Adversarial training is often modeled as a two-player zero-sum game, relying on strong assumptions that limit its practical guidance. In this paper, we instead analyze the interactions between training samples and show that even the fundamental objective—minimizing training loss—may not converge. To address this, we propose AT-Field, an adversarial training framework guided by sample-wise game-theoretic relationships. Specifically, we prove that training samples across different batches can form a none-potential game, where gradient descent induces cyclic behaviors, preventing convergence. By strategically searching and grouping these samples within the same batch, AT-Field transforms none-potential games into exact potential games, which are more effectively optimized using gradient-based methods. Experiments demonstrate that AT-Field integrates seamlessly with existing adversarial training techniques, enhancing both accuracy and robustness.

In the battlefield of cyberspace, sophisticated attackers often operate by following meticulously designed cyber kill chains, enabling them to maintain a persistent presence within victim systems while evading conventional detection mechanisms. Traditional honeypot-based deception defenses aim to uncover such threats by luring attackers into exposing their malicious activities through decoy systems. However, advanced attackers are frequently able to identify and avoid these traps, making it increasingly challenging to detect and engage them effectively. To overcome this challenge, this study proposes a novel defensive paradigm named as the safeguard mode, which emphasizes the covert identification of attackers rather than solely preventing initial breaches. By proactively recognizing potential threats in a hidden manner, victim systems can be better protected through early threat intelligence. Based on the propsoed safeguard mode concept, we propose$Honey^{4}$, abbreviated as$H^{4}$, a comprehensive framework designed to systematically entrap advanced threats.$H^{4}$comprises four core components: Honeypoint, Honeyproxy, Honeytrace, and Honeycenter, which work in concert to deceive, monitor, and analyze attacker behavior. Furthermore, we explore how Artificial Intelligence Generated Content (AIGC) techniques can enhance$H^{4}$'s capabilities, particularly as attackers themselves begin to leverage AI-driven tactics. The practical efficacy of the proposed safeguard mode and the$H^{4}$framework has been validated through being deployed in real scenarios including the 19th Asian Games and the Canton Fairs, and$H^{4}$has successfully captured a significant number of threatening IP addresses and malicious behavioral patterns, generating actionable cyber threat intelligence that fundamentally safeguards system defense.

Advanced Persistent Threats (APTs) have become one of the most prominent cybersecurity risks globally. The external network-facing (ENF) services (e.g., e-commerce platforms) within a system are particularly vulnerable, as they are directly exposed to the Internet and often serve as the primary targets for attackers. By deploying deception resources to protect these ENF services, defenders can detect threats early, block potential attacks, and enhance overall system resilience. However, most existing studies on cyber deception strategies assume simultaneous moves by both attacker and defender. Furthermore, few works have considered the evolution of the system state resulting from APT attacks on the ENF services. To address these limitations, this paper proposes a Cyber Deception Stackelberg Markov Game (CDSMG) for protecting ENF services, which dynamically captures state transitions and accurately characterizes the strategic interactions between defenders and APT attackers. In CDSMG, the defender acts as the leader, who proactively selects a subset of services to deploy the deception resources based on the current system state, while the APT attacker plays as the follower, making a best response which incorporates the defender’s policy into its own strategy. To overcome the challenge of the combinatorial optimization problem of selecting a subset of services, we propose a revised version of the PPO algorithm by using no-replacement sampling to select multiple services at once, thereby significantly reducing the action space size. Finally, experimental results demonstrate that our approach effectively defends against APT attacks. It not only outperforms several baseline methods but also exhibits better scalability and robustness under varied model parameter settings.

Metaverse is drawing increasing attention from both academia and industry. Interoperability among different metaverse systems has become essential. A cross-metaverse interoperability protocol can enable interoperability across metaverses. However, cross-metaverse protocols often suffer significant cost overhead and transaction latency. For example, in STYLE, a leading cross-metaverse platform, 74% of transaction latency and 97% of the cost overhead are attributed to the relay blockchain rather than the two participating metaverses. To make cross-metaverse efficient, in this paper, we propose a fast and cheap cross-metaverse interoperability protocol namedCrossMeta.CrossMetacan enable direct communication among heterogeneous metaverses rather than depending on a relay blockchain. This is achieved through two components: i) a committee that relays transactions from the source metaverse to the destination metaverse, along with availability proofs, and ii) a smart contract that verifies the proofs provided by the committee. To ensure an honest majority within the selected committee, we propose a dynamic committee selection method based on the chain quality property. Furthermore, we demonstrate that honest brokers achieve a Nash equilibrium. Additionally, we prove that the proposedCrossMetaprotocol satisfies the security properties of atomicity and liveness. To demonstrate the practicality ofCrossMeta, we implemented a prototype of theCrossMetausing two real-world metaverse platforms, i.e.,Axie InfinityandSandbox. The evaluation results show thatCrossMetaoutperforms other cross-metaverse solutions regarding transaction latency and gas fees.

