Paper

Semantic Multi-Agent Intrusion Detection for IoT:Zero-Day and Adversarial Threats with Risk-Aware Reasoning

Unreadunread

AI summary

arXiv:2606. 10323v1 Announce Type: new Abstract: The rapid proliferation of Internet of Things (IoT) devices has enabled unprecedented automation and connectivity, but it has also substantially increased the attack surface, exposing networks to sophisticated cyber threats, including zero-day and adversarial intrusions.

Reading status

Priority

About this paper

Authors: Saeid Jamshidi
Released: 2026-06-10 (29d ago)
Topic: Related to current project
Relation to my work: Read next because Semantic Multi-Agent Intrusion Detection for IoT:Zero-Day and Adversarial Threats with Risk-Aware Reasoning overlaps with clean result "Only continuous soft prefixes hit both EM axes at once on Qwen-2.5-7B-Instruct: discrete prompt searches split between the alignment objective and the distributional objective, and both discretizations of the soft prefix collapse (MODERATE confidence)", clean result "Training one persona to emit a [ZLT] marker without bystanders adopting it has a one-cell-wide LR x epochs window on Qwen2.5-7B-Instruct (LOW confidence)", clean result "The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH confidence)". Matching terms: eval, source, rate, trained, stage, language, model. Source: arxiv cs.CR (Cryptography and Security).
arXiv: 2606.10323

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to Semantic Multi-Agent Intrusion Detection for IoT:Zero-Day and Adversarial Threats with Risk-Aware Reasoning. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.