[
    {
        "id": "osp-19898",
        "type": "article-journal",
        "title": "Jev-IDS: System One Models for Network Intrusion Detection",
        "author": [
            {
                "family": "Severo",
                "given": "Paulo"
            },
            {
                "family": "Quincozes",
                "given": "Silvio E."
            },
            {
                "family": "Dias",
                "given": "Amanda"
            }
        ],
        "URL": "https://omanscience.com/en/articles/jev-ids-system-one-models-for-network-intrusion-detection",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Machine-learning Network Intrusion Detection Systems (IDS) depend on substantial labeled datasets and task-specific training, whereas Large Language Models (LLMs) detection can analyze flow records directly but incurs higher inference cost and latency, with less constrained outputs. This paper presents JEV-IDS, an open experimental general NIDS based on the Jev System One Model (SOM) to detect zero day intrusions Under label scarcity. JEV-IDS serializes one flow per request and asks JEV two questions: a binary attack probability and a finite-choice traffic category. Our results show that, at k=1, JEV was 4.8 times faster and 3.8 times cheaper than GPT-5.6 Luna, with 1.5 times higher novel-attack recall; it also produced 15 times fewer false alarms than a low-data Random Forest. Across 5,400 decisions on a 300-flow NSL-KDD pilot split, JEV achieved F1-Score 0.859, precision 0.941, recall 0.790, and novel-attack recall 0.838. Increasing k to 2 reduced its F1-Score to 0.839."
    }
]