The Cyber Threat Intelligence (CTI) marketplace is an emerging platform for CTI service requesters to countermeasure advanced cyber attacks, where CTI service providers are employed on payment. To create a trustworthy CTI marketplace environment, consortium-blockchain-based CTI service platforms have been widely proposed, where the blockchain system becomes the third role, crucially impacting the CTI service quality. How to sustainably promote CTI service quality in this tripartite marketplace is a challenging issue, which has not been well investigated in the literature. In this study, we propose a two-stage tripartite dynamic game-model-based incentive mechanism, where the participation incentives of the three parties are promoted under the constraints of Individual Rationality (IR) and Incentive Compatibility (IC). The sustainability of CTI service is quantitatively formalized through the CTI market demand, which impacts the future profits of the three parties. The Nash equilibrium of the proposed incentive mechanism is solved, where the CTI requester offers an optimal price to achieve effective defense against cyber attacks, and the blockchain platform and CTI service providers collaboratively contribute high-quality CTI services. Empirical experimental results show that the higher the quality of CTI services provided in the marketplace, the greater the market demand for CTI, resulting in a sustainable CTI marketplace.

The proliferation of Internet of Things (IoT) devices, now numbering in the tens of billions, has exposed new attack surfaces due to their heterogeneous network architectures and vast numbers of distributed endpoints. The offensive-defensive dynamics of Advanced Persistent Threats (APTs) in IoT environments exhibit unique complexities, including enhanced stealth capabilities and prolonged attack lifecycles. This paper introduces APT-KG2QA, a knowledge graph-driven framework for generating specialized question-answering datasets. This methodology addresses two critical challenges in deploying large language models (LLMs) for cybersecurity applications: mitigating inherent biases in attack behavior recognition and overcoming logical reasoning deficits stemming from limited access to high-quality, domain-specific training data. The methodology utilizes a systematic conversion mechanism to translate the APT KG data into a hierarchical instruction template framework, which incorporates a hybrid prompt template engine with adversarial augmentation modules to produce domain-adaptive fine-tuning data. Low-Rank Adaptation (LoRA) technique facilitates parameter-efficient fine-tuning for four basic models. Experimental results indicate that the models enriched with domain knowledge achieve average increases of 577.7% in BLEU-4 and 623.7% in ROUGE-L measures, showing significant enhancements noted in output correctness, relevance, and comprehensibility. This study explores cross-modal integration pathways between KGs and LLMs, confirming the effectiveness of structured knowledge infusion in improving cybersecurity analysis capabilities, thus offering methodological guidance for developing intelligent security defense systems.

In Internet of Things (IoT) environments, anti-honeypot attacks exploit device fingerprinting and protocol analysis to undermine traditional honeypot defenses. To this end, we introduce an innovative camouflage defense strategy that dynamically disguises real IoT devices as honeypots to mislead attackers and protect critical resources. We formalize this adversarial interaction using a multi-stage signaling game, thereby reversing the information asymmetry that typically favors attackers. Subsequently, we theoretically derive the Perfect Bayesian Nash Equilibrium (PBNE) under conditions of incomplete information, thereby confirming the existence of mixed-strategy equilibria. Practically, we develop a signaling game-based multi-agent proximal policy optimization (SG-MAPPO) algorithm by integrating LSTM networks and Bayesian updates, overcoming challenges in long-term strategy optimization and parameter convergence in dynamic IoT contexts. Experiments demonstrate that SG-MAPPO outperforms existing methods in terms of training efficiency and stability, effectively reducing attacker success rates and providing a proactive defense solution for IoT security.

As a key technology in the Industrial Internet of Things (IIoT), the combination of digital twins (DTs) and mobile-edge computing (MEC) facilitates edge intelligence in B5G, while the application of generative artificial intelligence (GenAI) further enhances edge intelligence in resource allocation. However, the DT-enabled MEC system in IIoT faces the challenges of the low latency and high reliability demands, especially with the massive increase in machine-type communication devices and limited wireless and computing resources. To reduce overall delay and ensure high-reliability communication among machine devices (MDs), we propose an optimization problem for joint wireless and MEC computation resource allocation (JWMC-RA), which is shown to be NP-hard and intractable. Thereafter, the original problem is decomposed into two stages, i.e., machine-to-machine (M2M) links clustering, and MEC computing resource management. In the step of M2M links clustering, a heuristic clustering scheme via spectrum radius (HCS-SR) is presented for reducing interference of MDs which uses GenAI and graph theory. In the step of MEC computing resource management, the initial optimization problem is transformed to a convex problem, then, the optimal task offloading ratios of MD and MEC computing resource allocation are obtained. Finally, simulations show that the JWMC-RA scheme can reduce the overall delay and ensure the communication reliability.

