LEADER 01711oam  2200373z- 450 
001 9910805897403321
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010   $a9783731512578
010   $a3731512572
035   $a(CKB)4590000000000414
035   $a(EXLCZ)994590000000000414
100   $a20250225c2023uuuu  -u-         -
101 0 $aeng
200 10$aSelf-learning Anomaly Detection in Industrial Production
210   $cKIT Scientific Publishing
225   $aKarlsruher Schriften zur Anthropomatik, 59
330   $aConfiguring an anomaly-based Network Intrusion Detection System for cybersecurity of an industrial system in the absence of information on networking infrastructure and programmed deterministic industrial process is challenging. Within the research work, different self-learning frameworks to analyze passively captured network traces from PROFINET-based industrial system for protocol-based and process behavior-based anomaly detection are developed, and evaluated on a real-world industrial system.
606   $aComputing and information technology
606   $aComputer science
606   $aMathematical theory of computation
606   $aIndustrial control systems
606   $aNetwork security
606   $aNetwork intrusion detection systems
615   $aComputing and information technology
615   $aComputer science
615   $aMathematical theory of computation
615   $aIndustrial control systems
615   $aNetwork security
615   $aNetwork intrusion detection systems
701   $aMeshram$b Ankush$01592469
906   $aBOOK
912   $a9910805897403321
996   $aSelf-learning Anomaly Detection in Industrial Production$93910293
997   $aUNINA