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| * Anomaly Detection of Web-based Attacks. Kruegel, Vigna. ''Proceedings of CCS'' 2003 * Spectrogram: A Mixture-of-Markov-Chains Model for Anomaly Detection in Web Traffic. Song, Keromytis, Stolfo. ''Proceedings of NDSS'' 2009 ---- * '''Network Intrusion Detection''' * Detecting Unknown Network Attacks Using Language Models. Rieck, Laskov. ''Proceedings of DIMVA'' 2006 * McPAD: A Multiple Classifier System for Accurate Payload-Based Anomaly Detection. Perdisci, Ariu, Fogla, Giacinto, Lee. ''Computer Networks'' 2009 ---- * '''Analysis of Malicious Program Behavior''' * Scalable, Behavior-Based Malware Clustering. Bayer, Comparetti, Hlauscheck, Kruegel, Krida. ''Proceedings of NDSS'' 2009 * Learning and Classification of Malware Behavior. Rieck, Holz, Willems, Düssel, Laskov. ''Proceedings of DIMVA'' 2008 ---- * '''Analysis of Malicious Executables''' * Learning to Detect and Classify Malicious Executables in the Wild. Kolter and Maloof. ''Journal of Machine Learning Research'' 2006. * McBoost: Boosting Scalability in Malware Collection and Analysis Using Statistical Classification of Executables. Perdisci, Lanzi, Lee. ''Proceeding of ACSAC'' 2008 ---- * '''Mimicry Attacks''' * Polymorphic Blending Attacks. Fogla, Sharif, Perdisci, Kolesnikov, Lee. ''Proceedings of USENIX SS'' 2006 * English Shellcode. Mason, Small, Monrose, Macmanus. ''Proceedings of CCS'' 2009 ---- * '''Automatic Signature Generation''' * Polygraph: Automatically Generating Signatures for Polymorphic Worms. Newsome, Karp, Song. ''Proceedings of IEEE S&P'' 2005 * Hamsa: Fast Signature Generation for Zero-day Polymorphic Worms with Provable Attack Resilience. Li, Sanghi, Chen, Kao, Chavez. ''Proceedings of IEEE S&P'' 2006 ---- * '''Attacks against Signature Generation''' * Limits of Learning-based Signature Generation with Adversaries. Blum, Song. ''Proceedings of NDSS'' 2008 * Misleading Worm Signature Generators Using Deliberate Noise Injection. Perdicsi, Dagon, Lee, Fogla, Sharif. ''Proceedings of IEEE S&P'' 2006 |
Seminar: Machinelles Lernen in der IT-Sicherheit
Termine und Dozenten
Termin |
Vorbesprechung und Themenvergabe am X.X.2010 um 14:00 Uhr im Raum FR 6046 |
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Blockseminar am X.X.2010 von 10:00 bis 16:00 Uhr im Raum FR 6046 |
Verantwortlich |
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Ansprechpartner |
Inhalt
In diesem Seminar beschäftigen wir uns mit Anwendungen des maschinellen Lernens in der IT-Sicherheit.
Ablauf
- Die Vorbesprechung findet am X.X.2010 statt.
- Die Teilnehmer wählen bis spätestens X.X.2010 ein Thema in Absprache mit dem Betreuer (siehe Themenliste).
- Die Teilnehmer legen bis spätestens X.X.2010 einen Entwurf des Vortrages vor und besprechen diesen mit dem Betreuer.
- Das Seminar findet als Blockveranstaltung am X.X.2.2010 statt.
Themen
Die Vorträge sollen jeweils 35 Minuten (+ 10 Minuten Diskussion) dauern.
Detection of Web-based Attacks
Anomaly Detection of Web-based Attacks. Kruegel, Vigna. Proceedings of CCS 2003
Spectrogram: A Mixture-of-Markov-Chains Model for Anomaly Detection in Web Traffic. Song, Keromytis, Stolfo. Proceedings of NDSS 2009
Network Intrusion Detection
Detecting Unknown Network Attacks Using Language Models. Rieck, Laskov. Proceedings of DIMVA 2006
McPAD: A Multiple Classifier System for Accurate Payload-Based Anomaly Detection. Perdisci, Ariu, Fogla, Giacinto, Lee. Computer Networks 2009
Analysis of Malicious Program Behavior
Scalable, Behavior-Based Malware Clustering. Bayer, Comparetti, Hlauscheck, Kruegel, Krida. Proceedings of NDSS 2009
Learning and Classification of Malware Behavior. Rieck, Holz, Willems, Düssel, Laskov. Proceedings of DIMVA 2008
Analysis of Malicious Executables
Learning to Detect and Classify Malicious Executables in the Wild. Kolter and Maloof. Journal of Machine Learning Research 2006.
McBoost: Boosting Scalability in Malware Collection and Analysis Using Statistical Classification of Executables. Perdisci, Lanzi, Lee. Proceeding of ACSAC 2008
Mimicry Attacks
Polymorphic Blending Attacks. Fogla, Sharif, Perdisci, Kolesnikov, Lee. Proceedings of USENIX SS 2006
English Shellcode. Mason, Small, Monrose, Macmanus. Proceedings of CCS 2009
Automatic Signature Generation
Polygraph: Automatically Generating Signatures for Polymorphic Worms. Newsome, Karp, Song. Proceedings of IEEE S&P 2005
Hamsa: Fast Signature Generation for Zero-day Polymorphic Worms with Provable Attack Resilience. Li, Sanghi, Chen, Kao, Chavez. Proceedings of IEEE S&P 2006
Attacks against Signature Generation
Limits of Learning-based Signature Generation with Adversaries. Blum, Song. Proceedings of NDSS 2008
Misleading Worm Signature Generators Using Deliberate Noise Injection. Perdicsi, Dagon, Lee, Fogla, Sharif. Proceedings of IEEE S&P 2006
Leistungsnachweis, Anrechenbarkeit, Voraussetzungen
Die Note wird anhand des Vortrages und der Folien festgelegt. Das Seminar ist Wahlpflichtbestandteil der Master-Module "Maschinelles Lernen 1" und "Maschinelles Lernen 2". Bachelor-Studenten können diese Master-Module auf Antrag ebenfalls belegen. Wir empfehlen den vorherigen Besuch der Vorlesung "Maschinelles Lernen 1".