Container technology in cloud computing has improved resource utilization and deployment efficiency, but it also introduces new security risks. Excessive privileges in containerized environments can allow attackers to exploit insufficiently restricted system calls, potentially leading to container escapes and other attacks. Some system calls are only necessary during the initialization phase of a container, and allowing them during runtime can increase the risk of exploitation. This paper proposes a Phase-based System Call Filtering (PSF) method to minimize system call permissions during the runtime of cloud containers. The PSF method builds a comprehensive whitelist of system calls during the initialization phase to cover all necessary calls for containerized applications. During runtime, a refined, phase-specific system call whitelist is enforced, dynamically adjusting privileges based on the functions encapsulated within the container. Additionally, we introduce a container phase recognition algorithm to distinguish between initialization and runtime phases, supporting the generation of phase-specific system call lists. Experimental results show that the proposed method enhances runtime system call restrictions, minimizes privileges, and improves the overall security of cloud containers.

The landscape of cyberattacks has transcended from mere hobbyist pursuits of cybercriminals to a lucrative business model, facilitating their sustenance. Concurrently, the cybercrime market has evolved into a complex ecosystem. Empowered by this environment, cybercriminal tactics have evolved from simple, isolated activities to intricate and coordinated cyberattacks. In this article, we reveal a new type of cyberattack paradigm termed Attack Exploiting Cyber-arms Industry (AECI), which, despite its potential for severe impact, requires less investment and entails fewer obstacles and risks compared to traditional methods. However, this type of attack is still neglected by security researchers and communities and this is the first work focusing on this type of attack. To elucidate AECI, we provide an overview of the cyber-arms industry and introduces an attack model. The model dissects each phase of AECI to illuminate its operational mechanics and strategic imperatives. Furthermore, to assess its potential impact, a mathematical model is proposed to estimate the scale of infection attributable to AECI. Through analysis of a specific attack case, our findings demonstrate that AECI can generate significant impacts within a brief timeframe, akin to the magnitude observed with the Mirai botnet. The proposed model is demonstrated to prove instrumental in effectively analyzing AECI and providing accurate estimations of its infection scale.

Data privacy protection legislation around the world has increasingly enforced the “right to be forgotten” regulation, generating a surge in research interest in machine unlearning (MU), which aims to remove the impact of training data from machine learning models upon receiving revocation requests from data owners. There exist two major challenges for the performance of MU: the execution efficiency and the inference interference. The former requires minimizing the computational overhead for each execution of the MU mechanism, while the latter calls for reducing the execution frequency to minimize interference with normal inference services. Nowadays most MU studies focus on the sample-level unlearning setting, leaving the other paramount feature-level setting under-explored. Adapting these existing techniques to the latter turns out to be non-trivial. The only known feature-level work achieves anapproximateunlearning guarantee, but suffers from degraded model accuracy and still leaves the inference interference challenge unsolved. We are therefore motivated to propose FELEMN, the first FEature-Level Exact Machine uNlearning method that overcomes both of the above-mentioned hurdles. For the MU execution efficiency challenge, we explore the impact of different feature partitioning strategies on the preservation of semantic relationships for maintaining model accuracy and MU efficiency. For the inference interference challenge, we propose two batching mechanisms to combine as many individual unlearning requests to be processed together as possible, while avoiding potential privacy issues coming with falsely postponing unlearning requests, which is grounded on theoretical analysis. Experiments on five real datasets show that our FELEMN outperforms up-to-date competitors with up to$3\times$speedup for each MU execution, and 50% runtime reduction by mitigating inference interference.

Learning-based systems have been proved to be vulnerable against model inversion attacks (MIAs), where attackers steal private information of training data by querying the target model using synthetic samples. To alleviate the urgent threat introduced by MIAs, existing advancements are proposed to increase the attack overhead by limiting the information available. Although these methods successfully reduced the attack success rate (ASR) for a one-time inversion attempt, they usually compromise the usability of the protected model. More importantly, existing MIA defense methods fail to capture attack attempts, which can lead to persistent threats to data privacy. To bridge this gap, we propose Neural Honeypoint, an active defense framework against MIAs. The key insight is that MIA attackers will make a series of forward steps in the feature space while benign users will not. Motivated by the observation, defenders can deploy active defense devices (honeypoints) on critical paths to capture attack behaviors. Specifically, Neural Honeypoint first models the attackers' capabilities from the frequency domain and designs specialized honeypoints for protected classes in the training dataset. Subsequently, it deploys these honeypoints into the protected model via backdoor-like model fine-tuning. Then, defenders can distinguish model inversion examples by comparing the similarity of input features with deployed honeypoints. Experiments show that Neural Honeypoint reduces the ASRs of advanced MIAs to 0%~2%. Furthermore, it can effectively capture inversion queries, which helps defenders to detect and block attacks in time.

