Internet of Things networks evolve as a rapidly growing field for security threats, such as Denial-of-Service cross-layer attacks, due to their heterogeneous and resource-constrained environment. Intrusion detection systems (IDSs) serve as a vital defense mechanism in modern cybersecurity. However, the adoption of such a system, especially one that adopts a cross-layer strategy, requires a standardized, multifaceted evaluation framework that accounts for both detection capability and operational overhead. To address these challenges, we proposed a modular weight-based framework that evaluates cross-layer Machine Learning (ML) IDS across multiple dimensions, namely, detection effectiveness and generalizability, data quality, and attack coverage and practical deployability. We then applied this framework to the state-of-the-art cross-layer ML IDSs identified through the PRISMA framework. This proof-of-concept application illustrates how current evaluation practices generate disparate, fragmented results, while also highlighting the limitations inherent in retrospective literature-based scoring.
Dimitrios Tasiopoulos, A. Xenakis, A. Lekidis et al.· Electronics· 0 citations
Swarms of LLM-assisted autonomous robots are increasingly proposed for cooperative intelligence, surveillance, and reconnaissance (ISR) in contested environments. A growing class of their assurance failures arises not within any single platform but across the swarm: individually-compliant actions compose into a mission-level violation: a prohibited objective split across platforms to evade per-platform lim- its, or a collective budget quietly exceeded. Per-platform guardrails miss these by construction, and contested communications let the violation hide behind lost or delayed evidence. We present a three-tier (platfor- m/squad/mission) compositional runtime-verification framework that de- composes a mission policy into per-agent and cross-agent aspects, aggre- gates per-platform verdicts over a verification-aware messaging fabric, and fuses them with an evidence-aware, two-axis (security x complete- ness) algebra whose provenance names the platforms that jointly trig- gered a violation. Because the fabric makes evidence loss and silence observable, unsupported negative verdicts are downgraded to an explicit unknown rather than reported as mission-wide all-clears. On a simulated ISR mission, an indirect prompt injection that causes real LLM planners to split a prohibited collection task across four platforms is invisible to every per-platform monitor yet detected compositionally with full prove- nance; under an injected fault campaign a best-effort central monitor emits silent false all-clears while the verification-aware fabric emits none
Nikolaos Kekatos, Stylianos Basagiannis, Panagiotis Katsaros et al.· 0 citations