Research

The Journal of Information Security and Applications (Volume 100, July 2026) published a paper titled "Anubis: A smart context-aware security model for access control" by Vítor Kehl Matter, Taimisson de Carvalho Schardosim, Márcio Garcia Martins and Jorge Luis Victória Barbosa. The source text provided contains only the bibliographic details and no description of the method or findings.

This article studies secure consensus in multiagent systems under hybrid cyber-attacks that combine deception attacks and denial-of-service (DoS) attacks in one attack-sequence model. The authors propose a fully distributed dynamic event-triggered control protocol that reduces communication use and avoids Zeno behavior. They quantify the maximum mismatch between attack durations and affected durations for each attack, and validate the approach with simulations of unmanned intelligent vehicles and a multirobot system.

ThreatMAMBA is a framework for Cyber Threat Attribution that builds a heterogeneous information graph from Indicators of Compromise (IOCs), Tactics, Techniques, and Procedures (TTPs), and temporal relationships in Cyber Threat Intelligence (CTI). It uses a state space selection mechanism and contrastive learning to construct temporal knowledge graphs, improving the Macro F1 score by 6.94% to 12.05% across event progression stages from 20% to 100%. Its attacker behavior profiles reach a Jaccard similarity of 0.2991 with MITRE ATT&CK, outperforming Qi'anxin by 0.1836.

This paper proposes RIBSC, a Robust Identity-based Signcryption scheme for VANETs, to address weaknesses in transmission robustness and identity privacy in existing schemes. It uses a Chinese Residual Theorem (CRT) based area session key distribution mechanism that can revoke malicious Roadside Units (RSUs) in real time, and a traceable pseudonym mechanism that protects vehicle and RSU identities while allowing true identities to be tracked when illegal activity occurs. The authors report a security proof under the random oracle model and experiments showing performance gains, with session information fixed at 148 bytes regardless of RSU count.

This paper presents a secure protocol for computing the Jaccard Coefficient over multisets, targeting collaborative data analysis and deduplication. Under the semi-honest adversarial model, the intersection and union cardinalities stay hidden from all parties, including the decryption key holder. The protocol runs at a linear cost of O(e_{m-1}) without accuracy loss, and a cloud-assisted encryption scheme improves efficiency by 25.5% to 30.4% over the non-cloud-assisted scheme.

DawnGuard is a framework for detecting encrypted malware traffic from the earliest stage of an attack, using multi-flow temporal graph learning. It applies a self-adjusting data augmentation strategy to early-stage traffic, builds Multi-Flow Graph Features (MGF), and feeds them to a Vision Transformer-based detector. Tested on two real-world datasets, it achieves an average F1 of 95.11%, 8.7% higher than the state-of-the-art method, using only the first 20% loading ratio of complete traffic.

The authors construct a threshold BBS+ signing protocol using verifiable multiplication-to-addition (MtA) techniques derived from vector oblivious linear evaluation (VOLE), with security proven in the Universal Composability (UC) framework. Building on this, they introduce T3AT, threshold-authorized and threshold-redeemable, non-transferable anonymous tokens that support collaborative issuance and verification under malicious adversaries and dishonest majorities, without trusted hardware or centralized authorities. The source reports that performance evaluation demonstrates practicality, efficiency and scalability.

The paper presents ESCM, a toolkit for outsourced computation under homomorphic encryption that lets servers run operations such as multiplication, division and sorting across different encrypted domains, so participants need not share one key. To resist collusion among servers and outages, the authors propose a distributed two trapdoor cryptosystem supporting (k,n) threshold decryption. Theoretical analysis, complexity comparisons with existing solutions, and simulation experiments are reported.

MTL-SEI, a multi-task learning framework for Specific Emitter Identification, addresses shortcut learning under cross-receiver distribution shifts, where networks rely on receiver-specific artifacts instead of transmitter fingerprints. It combines spectrum-based feature extraction, receiver-invariant adversarial training via a gradient reversal layer, equalization-state prediction, and uncertainty-guided task weighting. On the ManySig dataset under a receiver-disjoint protocol, it reaches 88.50% transmitter identification accuracy, a 37.7% improvement over the 1D-CNN baseline and over 6.92% average gain over state-of-the-art domain generalization methods.

The paper shows that two multi-client order-revealing encryption schemes, m-ORE and om-ORE, are vulnerable to a ciphertext-forgery attack. A colluding malicious client and server can silently inject counterfeit records into the encrypted dataset. The authors propose MORES, which they say preserves range-query functionality while provably resisting arbitrarily malicious participants and reduces query size and comparison cost by roughly one-third relative to both schemes.

This paper presents the first rigorous quantitative analysis of macro-level collaborative leakage, where individually harmless risk data reveal sensitive information when combined. The authors attribute the phenomenon to collaborative effects among pieces of risk data and formulate a sufficient condition for it. They show that Gaussian-distributed data can align correlations at both micro and macro levels, which helps prevent the leakage, but that this protection is inherently limited.

Researchers propose a secret-shared private set operation (PSO) framework for lightweight clients, aimed at cross-institution data matching and aggregation such as private set intersection and private set union. Existing delegated schemes impose heavy client-side masking and rely on one client as a computational leader, and the proposed scheme lets clients go offline while servers do all computation. The authors prove security under the semi-honest model and report that the protocol outperforms prior approaches when the set size is n ≥ 2^16.