PLC, as the core of industrial control systems, has been turned into a focal point of research for attackers targeting industrial control systems. However, current researched methods for attacking PLCs suffer from issues such as lack of precision and limited specificity. This paper proposes a novel attack method called AOIFF. Specially, AOIFF extracts the binary control logic code from a running PLC and reverses the binary code into assemble code. And then awareness of industrial field information is extracted from assemble code. Finally, it is based on awareness that attack code is generated and injected into a PLC, which can disrupt the normal control logic and then launch precise attacks on industrial control systems. Experimental results demonstrate that AOIFF can effectively perceive information in industrial field and initiate precise and targeted attacks on industrial control systems. Additionally, AOIFF achieves excellent results in the reverse engineering of binary code, enabling effective analysis of binary code.

In the scenario of smart healthcare applications, the Internet of Medical Things (IoMT) devices, equipped with limited resources, would offload numerous computation-heavy tasks to an edge server through 5G networks. However, IoMT devices should usually move around different diagnostic areas in smart healthcare systems, leading to the dynamics of the uplink channel quality. Moreover, the burst generation of a substantial number of tasks from IoMT devices can result in congestion within the computing queue of the edge server. And, heterogeneous services in IoMT devices make it hard to collect global information for a central controller to get the optimal optimization for all IoMT devices. So, how to determine task offloading among IoMT devices in a distributed scenario of smart healthcare applications should be considered appropriately and comprehensively. In this paper, we investigate task offloading in mobile edge computing (MEC) through wireless networks. To improve the utilization of wireless resources, non-orthogonal multiple access (NOMA) is adopted in 5G networks. We first formulate the mobility of IoMT devices as a Hidden Markov Model (HMM) and the problem of task offloading policy as a distributed Partial Markov Decision Process (Dec-POMDP). Then, we propose a mobility-aware method based on Multi-agent reinforcement learning for task offloading in 5G NOMA-enabled networks. In our approach, task offloading scheduling for each IoMT device in NOMA-enabled 5G networks is considered to improve energy efficiency and guarantee service quality. Besides, the time complexity and the existence of a Nash equilibrium for our proposed Dec-POMDP method are theoretically derived. Simulations are conducted to show that our algorithm outperforms other alternative methods in energy consumption under the delay constraint.

With the booming of mobile edge computing (MEC) and blockchain-based blockchain-based federated learning (BCFL), more studies suggest deploying BCFL on edge servers. In this case, edge servers with restricted resources face the dilemma of serving both mobile devices for their offloading tasks and the BCFL system for model training and blockchain consensus without sacrificing the service quality to any side. To address this challenge, this article proposes a resource allocation scheme for edge servers to provide optimal services at the minimum cost. Specifically, we first analyze the energy consumption of the MEC and BCFL tasks, considering the completion time of each task as the service quality constraint. Then, we model the resource allocation challenge into a multivariate, multiconstraint, and convex optimization problem. While solving the problem in a progressive manner, we design two algorithms based on the alternating direction method of multipliers (ADMMs) in both homogeneous and heterogeneous situations, where equal and on-demand resource distribution strategies are, respectively, adopted. The validity of our proposed algorithms is proved via rigorous theoretical analysis. Moreover, the convergence and efficiency of our proposed resource allocation schemes are evaluated through extensive experiments.

Spatial crowdsourcing (SC) has become a fundamental and emerging technology in Metaverse, facilitating the creation of immersive experiences through location-based services. In these systems, a central SC server leverages SC workers who physically travel to task locations to gather spatiotemporal environment data. However, conventional SC systems face two significant challenges: (1) the SC server, functioning as a centralized authority, can sometimes be unreliable, either due to intentional or unintentional misconduct, (2) to ensure efficient task assignment and validation, the location privacy of tasks and workers is openly accessible. In this study, we formally define location privacy preserved proof generation and verification problem (LP-PGVP) within an SC task matching scenario, with the aim to the above two challenges. Our proposed solution is a blockchain-based SC system (BlockSC), which provides a decentralized platform for task requesters and workers in the Metaverse context through calling smart contracts. We also introduce a ciphertext-based task matching scheme where task location access is granted only to eligible workers executing a task, benefiting from the design of geographic coordinate transformation and bilinear mapping methodology. To further demonstrate the task matching scheme’s operation and impact, we present an easy-to-understand case study. Our evaluation findings confirm that the proposed system effectively maintains location privacy for both SC workers and task requesters, without a considerable sacrifice in task matching efficiency.

