dynamic fluctuations experienced by sensors, resulting in significant measurement deviations. We identify an opportunity to address this issue by exploring potential patterns within sensor measurements. To this end, we propose CatUA, a novel city-scale fine-grained air quality estimation system designed to deliver accurate mobile air quality data. First, we design AirBERT, a representation learning model specifically aimed at discerning mixed gas concentrations from sensor data. Second, we implement a Prompt-informed Training Strategy that leverages extensive unlabeled and minimal labeled city-scale data to enhance the performance of CatUA. Notably, the Auto-Prompt mechanism allows CatUA to conveniently acquire new knowledge tailored to specific downstream tasks. To ensure the practicality of CatUA, we have invested considerable effort in developing the software stack on our meticulously crafted Sensing Front-end, which has successfully gathered city-scale air quality data for over 1,200 hours. Experiments conducted on the collected data demonstrate that CatUA reduces sensing errors by 96.9% with a latency of only 44.9ms, outperforming the state-of-the-art baseline by 42.6%.

In recent years, Bitcoin has become one of the most popular cryptocurrencies. The most significant mechanism of Bitcoin is PoW (Proof-of-Work), but it also brings opportunities for mining attacks. In our last study, we proposed a Selfish Mining-based Denial-of-Service Attack (SDoS), which can cause serious threats to the Bitcoin system. On this basis, we further put forward three greedier SDoS attack strategies: a competitive greedy SDoS attack strategy ESDoS, a trail greedy SDoS attack strategy TSDoS, a hybrid greedy SDoS attack strategy ETSDoS, and a more public SDoS attack strategy PSDoS. Besides, we also study the adversary’s optimal strategies under different conditions. The experimental results show that if the adversary adopts the SDoS optimal strategy, his revenue increase rate will be further improved and significantly higher than the other existing mining attacks. If the adversary masters 14% of the total mining power, he has a chance to improve his revenue (25% in Selfish Mining, 19.6% in SDoS), and if the adversary masters 15% of the total mining power, he is capable of launching a 51% attack.

Privacy-preserving record linkage (PPRL) aims to link records of the same real-world entity from different databases without exposing any private information about the entity. Bloom filters are widely used in PPRL due to their effectiveness in encoding records while enabling fast approximate linkage in the case of attribute value errors and changes. However, the basic Bloom filters used for PPRL can be subject to cryptanalysis attacks that expose the plain-text values encoded in them. Recent studies have successfully attacked some improved Bloom filter encodings in PPRL but require specific conditions or knowledge of various encoding parameters to obtain high accuracy. This paper presents a novel attack based on differential analysis against Bloom filters used for PPRL. The attack exploits graphs to model the relationship between attribute value variation and the difference between Bloom filters. Then, features are generated for the node in graphs according to a clustering algorithm that we propose. Thus, we can match nodes with similar features to re-identify encoded records. Experiments on two real-world databases show that even with improved Bloom filter encoding and some hardening techniques, our attack can re-identify private information from encoded records with high accuracy and require less priori knowledge.

In this paper, we focus on mining attacks targeting the Proof of Work (PoW) consensus mechanism in blockchain-based systems. Specifically, we model mining as a game and propose a mining attack – the Selfish mining-based denial of service (SDoS) attack. By studying the choices (mining or stopping) of honest miners under the attack and the adversary’s revenue, we demonstrate that selfish mining is incentive-compatible with game-level denial of service attack, and that SDoS can be more threatening than existing mining attacks. Even under the worst assumption, the adversary only needs to master more than 19.6% of the total mining power to increase the revenue, and can launch a 51% attack with much less than 50%. In addition, we show that honest miners may make decisions based on the overall or current utility, and choosing the current utility is more beneficial to the adversary.

Abstract Generally, the information theoretically secret key agreement by public discussion consists of three phases, namely, advantage distillation, information reconciliation, and privacy amplification. In the existing literatures, these three phases are studied separately, and no comprehensive consideration has been given. In this paper, we analyze the restrictive relationship among these three phases from an overall point of view. For the satellite scenario, we analyze in detail the Winnow protocol for the first time. We also present the mutual restrictive relationship between the parameters of the advantage distillation phase and the information reconciliation phase. We further address how to set the parameters of the advantage distillation and the information reconciliation to maximize the total secret key agreement efficiency. Moreover, we determine the required length range of the initial random key string to guarantee the validation of the secret key agreement. Copyright © 2015 John Wiley & Sons, Ltd.

ABSTRACT The self‐healing group key distribution scheme with revocation can deal with the problem of distributing session keys for secure group communication over an unreliable wireless network, with the capability to resist packet loss and collusion attack. However, existing access‐polynomial‐based self‐healing key management schemes have some problems, such as the small number of active group members, much redundancy in the key updating broadcast packet, and limited collusion attack resistance capability. In order to increase the number of active group members and improve the performance of self‐healing group key distribution schemes, we propose a self‐healing group key distribution scheme based on the access polynomial and one‐way hash key chain for resource‐constrained wireless networks in this paper. In our proposed scheme, by binding the time at which the user joins the group with the capability of recovering previous group session keys, some novel methods to construct the personal secret, the access polynomial, and the session key updating broadcast packet are presented. Compared with existing schemes under same conditions, analysis and simulation results show that our proposed scheme not only supports much more group members and sessions, but also provides a stronger security, considering that it can deal with more colluding users. Moreover, our proposed scheme is especially suitable to resource‐constrained wireless networks in a very bad environment. Copyright © 2012 John Wiley & Sons, Ltd.