Addressing the issue of existing worm propagation models lacking proactive defense mechanisms against novel worm viruses within network attack environments, this paper establishes a propagation model based on dynamic honeypots, considering the proactive defense capabilities of honeypots and the network control flexibility offered by software-defined networking (SDN). To accurately predict worm behaviour, considering that actual worm attacks are not one-step, a new state named threatened state is introduced into the classical susceptible-infectious-recovered (SIR) model. Subsequently, to analyse the influence of SDN dynamic honeypots on worm propagation, a game state corresponding to the threatened state in real networks is incorporated, and the STIR-HB model is proposed. The equilibrium point and basic reproduction number of the model are calculated, and the stability of the equilibrium point is proved. This model provides a theoretical foundation for future applications in scenarios such as enterprise networks, cloud computing environments, and critical infrastructure systems.

Generalization remains a fundamental challenge in the field of Multi-Agent Reinforcement Learning (MARL). In real-world settings, agents frequently encounter changing environments and novel configurations of entities, resulting in unstable performance and significant degradation in Out-of-Distribution (OOD) scenarios. From the perspective of improving generalization, existing methods address this challenge through various approaches, such as dynamic policy adaptation, role specialization, and regularization techniques. Among them, dropout is a representative approach that enhances generalization by randomly omitting parts of the input during training. However, most existing dropout strategies overlook the varying importance of different entities, which can result in the removal of critical information and the retention of irrelevant observations, thereby limiting the model’s performance and stability in complex, dynamic environments. This oversight often results in unstable policies and limited generalization capabilities. In this work, we propose a new generalization method called Attribution-Guided Observation Dropout (AGOD). This method introduces an attribution coefficient to measure the contribution of each observed entity. It selectively drops those with higher attribution values during training, thereby encouraging agents to avoid over-reliance on key information and enhancing their generalization ability in complex environments. The proposed AGOD method considers entity importance, avoiding indiscriminate dropout, and shows sustained superior performance on the MPEV2 benchmark, proving its effectiveness in complex dynamic environments.

Decentralized Exchanges (DEX) allow cryptocurrencies to trade autonomously with each other without involving any centralized financial intermediaries. Among these DEX models, Automated Market Maker (AMM) is most commonly used by major platforms like Uniswap and Curve. However, a typical AMM suffers three main challenges. First, arbitrage trading may cause AMM-based liquidity providers to lose liquidity in assets. Second, adversaries extract on a monthly basis over 10 million USD from AMM traders via sandwich attacks. Third, the volatility of asset prices in AMM may violate the fairness of trading. In this work, we propose a new AMM design, Dynamic Curve-based Automated Market Maker (DCAMM), which utilizes a price oracle with real-time market price feedback to automatically adjust the pool's asset price to match the market price. In DCAMM, there is no space for price manipulation, and traders' slippage losses are converted into equal gains for the liquidity pool. Thus, DCAMM provides the resistance to arbitrage trading and sandwich attacks. Moreover, DCAMM provides a more stable asset price through a strict price adjustment, benefiting traders and safeguarding trading fairness.

Conditional privacy-preserving authentication can provide anonymity and traceability to Vehicular Ad-Hoc Networks (VANETs), which protects users' privacy while resisting malicious users and false messages. However, existing schemes suffer from various disadvantages, such as unavailable batch verification, unrenewable user public keys/certificates, and untimely revocation. In this work, we design an efficient conditional privacy-preserving authentication scheme with the on-chain key management (EAKM) for VANETs. To achieve lightweight authentication, we design an efficient Signature of Knowledge (SoK) and a batch verification algorithm. Moreover, we use the hash chain technology to update users' anonymous public keys. In addition, based on the blockchain technology and smart contract, we could manage users' anonymous public keys efficiently and transparently. Security analysis and simulation results show that EAKM ensures conditional privacy with less authentication overhead.

In this paper, we study the problem of global reward maximization with only partial distributed feedback. This problem is motivated by several real-world applications (e.g., cellular network configuration, dynamic pricing, and policy selection) where an action taken by a central entity influences a large population that contributes to the global reward. However, collecting such reward feedback from the entire population not only incurs a prohibitively high cost, but often leads to privacy concerns. To tackle this problem, we consider differentially private distributed linear bandits, where only a subset of users from the population are selected (called clients) to participate in the learning process and the central server learns the global model from such partial feedback by iteratively aggregating these clients’ local feedback in a differentially private fashion. We then propose a unified algorithmic learning framework, called differentially private distributed phased elimination (DP-DPE), which can be naturally integrated with popular differential privacy (DP) models (including central DP, local DP, and shuffle DP). Furthermore, we prove that DP-DPE achieves both sublinear regret and sublinear communication cost. Interestingly, DP-DPE also achieves privacy protection “for free” in the sense that the additional cost due to privacy guarantees is a lower-order additive term. Finally, we conduct simulations to corroborate our theoretical results and demonstrate the effectiveness of DP-DPE.