Federated learning (FL) is an emerging paradigm for privacy-preserving machine learning, in which multiple clients collaborate to generate a global model through training individual models with local data. However, FL is vulnerable to model poisoning attacks (MPAs) as malicious clients are able to destroy the global model by modifying local models. Although numerous model poisoning defense methods are extensively studied, they remain vulnerable to newly proposed optimized MPAs and are constrained by the necessity to presume a certain proportion of malicious clients. To this end, in this paper, we propose MODEL, a model poisoning defense framework for FL through truth discovery (TD). A distinctive aspect of MODEL is its ability to effectively prevent both optimized and byzantine MPAs. Furthermore, it requires no presupposed threshold for different settings of malicious clients (e.g., less than 33% or no more than 50%). Specifically, a TD-based metric and a clustering-based filtering mechanism are proposed to evaluate local models and avoid presupposing a threshold. Furthermore, MODEL is effective for non-independent and identically distributed (non-IID) training data. In addition, inspired by game theory, we incorporate a truthful and fair incentive mechanism in MODEL to encourage active client participation while mitigating the potential desire for attacks from malicious clients. Extensively comparative experiments demonstrate that MODEL effectively safeguards against optimized MPAs and outperforms the state-of-the-art.

As a distributed learning paradigm, federated learning (FL) can be applied in mobile edge computing (MEC) to support real-time artificial intelligence by leveraging edge computation resources while preserving data privacy in the end devices. However, the unpredictable wireless connections between end devices and edge servers in MEC (e.g., frequent handovers and unstable wireless channels) may result in the loss of important model parameters, which slows down the FL training process and degrades the quality of the global model. In this paper, we propose an adaptive collaborative federated learning (ACFL) scheme to accelerate the convergence and improve model reliability by mitigating communication-based parameter loss under a three-layer MEC architecture. First, a dynamic epoch adjustment method is proposed to reduce communication rounds by dynamically adjusting the training epochs in end devices. In addition, to accelerate the FL convergence, we present an edge server collaborative training scheme by leveraging a multi-layer computing architecture, where edge servers utilize their maintained data to collaboratively train models with end devices. Finally, extensive simulations are conducted and show that ACFL can efficiently improve model reliability and accelerate the convergence of the FL process in MEC.

The burgeoning landscape of blockchain technology has made the security of deployed smart contracts an imperative concern. While existing security measures excel in pre-deployment testing, they fall short in protecting smart contracts once they are deployed, leaving them susceptible to malicious attacks. In this paper, we propose a novel Smart Contract Firewall framework designed to bridge this security gap. Functioning as a dynamic gateway, the framework employs real-time transaction inspection through adaptable filtering rules, enabling the identification and rollback of malicious transactions as they occur. Our empirical analysis demonstrates the framework's efficacy in mitigating a majority of existing vulnerabilities in the deployed smart contracts. Although the added layer of security comes at a cost, we prove that the increased gas expenses could be limited to 30 % -50 % for most transactions. This trade-off, we argue, is a small price to pay for significantly enhanced security.

In video action recognition, shortcut static features can interfere with the learning of motion features, resulting in poor out-of-distribution (OOD) generalization. The video background is clearly a source of static bias, but the video foreground, such as the clothing of the actor, can also provide static bias. In this paper, we empirically verify the existence of foreground static bias by creating test videos with conflicting signals from the static and moving portions of the video. To tackle this issue, we propose a simple yet effective technique, StillMix, to learn robust action representations. Specifically, StillMix identifies bias-inducing video frames using a 2D reference network and mixes them with videos for training, serving as effective bias suppression even when we cannot explicitly extract the source of bias within each video frame or enumerate types of bias. Finally, to precisely evaluate static bias, we synthesize two new benchmarks, SCUBA for static cues in the background, and SCUFO for static cues in the foreground. With extensive experiments, we demonstrate that StillMix mitigates both types of static bias and improves video representations for downstream applications. Code is available at https://github.com/lihaoxin05/StillMix.

IoT data exchange plays a vital role in supporting various applications and services with massive IoT devices. However, the existence of malicious devices threatens the integrity and reliability of the exchanged data. Trust management has been used to mitigate the impact of malicious devices in centralized and decentralized architectures. However, most of these traditional trust management systems bear computation, storage, and communication challenges. In this study, we propose a semi-centralized trust management system architecture based on blockchain in both single and multiple domains. The IoT devices are centralized organized by cloud servers who coordinately sustain a rating data ledger within each domain based the proposed rotation based consensus protocol in a decentralized manner to support cross-domain data exchange. A computational trust model is proposed by aggregating the direct and indirect trust information, where we elaborately design decay function, recommendation credibility and adaptable weights so as to calculate the trust value of dynamic malicious devices. Finally, we evaluate the proposed system model in various situations through simulation based experiments and compare it with two classical models in the literature. The experimental results demonstrate the effectiveness of the proposed trust model in identifying malicious devices and mitigating the influence of malicious devices.

