Search: “benchmarks”

6 stories in the last 24h

An editorial scorecard ranks the top ten Google Cloud security tools for 2026, with Wiz, Sysdig, and Security Command Center leading on correlation, GKE runtime, and native depth.

The roundup evaluates ten GCP security tools across five weighted criteria including GCP-native depth, correlation, runtime protection, multicloud parity, and value. Google Security Command Center is positioned as the included native floor, while Wiz and Sysdig top the weighted scores at 4.50, followed by Prisma Cloud at 4.40. The piece notes Forseti is deprecated and that Lacework is now Fortinet's FortiCNAPP, and flags diligence around Google's acquisition of Wiz. It is an editorial assessment, not a lab test, with pricing compared by model only.

Cyber Security News08:37 UTC · 3h agoIndustry

Top 10 Best Microsoft Azure Security Tools in 2026

An editorial roundup of the ten best Azure security tools in 2026 names Defender for Cloud the native floor, with Wiz, Orca, and Prisma Cloud leading third-party options.

The brief reviews ten Azure security tools, positioning Microsoft Defender for Cloud's free foundational tier and published per-resource plans as the rational starting point for every Azure estate. Wiz and Orca are shortlisted for agentless attack-path correlation, Prisma Cloud for multicloud breadth, CrowdStrike for runtime protection, and Tenable for CIEM depth with Entra permission analytics. The piece emphasizes Azure's tight identity-infrastructure coupling via Entra ID as both an operational advantage and its most critical risk surface. Scoring is editorial, not lab-tested, with pricing compared by model only.

Cyber Security News08:31 UTC · 3h agoIndustry

Cyber Adversary Simulation (CyAS): scheme documents now available

NCSC published adversary simulation guidance and the first Cyber Adversary Simulation (CyAS) scheme documents ahead of the scheme's November 2026 launch.

The UK NCSC released guidance on adversary simulation engagements and the first CyAS scheme documents, including the Scheme Standard and the Working Practices Document. The capability-led scheme, developed with cyber oversight bodies, will formally launch in November 2026 and assess companies seeking NCSC-assured provider status. NCSC describes the current version as a minimum viable product to be refined with feedback from buyers and providers.

NCSC UK09:09 UTC · 2h agoAdvisory 2 sources

Differential Trust: Dynamic Multi-Authority Anonymous Credentials with Epoch-Weighted Updates

Researchers propose MA-ACEW, the first multi-authority anonymous credential model with epoch-weighted issuance and efficient cross-epoch credential updates.

The paper introduces MA-ACEW, a multi-authority anonymous credential scheme that weights authorities differently during credential issuance, targeting decentralized systems such as Proof-of-Stake networks. Its core primitive, Epoch-Bound Pointcheval-Sanders Signatures (EB-PS), binds signatures to time epochs, enabling non-interactive credential updates when authority weight distributions change. The authors formalize EUF-eCMA unforgeability and prove unforgeability, anonymity, and blindness under a novel STB-GPS assumption. Aggregating a credential from 128 partial credentials takes about 10.68 ms on average.

arXiv cs.CRSep 16, 15:21 UTC · 20h agoResearch

s-MDM: Generative Virtualization of Multi-Device Hardware Variations for Portable DL-SCA

Researchers present s-MDM, a generative framework synthesizing virtual device profiles to improve cross-device portability of deep learning side-channel analysis.

The poster introduces the Synthetic Multiple Device Model (s-MDM), a zero-target-trace generative framework addressing performance degradation of deep learning side-channel analysis on unseen hardware. It combines a structured cVAE generator, Walsh-Hadamard leakage anchors, continuous style modulation, and decoupled leakage-style-domain critics to synthesize virtual source-device profiles offline. Benchmarked on 32-bit AES_PTv2 traces, s-MDM achieves consistently low key rank on layout- and acquisition-shifted Pinata targets where physical baselines are unstable.

arXiv cs.CRSep 16, 15:04 UTC · 20h agoResearch

Echo: Learning-based Matching Decompilation using Trusted Back Translation

Researchers introduce Echo, a matching decompiler using trusted back-translation that roughly doubles exact-match rates and outperforms GPT-5.6 and Codex on Mirai.

Echo performs matching decompilation by using compilation as trusted feedback for iterative search: a domain-specific model generates candidate code and compilation configurations, which are recompiled, compared at assembly level, and repaired via rule-based rewriting, neural refinement, and reasoning-based refinement. On function-level benchmarks, Echo produces 2.43x more exact matches than the strongest baseline and the highest structural similarity to ground truth. On the Mirai malware binary, it matches 2.75x and 7.4x as many functions as GPT-5.6 and Codex, respectively.

arXiv cs.CRSep 16, 14:14 UTC · 21h agoResearch1