In this paper, we study the problem of fair worker selection in Federated Learning systems, where fairness serves as an incentive mechanism that encourages more workers to participate in the federation. Considering the achieved training accuracy of the global model as the utility of the selected workers, which is typically a monotone submodular function, we formulate the worker selection problem as a new multi-round monotone submodular maximization problem with cardinality and fairness constraints. The objective is to maximize the time-average utility over multiple rounds subject to an additional fairness requirement that each worker must be selected for a certain fraction of time. While the traditional submodular maximization with a cardinality constraint is already a well-known NP-Hard problem, the fairness constraint in the multi-round setting adds an extra layer of difficulty. To address this novel challenge, we propose three algorithms: Fair Continuous Greedy (FairCGl and FairCG2) and Fair Discrete Greedy (FairDG), all of which satisfy the fairness requirement whenever feasible. Moreover, we prove nontrivial lower bounds on the achieved time-average utility under FairCGl and FairCG2. In addition, by giving a higher priority to fairness, FairDG ensures a stronger short-term fairness guarantee, which holds in every round. Finally, we perform extensive simulations to verify the effectiveness of the proposed algorithms in terms of the time-average utility and fairness satisfaction.

The Age-of-Information is an important metric for investigating the timeliness performance in information-update systems. In this paper, we study the AoI minimization problem under a new Pull model with replication schemes, where a user proactively sends a replicated request to multiple servers to “pull” the information of interest. Interestingly, we find that under this new Pull model, replication schemes capture a novel tradeoff between different values of the AoI across the servers (due to the random updating processes) and different response times across the servers, which can be exploited to minimize the expected AoI at the user's side. Specifically, assuming Poisson updating process for the servers and exponentially distributed response time, we derive a closed-form formula for computing the expected AoI and obtain the optimal number of responses to wait for to minimize the expected AoI. Then, we extend our analysis to the setting where the user aims to maximize the AoI-based utility, which represents the user's satisfaction level with respect to freshness of the received information. Furthermore, we consider a more realistic scenario where the user has no prior knowledge of the system. In this case, we reformulate the utility maximization problem as a stochastic Multi-Armed Bandit problem with side observations and leverage a special linear structure of side observations to design learning algorithms with improved performance guarantees. Finally, we conduct extensive simulations to elucidate our theoretical results and compare the performance of different algorithms. Our findings reveal that under the Pull model, waiting does not necessarily lead to aging; waiting for more than one response can often significantly reduce the AoI and improve the AoI-based utility in most scenarios.

This paper investigates the sampled-delayed-databased consensus problem for upper-triangular multiagent nonlinear systems via output feedback. The sampled-delayed outputs are directly utilized to design observer-based piecewise continuous output feedback controllers. By properly constructing a Lyapunov-Krasovskii function, we obtain a parametric sequence and prove that it is convergent. Then, sufficient conditions are presented to guarantee the considered multiagent systems achieve global and exponential consensus. We also give the maximum admissible sampling period and transmission delay. Finally, simulation results show the validity and effectiveness of the newly proposed methods.

The multi-armed bandit (MAB) model has been widely adopted for studying many practical optimization problems (network resource allocation, ad placement, crowdsourcing, etc.) with unknown parameters. The goal of the player (i.e., the decision maker) here is to maximize the cumulative reward in the face of uncertainty. However, the basic MAB model neglects several important factors of the system in many realworld applications, where multiple arms (i.e., actions) can be simultaneously played and an arm could sometimes be “sleeping” (i.e., unavailable). Besides reward maximization, ensuring fairness is also a key design concern in practice. To that end, we propose a new Combinatorial Sleeping MAB model with Fairness constraints, called CSMAB-F, aiming to address the aforementioned crucial modeling issues. The objective is now to maximize the reward while satisfying the fairness requirement of a minimum selection fraction for each individual arm. To tackle this new problem, we extend an online learning algorithm, called Upper Confidence Bound (UCB), to deal with a critical tradeoff between exploitation and exploration and employ the virtual queue technique to properly handle the fairness constraints. By carefully integrating these two techniques, we develop a new algorithm, called Learning with Fairness Guarantee (LFG), for the CSMAB-F problem. Further, we rigorously prove that not only LFG is feasibility-optimal, but it also has a time-average regret upper bounded by N/2η + β1√mNT log T +β2N/T, where N is the total T number of arms, m is the maximum number of arms that can be simultaneously played, T is the time horizon, β1and β2are constants, and η is a design parameter that we can tune. Finally, we perform extensive simulations to corroborate the effectiveness of the proposed algorithm. Interestingly, the simulation results reveal an important tradeoff between the regret and the speed of convergence to a point satisfying the fairness constraints.

In compressive tracking algorithms, a feature reduction projection matrix is constructed by using compressed sensing theory. Target and non-target objects are discriminated by using a naive Bayesian classifier. Such an algorithm may ensure accuracy of target tracking in real-time. But it is not adaptive for tracking with respect to scales and rotations. In this paper, we propose a novel adaptive algorithm based on feature point matching for tracking objects which appear with various changes. We combine weight-average and improved compressive tracking algorithms together for tracking objects, then calculate the corresponding feature points between two subsequent frames of the same object for obtaining the target changes related to various scales and rotations. Our experimental results show that the improved algorithm effectively improves the accuracy of target tracking and ensures adaptability of the tracking algorithm.