The consensus algorithm is the core component of a blockchain system, which determines the efficiency, security, and scalability of the blockchain network. The representative consensus algorithm is the proof of work (PoW) proposed in Bitcoin, where the consensus process consumes large amount of compute in solving meaningless Hash puzzel. Meanwhile, the deep learning (DL) has brought unprecedented performance gains at heavy computate cost. In this demo, we channels the otherwise wasted computational power to the practical purpose of training neural network models, through the proposed proof of learning (PoL) consensus algorithm. In PoLe, the training/testing data are released to the entire blockchain network (BCN) and the consensus nodes train NN models on the data, which serves as the proof of learning. When the consensus on the BCN considers a NN model to be valid, a new block is appended to the blockchain. Through our system, we investigate the potential of enpowering machine learning with consensus building on blockchains.

Weakly-supervised Temporal Action Localization (WTAL) aims to detect the action segments with only video-level action labels in training. The key challenge is how to distinguish the action of interest segments from the background, which is unlabelled even on the video-level. While previous works treat the background as "curses", we consider it as "blessings". Specifically, we first use causal analysis to point out that the common localization errors are due to the unobserved confounder that resides ubiquitously in visual recognition. Then, we propose a Temporal Smoothing PCA-based (TS-PCA) deconfounder, which exploits the unlabelled background to model an observed substitute for the unobserved confounder, to remove the confounding effect. Note that the proposed deconfounder is model-agnostic and non-intrusive, and hence can be applied in any WTAL method without model re-designs. Through extensive experiments on four state-of-the-art WTAL methods, we show that the deconfounder can improve all of them on the public datasets: THUMOS-14 and ActivityNet-1.31.

Strangers build trustworthiness through reputation systems in various online platforms. A reputation system collects history ratings from users about an object/entity and aggregates them as a reputation score, for the reference of future potential users. The reputation score can accurately reflect the real quality of this object if all the ratings are fairly provided, otherwise it may mislead other users if the ratings are unfairly provided. In order to mitigate the impacts of unfair ratings, many unfair rating detection models have been studied in the recent years, through identifying and filtering out the unfair ratings. In this work, we aim to investigate the existing unfair rating detection models considering realistic application settings in an interactive approach where the process of the unfair ratings detection is conducted by involving the interactions between the application system designer and these models. Based on this idea, we design a customized interactive system (CIS) which can satisfy the customized demands of application system designers through five customized functions, i.e., customized scenes, customized attack, customized model, customized metrics, and customized result presentations. After a series of interactions, application system designers can obtain the detection model that best fulfills their demands and the corresponding optimal parameters. To present the applicability of the proposed CIS system, we analyze several typical reputation models in our experiments and the experimental results indicate that our work can effectively bridge the existing unfair rating detection models with realistic applications.

The advent of neural network (NN) based deep learning, especially the recent development of the automatic design of networks, has brought unprecedented performance gains at heavy computational cost. On the other hand, in order to generate a new consensus block, Proof of Work (PoW) based blockchain systems routinely perform a huge amount of computation that does not achieve practical purposes but to solving a difficult cryptographic hash puzzle problem.In this study, we propose a new consensus mechanism, Proof of Learning (PoLe), which directs the computation spent for block consensus toward optimization of neural networks. In our design, the training and testing data are released to the entire blockchain network and the consensus nodes train NN models on the data, which serves as the proof of learning. As a core component of PoLe, we design a secure mapping layer (SML) to prevent consensus nodes from cheating, which can be straightforwardly implemented as a linear NN layer. When the consensus on the blockchain network is achieved, a new block is appended to the blockchain. We experimentally compare the PoLe protocol with Proof of Work (PoW) and show that PoLe can achieve a more stable block generation rate, which leads to more efficient transaction processing. Experimental evaluation also shows the PoLe can achieve a stable block generation rate without significantly sacrificing training performance.

