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.

Internet of Things (IoT) devices using subscription services (e.g. connected vehicles accessing entertainment programs) often purchase membership credentials from service providers with limited usage counts or validity periods, we call them pay-per-use or time span of membership services. However, users’ access records, usage preferences, and habits are collected by network adversarys or membership providers for creating users’ profiles, targeted advertising, and even for being sold maliciously. To deal with these problems, lots of anonymous authentication protocols are proposed to provide users with pseudonyms to conceal their real identities. Although these protocols effectively prevent network adversarys from compromising users’ privacy, membership service providers can still gather users’ behavioral privacy via their membership credentials. Therefore, several scholars proposed k-times anonymous authentication protocols and self-blind credentials to enhance users’ privacy protection, but the k-times anonymous authentication protocols are only for pay-per-use membership services and the schemes of self-blind credentials are lack of regulating malicious users. To address these issues, this article proposes an anonymous authentication protocol for time span of membership (AATM) with self-blindness and accountability. Specifically, we utilize Structure Preserving Signatures on Equivalence Classes (SPS-EQ) and Signatures with Flexible Public Key (SFPK) to build accountable, self-blinding credentials that ensure that every time a user visits a member, he or she can create a brand new identity on their own, which not only prevents users from being linked by service providers, but also supports conditional fair regulation. Security and performance analyses show that AATM is better than the state-of-the-art schemes in terms of security and privacy-preserving capabilities, and its computation cost also meets the practical application requirements.

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.

In recent years, reversible data hiding in encrypted domain (RDH-ED) has garnered considerable interest among researchers, resulting in the development of high-performance methods based on various carriers. However, the challenge of enhancing the data embedding capacity while ensuring reversibility becomes increasingly pronounced when the carrier is a three-dimensional (3D) model. In this paper, a high capacity RDH-ED method based on dynamic prediction and virtual connection for 3D models is proposed. Unlike existing methods that partition the vertices in the model into embeddable and prediction sets, where each vertex can only serve one function, the proposed dynamic prediction mechanism constructs a data embedding order set by leveraging the connectivity relationships between vertices. This allows each vertex within the set to both embed data and provide predictions, significantly increasing the proportion of embeddable vertices. Moreover, the proposed method is the first work to consider independent vertices within the model and integrates a novel virtual connection approach with the dynamic prediction process, enabling all independent vertices to participate in data embedding and prediction, thereby further enhancing the data embedding capacity. Experimental results demonstrated that the proposed method significantly outperforms other state-of-the-art methods in terms of data embedding capacity while ensuring reversibility.

Currently, deep learning-based video inpainting algorithms can fill in a specified video region with visually plausible content, usually leaving imperceptible traces. Since deep video inpainting methods can be used to maliciously manipulate video content, there is an urgent need for an effective method to detect and localize deep video inpainting. In this paper, we propose a dual-stream video inpainting detection network, which includes a ConvNeXt dual-stream encoder and a multi-scale feature cross-fusion decoder. To further explore the spatial and temporal traces left by deep inpainting, we extract motion residuals and enhance them using 3D convolution and SRM filtering. Furthermore, we extract filtered residuals using LoG and Laplacian filtering. These residuals are then entered into ConvNeXt, thereby learning discriminative inpainting features. To enhance detection accuracy, we design a top-down pyramid decoder that aims at deep fusion of multi-dimensional multi-scale features to fully exploit the information of different dimensions and levels in detail. We created two datasets containing state-of-the-art video inpainting algorithms and conducted various experiments to evaluate our approach. The experimental results demonstrate that our approach outperforms existing methods and attains a competitive performance despite encountering unseen inpainting algorithms.

