XAI and Statistical Analysis for Reliable Intrusion Detection in the UAVIDS-2025 Dataset: From Tree to Hybrid and Tabular DNN Ensembles
2026-05-15T07:23:33Z•8b088a39f764b126ff04c9af5509b8d4f8fd4b69ce12cb79382a5be6fc2c7526
AgentTrapBRICKSTORMDSTAN-MedExploitBenchGo malwareICS/IIoTIoMTLLM agentsObscuraPanteganaSHAPUAV intrusion detectionUAVIDS-2025V8 bugsVolatility 3XAIcapability ladderdarknet trafficexploit benchmarkfalse data injectionmemory forensicsphysiological plausibilityreconnaissance evasion','homomorphic encryption','encrypted控制','supply-chainthird-party skills
What happened
Collection of recent security research papers covering multiple emerging risks and defenses: explainable ML for UAV intrusion detection (UAVIDS-2025) identifying feature-level causes of Wormhole/Blackhole misclassifications; AgentTrap, a dynamic benchmark showing third‑party LLM skills can embed malicious runtime workflows that evade simple jailbreak detection; a Volatility 3 memory‑forensics framework for Go malware that reliably recovers runtime artifacts (C2 endpoints, keys, persistence) from samples like BRICKSTORM/Obscura/Pantegana; ExploitBench, a capability‑graded benchmark on 41 V8bugs
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_cr
- Record identifier
- 8b088a39f764b126ff04c9af5509b8d4f8fd4b69ce12cb79382a5be6fc2c7526
- Enrichment time
- 2026-05-15T07:23:33Z
- AI-assisted enrichment
- Yes
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