Malware like botnets typically uses domain generation algorithms (DGAs) to dynamically produce a large number of random algorithmically generated domains (AGDs) and use a few of them to communicate with the command and control servers. AGD detection
{
"title_en": "Machine Learning-Based Algorithmically Generated Domain Detection",
"designation": "",
"series": "",
"status": "",
"date": "2022-05-01",
"date_original": "May 1, 2022",
"url": "https://www.nist.gov/publications/machine-learning-based-algorithmically-generated-domain-detection",
"source": "NIST Publications",
"display_en": "",
"authors_en": "Zheng Wang , Yang Guo , Douglas Montgomery",
"abstract_excerpt_en": "Malware like botnets typically uses domain generation algorithms (DGAs) to dynamically produce a large number of random algorithmically generated domains (AGDs) and use a few of them to communicate with the command and control servers. AGD detection",
"id": "cbcb153ddfd823bf420c",
"sources": [
"NIST Publications"
],
"source_records": [
{
"title_en": "Machine Learning-Based Algorithmically Generated Domain Detection",
"designation": "",
"series": "",
"status": "",
"date": "2022-05-01",
"date_original": "May 1, 2022",
"url": "https://www.nist.gov/publications/machine-learning-based-algorithmically-generated-domain-detection",
"source": "NIST Publications",
"display_en": "",
"authors_en": "Zheng Wang , Yang Guo , Douglas Montgomery",
"abstract_excerpt_en": "Malware like botnets typically uses domain generation algorithms (DGAs) to dynamically produce a large number of random algorithmically generated domains (AGDs) and use a few of them to communicate with the command and control servers. AGD detection"
}
],
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}