Research

Hui Tian, Haiju Wang, Shufan Fei and Hanyu Quan published TMAS in Computers & Security, available online 6 June 2026. The source text provided is only publication metadata (date, source and authors) and does not describe the scheme, method or findings.

Alessio Izzillo, Riccardo Lazzeretti and Emilio Coppa published STAFF in Computers & Security, available online 6 June 2026. The source text provides only the title, publication details and authors, so the research question, method and findings cannot be described from it.

Rinieri, Iacobelli, Melis, Prandini and Callegati published PLC-Defuser in Computers & Security, Volume 169, in October 2026. The source text provided contains only the publication metadata, so the research question, method and findings cannot be stated from it.

The source is a bibliographic record for a Computers & Security article, Volume 169, published October 2026, by Chengliang Gao, Jing Qiu, Yifei Sun, Du Cheng and Lihua Yin. The title indicates a method for generating logical attack graphs for enterprise networks from endpoint data, but the source text gives no method details, results or numbers.

Researchers Jiří Gavenda and Marek Sýs published CoolTest in Computers & Security, Volume 170, in November 2026. The source text provided gives no further details of the method or findings.

NCorr-FP is a fingerprinting system for structured tabular data that embeds recipient-specific marks while preserving statistical fidelity. It uses local record similarity and density estimation to guide where fingerprint bits are inserted, then reverses that logic to extract the fingerprint from a possibly modified dataset. Experiments report near-imperceptible fingerprints (minute Hellinger distances and KL divergences), 100% detection confidence under substantial data deletions, and robustness against adaptive and collusion attacks.

BAVote is a blockchain-based electronic voting system that provides both voter anonymity and traceability. It is built on ConsATS, a threshold signature scheme with a constant-size verification key that compresses all voters' public keys into one. The prototype, tested on a local blockchain and the Ethereum Sepolia testnet, cut verification-key storage cost by more than 90% and sped up verification 7x compared with existing schemes.

Researchers Diksha Goel, Kristen Moore, Jeff Wang, Minjune Kim and Thanh Thi Nguyen published a multi-layer framework for explaining reinforcement learning-based cyber agents in the Journal of Information Security and Applications, Volume 101, in September 2026. The source text provided contains only publication metadata, so the study's method and findings cannot be described from it.

Three cybersecurity experts from academia and industry discuss why many phishing training programs fail to change behavior. They also consider how to measure training effectiveness meaningfully. The discussion covers strategies for improving organizational resilience against current and future phishing threats.

Researchers propose a disentangled representation-learning framework based on a Variational Autoencoder (VAE) to suppress identity-revealing cues in teleoperation motion streams while keeping task-relevant motion patterns. Evaluated offline on reconstructed trajectories from a tele-robotic prototype with multiple users performing manipulation tasks, the approach substantially reduces re-identification risk and offers a favorable privacy-utility trade-off in task utility.

This paper proposes QS-BTrust, a quantum-secure, privacy-preserving authenticated broadcast protocol for Integrated Vehicular Networks. It combines Physical Unclonable Functions, post-quantum digital signatures, and Hashgraph-based authentication tag management. The authors analyze it under the Dolev–Yao adversary model, validate it with ProVerif, and assess it under the Random Oracle Model and Quantum Random Oracle Model, reporting lower verification overhead under high message loads than representative schemes in simulation.

Researchers address partially spoofed speech detection, where an utterance mixes fake and bona fide segments and no segment-level labels exist. They propose FMG, a Fake segment Mining based Graph neural network that models inter-segment temporal dependence through ATD and ATC branches and identifies representative fake segments with an FSM module using attentive pooling and prototype and entropy losses. Experiments on spoofed-speech datasets show it outperforms compared models.