Model inversion attacks (MIAs) aim to recover private data from inaccessible training sets of deep learning models, posing a privacy threat. MIAs primarily focus on the white-box scenario where attackers have full access to the model’s structure and parameters. However, practical applications are usually in black-box scenarios or label-only scenarios, i.e., the attackers can only obtain the output confidence vectors or labels by accessing the model. Therefore, the attack models in existing MIAs are difficult to effectively train with the knowledge of the target model, resulting in sub-optimal attacks. To the best of our knowledge, we pioneer the research of a powerful and practical attack model in the label-only scenario. In this paper, we develop a novel MIA method, leveraging a conditional diffusion model (CDM) to recover representative samples under the target label from the training set. Two techniques are introduced: selecting an auxiliary dataset relevant to the target model task and using predicted labels as conditions to guide training CDM; and inputting target label, pre-defined guidance strength, and random noise into the trained attack model to generate and correct multiple results for final selection. This method is evaluated using Learned Perceptual Image Patch Similarity as a new metric and as a judgment basis for deciding the values of hyper-parameters. Experimental results show that this method can generate similar and accurate samples to the target label, outperforming generators of previous approaches.

Personal data custodian services enable data owners to share their data with data consumers in a convenient manner, anytime and anywhere. However, with data hosted in these services being beyond the control of the data owners, it raises significant concerns about privacy in personal data sharing. Many schemes have been proposed to realize fine-grained access control and privacy protection in data sharing. However, they fail to protect the rights of data owners to their data under the law, since their designs focus on the management of system administrators rather than enhancing the data owners’ privacy. In this paper, we introduce a novel task-driven personal data sharing system based on the data capsule paradigm realizing personal data sharing autonomy. It enables data owners in our system to fully control their data, and share it autonomously. Specifically, we present a tamper-resistant data capsule encapsulation method, where the data capsule is the minimal unit for independent and secure personal data storage and sharing. Additionally, to realize selective sharing and informed-consent based authorization, we propose a task-driven data sharing mechanism that is resistant to collusion and EDoS attacks. Furthermore, by updating parts of the data capsules, the permissions granted to data consumers can be immediately revoked. Finally, we conduct a security and performance analysis, proving that our scheme is correct, sound, and secure, as well as revealing more advantageous features in practicality, compared with the state-of-the-art schemes.

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.

Glyph perturbation adjusts the characters’ structures and strokes to make the original characters change subtly, which cannot be detected by the naked eye. These generated variants with different glyph perturbation can represent different status of secret messages, which can be used to embed information in Chinese text documents. However, Chinese characters have characteristics in large numbers, complex structures, and diverse fonts, which limit the generation of glyph perturbation and make the design of Chinese characters time-consuming and laborious. Many font style transfer methods for Chinese characters have been proposed to improve the efficiency of Chinese character generation based on deep learning. At present, there are few studies on efficient font style transfer for glyph perturbation of Chinese characters. In this paper, a stylized glyph perturbation method based on style extractor and attention augmented convolution is proposed. It adopts a multi-head attention mechanism to enhance convolution in the font transfer, which concatenates the convolution feature maps and the self-attention activation maps to weaken the limitations of ordinary convolution in processing images. The extracted style features are sent into the decoder of the font transfer network so as to improve the stylized ability. Particularly, the impact of style extractor and attention augmented convolution on the glyph perturbation generation is addressed. The extraction accuracy and embedding capacity are tested in our experiments. The embedding capacity of secret message can achieve around 1.8 bit/character.

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.

With the increasing popularity of Online Social Networks (OSNs), covert communication is rapidly shifting from lossless channels like email to lossy channels, specifically social networks. In response to this trend, robust adaptive steganography has emerged as a powerful technique for concealing information in lossy transport channels. Previous approaches have aimed to address the challenge of JPEG image compression during transmission by utilizing static compression-resistant domains, Syndrome-Trellis Codes (STC), and Error Correction Codes (ECC). However, reliance on a significant number of ECC check codes to ensure robustness could inadvertently affect security. In response to this challenge, we introduce the “Adaptive STC-ECC” strategy, which enhances security by minimizing the number of check codes without compromising robustness. We further improve the robustness by simulating the embedding process and strategically placing the wet point in unstable cover elements. Furthermore, we exploit the residual information between the pre-cover and cover images to adjust the distortion and accurately determine the direction of the dither modulation, thus improving the overall security. Extensive experiments have been conducted to evaluate the performance of our proposed approach, and the results demonstrate its superior robustness and security compared to existing state-of-the-art approaches.

