Electronic countermeasure (ECM) is crucial for preventing target exposure. However, traditional ECM technologies may exhibit limited adaptability or incur high hardware costs, and may even risk exposing the target. To tackle the above challenges, we propose in this paper an intelligent reflecting surface (IRS)-aided electromagnetic spoofing system, where an IRS is mounted on the target to redirect the reflected signals toward surrounding clusters to create decoy targets for confusing radar detection, while simultaneously making the genuine target invisible to radars. Specifically, we optimize the IRS reflection to maximize the sum received signal power at all radars from the cluster direction and ensure that each radar’s received signal power from the target direction remains below a given detection threshold. For the single-radar and single-cluster setup, we solve the IRS reflection optimization problem by using the Lagrange multiplier method and derive a semi-closed-form optimal solution, which is then generalized to the multi-radar and multi-cluster case. Furthermore, a spoofing performance upper bound for the single-radar case is obtained based on the phase alignment method, and a low-complexity closed-form solution based on minimum mean-square error (MMSE) criteria is developed for the multi-radar case. Additionally, we propose practical low-complexity estimation schemes at the target to acquire angle-of-arrival (AoA) and/or signal power gain information via a small number of receive sensing devices. Simulation results validate the performance advantages of our proposed IRS-aided electromagnetic spoofing system with the proposed IRS reflection designs, as compared to the baseline systems.

Federated learning effectively safeguards data privacy by enabling local model training across multiple clients. However, it remains susceptible to label flipping attacks, which can significantly degrade the global model’s performance, even when the proportion of malicious clients is small. Existing defense methods often rely on assumptions regarding client data distribution or attacker proportions, which limits their effectiveness in real-world scenarios characterized by data heterogeneity and unknown attack scale. This paper introduces a novel and robust defense mechanism, FedDLFA. FedDLFA is grounded in a key insight: Due to the adversarial nature of the training objective, malicious clients exhibit significantly distinct neural activation patterns under standardized inputs compared to normal clients. FedDLFA extracts neural activation vectors from all client models, calculates their cosine similarity, constructs a similarity matrix, and applies clustering techniques to divide clients into two groups. Subsequently, it employs a density-size joint scoring mechanism to identify potential clusters of malicious clients. Experiments conducted on the MNIST, FMNIST, and CIFAR10 datasets, under both IID and Non-IID settings, demonstrate that FedDLFA achieves superior accuracy and effectively mitigates attack success rates compared to existing state-of-the-art methods.

Android’s dominant market share has made it a prime target for malware attacks. Although machine learning-based detection systems have demonstrated effectiveness, they remain vulnerable to adversarial attacks, which modify samples to preserve malicious functionality while evading detection. Adversarial training is a prevalent defense strategy. However, generating effective adversarial examples for Android malware is challenging due to the complex mapping between feature and problem space. To address this, recent efforts have explored feature-space attacks constrained by statistical dependencies. Yet, such approaches inherently rely on large-scale datasets to achieve strong performance, and may fail to capture the underlying semantic relationships among features, like call associations. In this paper, we propose a novel method that incorporates semantic dependencies, i.e., API dependencies extracted from function call graphs of APKs. By leveraging these dependencies as domain constraints, our method preserves intrinsic call associations among features during perturbation. This leads to adversarial examples that more closely reflect realistic attack behaviors. Furthermore, a reinforcement learning-based mechanism is employed to enhance the evasive capability of the generated adversarial samples against detection models. The resulting adversarial samples are leveraged for adversarial training to enhance detector robustness. Experimental results demonstrate that the adversarial examples generated by our approach effectively enhance model robustness via adversarial training, yielding superior resilience in realistic adversarial environments. In adversarial attack scenarios, the proposed method attains the highest detection accuracy against problem-space attacks, surpassing the baseline model without adversarial training by 45.7% and 14.3%, respectively. Moreover, our method significantly reduces the average generation time by 83.5% compared to problem-space adversarial example generation approaches.

Summary Key revocation and ciphertext update are two critical issues for identity‐based encryption schemes. Designing an identity‐based encryption scheme with key revocation and ciphertext update functionalities simultaneously is still a tricky challenge. Recently, Liang et al. introduce the notion of cloud‐based revocable identity‐based proxy re‐encryption scheme and present a concrete scheme with aim to solve the challenge. In this paper, we first showed the scheme of Liang et al. cannot resist re‐encryption key forgery attack and collusion attack. We then introduced a new cryptographic primitive, named cloud‐aided revocable identity‐based encryption scheme with ciphertext update (CA‐RIBE‐CU), to achieve both ciphertext update and key revocation for identity‐based encryption schemes. We also defined the syntax and security model of CA‐RIBE‐CU scheme and proposed a CA‐RIBE‐CU scheme from bilinear pairings. Compared with the scheme of Liang et al., our proposed scheme is collusion resistant, takes lower decryption computation, and achieves constant size re‐encrypted ciphertext. Finally, we proved the proposed scheme is adaptively secure under the decisional bilinear Diffie–Hellman assumption in the standard model. Copyright © 2016 John Wiley & Sons, Ltd.