In e-commerce, retailers or sellers are often assessed by customers or buyers based on reputation information to make wise purchasing decisions. Seller reputation becomes an important credential to shadow seller future behaviour. Most existing reputation models directly aggregate the ratings provided by past buyers. However, it is well documented in practical e-commerce systems that buyers’ ratings can be distorted due to collusion, which negatively affects the applicability of these reputation models. To address this challenging problem, we propose the repurchase-and-return reputation (3R) model, which puts buyers’ ratings into context before aggregating them to compute seller reputation. It considers buyer repurchase and product return behaviour after the point in time when the particular rating was provided. Intuitively, repurchases indicate that the buyers are satisfied with the previously purchased products. Thus, their positive ratings should be given more weight. Similarly, product return behaviours indicate that buyers are dissatisfied with their previous purchasing decisions. Thus, their negative ratings should be given more weight. Based on the proposed 3R reputation model, we design a price premium for a transaction considering the post-purchase behaviour of both the buyer and seller in their transactions. The proposed model is proven capable of achieving a pure strategy Nash equilibrium, in which sellers honestly provide products and buyers prefer to return bad products and repurchase good quality products. Experimental evaluation based on extensive simulation demonstrates that our model can accurately evaluate sellers’ honesty and perform well against prevailing unfair rating attacks.

The rapid development of blockchain technology and its various applications have attracted huge attention in the last five years. The consensus mechanism and incentive mechanism are the backbone of a blockchain network. The consensus mechanism plays a crucial role in sustaining the network security, integrity, and efficiency. The incentive mechanism motivates the distributed nodes to “mine” so as to participate the consensus mechanism. The existing mechanisms bear the fairness and justice issues. In this paper, from the perspective of mechanism design, we propose a consensus-incentive mechanism through applying continuous double auction theory, which is abbreviated as ABC mechanism. Our mechanism consists of four stages, including initiation stage, auction stage, completion stage, and confirmation stage. The auction model in use is the continuous double auction to ensure the transactions are stored in a real-time manner. Through extensive experimental evaluations, our mechanism is proven to improve the fairness and justice of the blockchain network.

Jointing visual-semantic embeddings (VSE) have become a research hotpot for the task of image annotation, which suffers from the issue of semantic gap, i.e., the gap between images' visual features (low-level) and labels' semantic features (high-level). This issue will be even more challenging if visual features cannot be retrieved from images, that is, when images are only denoted by numerical IDs as given in some real datasets. The typical way of existing VSE methods is to perform a uniform sampling method for negative examples that violate the ranking order against positive examples, which requires a time-consuming search in the whole label space. In this paper, we propose a fast adaptive negative sampler that can work well in the settings of no figure pixels available. Our sampling strategy is to choose the negative examples that are most likely to meet the requirements of violation according to the latent factors of images. In this way, our approach can linearly scale up to large datasets. The experiments demonstrate that our approach converges 5.02x faster than the state-of-the-art approaches on OpenImages, 2.5x on IAPR-TCI2 and 2.06x on NUS-WIDE datasets, as well as better ranking accuracy across datasets.

Software Defined Networking (SDN) decouples the control plane from the data plane, which increases network scalability and flexibility. But malicious applications on SDN controller can cause the entire network to crash. So, we design a reputation and incentive mechanism on SDN to reduce application's malicious access. In the proposed module, first of all, the application behavior is analyzed and the malicious accesses are identified, which are used to build the reputation and incentive mechanism. Second, the analysis results of the application behavior are combined through beta probability density to obtain the reputation rating. The reward or punishment will be given based on the behavior and reputation of the application under the selected social strategy. Simulation results show that the system can accurately identify malicious behavior and reduce malicious requests, with an acceptable runtime overhead about 300 microseconds.

In this paper, we propose a decentralized identity management system based on Blockchain. The function of the system mainly includes identity authentication and reputation management. The technical advantages of the Blockchain makes the data in the system safe and credible. In addition, we use smart contracts to write system rules to ensure the reliability of user information. We bind the user's entity information with the public key address and determine the true identity of a virtual user on the Blockchain. We use the token to represent the reputation which is shown to be an effective reputation model, making the participants in the system prefer to maintain and manage their personal reputation. Our system makes it possible for users to securely manage their identity and reputation on the Internet.

In electronic commerce, buyers and sellers conduct transactions without physical interactions. In reputation systems, the trustworthiness of sellers is achieved by aggregating the ratings shared by other buyers with whom the sellers have ever conducted transactions. However, the ratings provided by buyers for evaluating the same seller could be diverse due to their different judgment criteria, which is referred as the subjectivity problem of reputation systems. It indicates that the ratings shared by some buyers may mislead other buyers with different personalities, making it challenging to aggregate the ratings properly in reputation systems. In this paper, in order to cope with the subjectivity problem, a hybrid architecture of reputation systems is proposed, which is based on coalition formation game theory. In the proposed module, buyers with the same subjectivity will automatically form a club, and share their ratings so as to build seller reputation within their club. The utility of a club is the profit created by the reputation system, which is further divided among the buyers of the club. Two utility allocation algorithms have been investigated, i.e., the proportional and Shapley allocations, respectively. Theoretical analysis and experimental results have shown that buyers with the same personality have the incentive to form a separate pure club if specific conditions are satisfied.