Regulating illegal activities in cyberspace to balance user privacy and cyberspace governance has been a non-trivial challenge when designing anonymous authentication solutions. For example, while several existing anonymous authentication protocols support accountability, they either risk leaking users' private keys or incur significant overhead for accountability in each ongoing authentication, including in cloud service-based authentication schemes. Seeking to address these limitations, this paper proposes an auditable anonymous user authentication (A2UA) protocol based on blockchain for cloud services. The A2UA protocol mainly employs bilinear pairing, partial authentication factors, dynamic credits and fake-public keys (FPKs) to achieve anonymous mutual authentication between users and cloud service providers, and applies ring signature and blockchain to accomplish two-level accountability while maintaining user privacy. Our analysis results show that the A2UA protocol outperforms several other existing schemes in terms of security, computation and communication costs as well as security and privacy features. Additionally, it has good feasibility in terms of the Ethereum Gas cost as demonstrated in our evaluation.

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.

Summary As people spend increasingly more time on social networks, cyberbullying has become a social problem that needs to be solved by machine learning methods. Our research focuses on textual cyberbullying detection because text is the most common form of social media. However, the content information in social media is short, noisy, and unstructured with incorrect spellings and symbols, and this impacts the performance of some traditional machine learning methods based on vocabulary knowledge. For this reason, we propose a Char‐CNNS (Character‐levelConvolutionalNeuralNetwork withShortcuts) model to identify whether the text in social media contains cyberbullying. We use characters as the smallest unit of learning, enabling the model to overcome spelling errors and intentional obfuscation in real‐world corpora. Shortcuts are utilized to stitch different levels of features to learn more granular bullying signals, and a focal loss function is adopted to overcome the class imbalance problem. We also provide a new ChineseWeibocomment dataset specifically for cyberbullying detection, and experiments are performed on both the Chinese Weibo dataset and the English Tweet dataset. The experimental results show that our approach is competitive with state‐of‐the‐art techniques on cyberbullying detection task.

Peer-to-peer (P2P) social networks rely on voluntary resource contributions of peers, understanding and maximizing the effects of resource allocation mechanisms on resource con- tribution of peers have been a focus in such networks. In most of proposed research, the resource sharing dilemma is always modeled by a two-player donor-recipient game in which peers are limited to binary decision (e.g., contribute or not). However, in addition to contributing to multiple recipients simultaneously, a peer also can determine its contribution level in networks. In this paper, we first formulated the resource sharing transaction among a group of peers as a multi-player donor-recipient game with multiple strategies which signify contribution willingness of peers. Then, we studied the influences of two reciprocity based allocation mechanisms in which peers are served based on their direct and total contributions, on the evolution of peers' contribution strategies. Moreover, the influences of some common behaviors of peers (e.g., leave- rejoin and irrational behaviors, slandering behaviors in reporting others' contribution) are also studied. The research is expected to provide valuable information for resource allocation mechanism design in social networks.

The epidemic containment game is a formulation to describe voluntary vaccination behaviors before epidemic spreading. This game relies on the characterization of the susceptible-infected- susceptible (SIS) model in terms of the spectral radius of the network. Existing researches showed that finding the worst Nash Equilibrium (NE) is NP-hard and used a heuristic algorithm called Low Degree (LDG) to estimate the maximum social cost under the worst NE (Max NE cost). By comparing the results of the LDG algorithm and exhaustive search, we found the LDG algorithm cannot estimate Max NE cost well, thus, we proposed a new neighbor information based algorithm to estimate Max NE cost in this paper. Moreover, we discussed Stackelberg strategies in which some nodes are secured first by a leader, then other agents choose their strategies voluntarily. We found the target (TAR) strategy is effective to reduce Max NE cost in a scale- free network when T is large and useless when T is low (T is the ratio of the recovery rate to the transmission rate in the SIS model). Moreover, we found that a lot of nodes with small degrees are secured voluntarily under the TAR strategy when T is low, which leads to high Max NE cost. At last, we proposed a new greedy algorithm to select nodes secured first, which can reduce Max NE cost when T is low.

