September 2026

Advanced Persistent Threats (APTs) pose one of the most significant challenges in cyberspace security. They are characterized by stealthy and sustained efforts to steal high-value files from the endpo

Deep neural networks (DNNs) are vulnerable to backdoor/Trojan attacks, posing a severe threat to security-critical applications. In this paper, we investigate a scenario in which two different backdoo

The aim of a backdoor attack is to manipulate the behavior of a DNN under trigger-activated conditions by poisoning the dataset. Most backdoor detectors can be regarded as feature clustering methods a

Deep learning-based side-channel attack (DLSCA) has become the dominant paradigm for extracting sensitive information from hardware implementations due to its ability to learn discriminative features

Private Hash Matching (PHM) enables the detection of harmful media in end-to-end encrypted communication by comparing user media hashes against a server-side set of known harmful hashes, without expos

The coupling between radar and communication beams poses a central challenge to the beamforming design of covert integrated sensing and communication (ISAC) systems, as it distorts both the radar echo

To remove the contribution of specific data from the global model in federated learning, federated unlearning has recently emerged. Existing approaches face challenges such as reliance on full client

Dynamic Searchable Symmetric Encryption (DSSE) enables efficient searches over encrypted data but often suffers from search pattern leakage, allowing adversaries to infer sensitive information. While

In many secure multiparty computation (MPC) applications, the semantic domains of some operands are much smaller than the underlying arithmetic field or ring. The arithmetic domain is typically chosen

Deep Reinforcement Learning (DRL) has demonstrated remarkable capabilities in domains such as robotics, finance, and autonomous systems. With the increasing cost of training, DRL models are increasing