In recent years, trust has emerged with the development of cloud computing. It is a critical step to select a trusted cloud provider before the service begins, which is related to the interests of cloud consumers themselves and the quality of the service. A SLA trust model based on behavior evaluation is proposed in this paper. User's subjective evaluations are abandoned, the provider who is trusted and meets the demand for cloud consumers is selected according to the transaction history (mainly the parameter vectors formed during the transaction process) between cloud providers and cloud consumers and trust value of cloud providers before the service starts. In the service process, using iterative methods dynamically updates the trust value based on the fulfillment of SLA parameters. At the same time, the time factor is taken into account, so that the trust value is more reasonable. Experiments show that the model is able to select trusted provider to trade according to the demands of cloud consumers, dynamically updates the trust value and the trust value of malicious providers can be suppressed.

With the prevalence of social networks, social recommendation is rapidly gaining popularity. Currently, social information has mainly been utilized for enhancing rating prediction accuracy, which may not be enough to satisfy user needs. Items with high prediction accuracy tend to be the ones that users are familiar with and may not interest them to explore. In this paper, we take a psychologically inspired view to recommend items that will interest users based on the theory of social curiosity and study its impact on important dimensions of recommender systems. We propose a social curiosity inspired recommendation model which combines both user preferences and user curiosity. The proposed recommendation model is evaluated using large scale real world datasets and the experimental results demonstrate that the inclusion of social curiosity significantly improves recommendation precision, coverage and diversity.

Reputation systems aggregate the ratings provided by buyers to gauge the reliability of sellers in e-marketplaces. The evaluation accuracy of seller reputation significantly impacts the sellers' future utility. The existence of unfair ratings is well-recognized to negatively affect the accuracy of reputation evaluation. Most of the existing approaches dealing with unfair ratings focus on filtering/discounting/aligning the possible unfair ratings caused by malicious attacks or subjective difference. However, these approaches are not effective against unfair ratings in the form of misreporting (e.g., a well-behaving buyer misjudged a seller and provided a negative rating to a transaction which deserves a positive one, and the buyer is willing to revert the misreported negative rating). In this case, how should the buyer undo the damage caused by such misreported ratings and help the seller recover utility loss? In this paper, we propose a reputation revision mechanism to mitigate the negative effects of the misreported ratings. The proposed mechanism temporarily inflates the reputation of the misjudged seller for a period of time, which allows the seller to recover his utility loss caused by the misreported ratings. Extensive realistic simulation based experiments demonstrate the necessity and effectiveness of the proposed mechanism.

Reputation models depend on the ratings provided by buyers togauge the reliability of sellers in multi-agent based e-commerce environment. However, there is no prevention forthe cases in which a buyer misjudges a seller, and provides a negative rating to an original satisfactory transaction. In this case,how should the seller get his reputation repaired andutility loss recovered? In this work, we propose a mechanism to mitigate the negativeeffect of the misreported ratings. It temporarily inflates the reputation of thevictim seller with a certain value for a period of time. This allows the seller to recover hisutility loss due to lost opportunities caused by the misreported ratings. Experiments demonstrate the necessity and effectiveness of the proposed mechanism.

Truthful bidding is a desirable property for continuous double auctions (CDAs). Many incentive mechanisms have been proposed to elicit truthful bids. However, existing truthful CDA mechanisms often overlook the possibility that sellers may choose not to deliver the auctioned items to buyers as promised. In this situation, buyers may become unwilling to bid their true valuations in the future to compensate for their risks of being cheated, thereby rendering CDAs ineffective. In this paper, we propose a novel reputation-aware CDA (named RCDA) mechanism to consider the honesty of auction participants. It dynamically adjusts bids and asks according to the reputation of participants to reflect the risks involved in the transactions. Theoretical analysis proves that RCDA is effective in eliciting truthful bids from buyers and sellers in the presence of possible dishonest behavior from both buyers and sellers.

In e-marketplaces with limited inventory where buyers' demand is larger than sellers' supply, promoting honesty raises new challenges: sellers may behave dishonestly because they can sell out all products without the necessity of gaining high reputation; buyers may provide untruthful ratings to mislead other buyers in order to have a higher chance to obtain the limited products. In this paper, we propose a novel incentive mechanism to promote honesty in such e-marketplaces. More specifically, our mechanism models both buyer and seller honesty. It offers higher prices to the products provided by honest sellers so that the sellers can gain more profit. Honest buyers also have a higher chance to do business with honest sellers and are able to gain more utility. Theoretical analysis and experimental results show that our mechanism promotes both buyer and seller honesty. Finally, we address the re-entry problem by imposing membership fees on new sellers. We show that the membership fee can discourage sellers from re-entry both in theoretical analysis and experimental validation.