Traffic flow prediction is an important research issue to avoid traffic congestion in transportation systems. Traffic congestion avoiding can be achieved by knowing traffic flow and then conducting transportation planning. Achieving traffic flow prediction is challenging as the prediction is affected by many complex factors such as inter-region traffic, vehicles' relations, and sudden events. However, as the mobile data of vehicles has been widely collected by sensor-embedded devices in transportation systems, it is possible to predict the traffic flow by analysing mobile data. This study proposes a deep learning based prediction algorithm, DeepTFP, to collectively predict the traffic flow on each and every traffic road of a city. This algorithm uses three deep residual neural networks to model temporal closeness, period, and trend properties of traffic flow. Each residual neural network consists of a branch of residual convolutional units. DeepTFP aggregates the outputs of the three residual neural networks to optimize the parameters of a time series prediction model. Contrast experiments on mobile time series data from the transportation system of England demonstrate that the proposed DeepTFP outperforms the Long Short-Term Memory (LSTM) architecture based method in prediction accuracy.

In collaborative data publishing (CDP), an m -adversary attack refers to a scenario where up to m malicious data providers collude to infer data records contributed by other providers. Existing solutions either rely on a trusted third party (TTP) or introduce expensive computation and communication overheads. In this paper, we present a practical distributed k -anonymization scheme, m - k -anonymization, designed to defend against m -adversary attacks without relying on any TTPs. We then prove its security in the semihonest adversary model and demonstrate how an extension of the scheme can also be proven secure in a stronger adversary model. We also evaluate its efficiency using a commonly used dataset.

In a group‐based trust management scheme, peers are partitioned into groups based on chosen characteristics, such as location and interest. The super peer (SP), who is responsible for the storage and distribution of reputation value, has an important role in group‐based trust management. Thus, if the SP is a disguised or malicious peer, serious security problems could occur. To solve these security problems, the authors propose a traceable, group‐oriented, signature scheme with multiple signing policies for trust management. The SP's signature is generated by a designated group called the signature group. In the authors scheme, peers in the signature group will decide whether to generate the signature for the SP based on the SP's reputation, meaning that attackers cannot forge a valid signature. In addition, an outsider also can trace the signers who were involved in generating the signature for reputation valuation.

SUMMARY The accuracy of feedback presentation, the sensitivity, and the robustness of reputation evaluation are critical issues to be addressed in a reputation system under the grid environment. This paper proposes a QoS‐based fine‐grained grid reputation system, where economic elements are considered to make the reputation system more sensitive in the commercial grid environments. A novel fine‐grained feedback presentation model based on aggregation of objective QoS attributes and subjective opinions of evaluators is proposed to enable a semi‐automatic, personalized and accurate feedback presentation. Through the introduction of a punishment factor and the adaptive reference of a previous trust value, the proposed reputation system effectively improves the sensitivity of the reputation evaluation. Moreover, the weighted combination of interorganizational trust, direct trust and recommended trust makes the reputation system more robust against collusion attacks. Simulation results show that the proposed reputation system can practically predict feedback and effectively resist malicious attacks such as fake transaction attacks and badmouthing attacks. This provides a clear advantage in the applications of grid service selection. Copyright © 2011 John Wiley & Sons, Ltd.

ABSTRACT Many traditional steganography methods do not disperse and hide secret data smoothly over all the capacity of cover images. Rather, they severely modify part of the cover image(s) to embed secret data, which results in stego images that have poor visual quality. In addition, there are still some secret image‐sharing approaches that cannot reveal the secret image losslessly without pixel expansion or extra storage or restore distortion‐free cover image(s) if they use steganography. In this paper, a novel scheme, which is based on Shamir's (t,n)‐threshold scheme (1979) and Galois Field GF(28) and uses dynamic embedding and least significant bit construction, is proposed to solve the issues mentioned above. Our experimental results showed that the dynamic embedding performance in our scheme was satisfactory and that both the secret image and the cover image can be restored losslessly without pixel expansion or extra storage. Copyright © 2012 John Wiley & Sons, Ltd.

The distributed system made the large-scale scientific computing possible in a cost effective way. And the hardware resources in such systems are also getting much cheaper than years before. However, the problem of executing the job using minimum resources is still reasonable and important, especially for the cloud environment, who has to save energy and control cost. Unfortunately, only a few existing scheduling algorithms have taken into account the resource usage issue. In this study, with considering the realistic network topology and communication model, we firstly propose the Deadline, Reliability, Resources-aware (DRR) scheduling algorithm. The theory analysis fully demonstrate that, the output schedule of our algorithm can satisfy the user's requirement on reliability and deadline. Through the experiments, with setting the deadline less than the make span of the MaxRe algorithm's output schedule, we find that our algorithm can complete the job under this deadline. Besides, our algorithm can save almost 50% computation resources and 70% communication resources than FTSA (bl) and FTSA (tl+bl) algorithms.

To achieve security in wireless sensor networks (WSNs), key management is one of the most challenging issues in design of WSN due to resource-constrained sensor nodes. Various key management schemes (KMs) have been proposed to enable encryption and authentication in WSN for different application scenarios. According to different equirements, it is important to select the trustworthy KMs in a WSN for setting up a fully appropriate WSN mechanism. An Analytic Hierarchy Process (AHP)-aided method helping with the complex decision has been presented in our previous work. Our purpose in this paper is to do performance analysis of KMs in WSN using our previous AHP-aided method. We analyze the characters of abundance KMs intuitively. The following five performance criteria are considered: scalability, key connectivity, resilience, storage overhead and communication overhead. As all permutations of five performance criteria include 120 types' situations, experimental analyses on 43 KMs for the optimum selection are presented.

Abstract Trust research is a key issue in peer‐to‐peer (P2P) networks. Reputation‐based trust models as one of the good solutions to resolve the trust problems in P2P network are received more and more attention in recent years. One of the fundamental challenges is to capture the evolving nature of a trust relationship between peers and reflect the varied bias or preference of peers in a distributed and open environment. In this paper, we present a fine‐grained trust computation model for P2P networks. Our model defines the service as a fined‐grained quality‐of‐service (QoS) (N‐dimensional vector), and in order to accurate the recommendation trust computing, several concepts are introduced to reflect the recommenders' current status, history behavior, and the gap between these two behaviors. Also, we firstly introduce the Gauss‐bar function to measure the preference similarity between peers. All these will result in a flexible model which represents trust in a manner more close to human intuitions and satisfies the diverse QoS requirements of peers in P2P networks. The extensive simulations have confirmed the efficiency of our model. Copyright © 2009 John Wiley & Sons, Ltd.

In the existing studies on fault-tolerant scheduling, the active replication schema makes use of ε + 1 replicas for each task to tolerate E failures. However, in this paper, we show that it does not always lead to a higher reliability with more replicas. Besides, the more replicas implies more resource consumption and higher economic cost. To address this problem, with the target to satisfy the user's reliability requirement with minimum resources, this paper proposes a new fault tolerant scheduling algorithm: MaxRe. In the algorithm, we incorporate the reliability analysis into the active replication schema, and exploit a dynamic number of replicas for different tasks. Both the theoretical analysis and experiments prove that the MaxRe algorithm's schedule can certainly satisfy user's reliability requirements. And the MaxRe scheduling algorithm can achieve the corresponding reliability with at most 70% fewer resources than the FTSA algorithm.

In P2P systems, it is important to guarantee the efficiency and security of message routing between the source and the destination. Unfortunately, malicious attackers can easily insert a lot of nodes they controlled, and disturb the message routing between honest nodes. In order to counter such attacks, secure routing protocols which ensure successful message routing are needed. Furthermore, it is very important to analyze existing routing protocols for the inventions of secure routing protocols. In this paper, we propose a security model for evaluating security level of routing protocol. In this model, we define the concept of Regular Path, and use it as the indicator of security level. Then, we analyze three representative routing protocols based on proposed model to make comparisons from the aspects of security level and implement level.