Vor Ort
Merkzettel Warenkorb
  • Produktbild: Applications of Data Mining in Computer Security
  • Produktbild: Applications of Data Mining in Computer Security
Band 6

Applications of Data Mining in Computer Security

Aus der Reihe Advances in Information Security

97,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

23.10.2012

Abbildungen

XX, 252 p.

Herausgeber

Daniel Barbará + weitere

Verlag

Springer Us

Seitenzahl

252

Maße (L/B/H)

23,5/15,5/1,6 cm

Gewicht

423 g

Auflage

Softcover reprint of the original 1st edition 2002

Sprache

Englisch

ISBN

978-1-4613-5321-8

Beschreibung

Portrait

Dr. Sushil Jajodia is Professor and Chairman of the Dept. of Information and Software Engineering, and Director of the Center for Secure Information Systems at the George Mason University, Fairfax, Virginia, USA

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

23.10.2012

Abbildungen

XX, 252 p.

Herausgeber

Verlag

Springer Us

Seitenzahl

252

Maße (L/B/H)

23,5/15,5/1,6 cm

Gewicht

423 g

Auflage

Softcover reprint of the original 1st edition 2002

Sprache

Englisch

ISBN

978-1-4613-5321-8

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

Noch keine Bewertungen vorhanden

Verfassen Sie die erste Bewertung zu diesem Artikel

Helfen Sie anderen Kundinnen und Kunden durch Ihre Meinung.

Kundinnen und Kunden meinen

Bewertungen (0)
  • Produktbild: Applications of Data Mining in Computer Security
  • Produktbild: Applications of Data Mining in Computer Security
  • 1 Modern Intrusion Detection, Data Mining, and Degrees of Attack Guilt.- 1. Introduction.- 2. Detection Strategies.- 3. Data Sources.- 4. Degrees of Attack Guilt.- 5. Conclusion.- References.- 2 Data Mining for Intrusion Detection.- 1. Introduction.- 2. Data Mining Basics.- 3. Data Mining Meets Intrusion Detection.- 4. Observations on the State of the Art.- 5. Future Research Directions.- 6. Summary.- References.- 3 An Architecture for Anomaly Detection.- 1. Introduction.- 2. Architecture.- 3. ADAM: an implementation of the architecture.- 4. Experiences.- 5. Breaking the dependency on training data.- 6. Future.- References.- 4 A Geometric Framework for Unsupervised Anomaly Detection.- 1. Introduction.- 2. Unsupervised Anomaly Detection.- 3. A Geometric Framework for Unsupervised Anomaly Detection.- 4. Detecting Outliers in Feature Spaces.- 5. Algorithm 1: Cluster-based Estimation.- 6. Algorithm 2: K-nearest neighbor.- 7. Algorithm 3: One Class SVM.- 8. Feature Spaces for Intrusion Detection.- 9. Experiments.- 10. Discussion.- References.- 5 Fusing a Heterogeneous Alert Stream into Scenarios.- 1. Introduction.- 2. Fusion Approach.- 3. Architecture.- 4. Definitions.- 5. Probability Assignment.- 6. Experimental Results.- 7. System Benefits.- 8. Discussion and Summary.- References.- 6 Using MIB II Variables for Network Intrusion Detection.- 1. Introduction.- 2. Background.- 3. Model Construction.- 4. Experiments and Performance Evaluation.- 5. Discussion.- 6. Related Work.- 7. Conclusions and Future Work.- References.- 7 Adaptive Model Generation.- 1. Introduction.- 2. Components of Adaptive Model Generation.- 3. Capabilities of Adaptive Model Generation.- 4. Model Generation Algorithms.- 5. Model Generation Example: SVM.- 6. System Example 1: Registry Anomaly Detection.- 7. System Example 2: HAUNT.- 8. Conclusion.- References.- 8 Proactive Intrusion Detection.- 1. Introduction.- 2. Information Assurance, Data Mining, and Proactive Intrusion Detection.- 3. A methodology for discovering precursors - Assumptions, Ob-jectives, Procedure and Analysis.- 4. A Case Study - Precursor Rules for Distributed Denial of Ser-vice Attacks.- 5. Conclusions.- References.- 9 E-mail Authorship Attribution for Computer Forensics.- 1. Introduction and Motivation.- 2. Authorship Attribution.- 3. E-mail Authorship Attribution.- 4. Support Vector Machine Classifier.- 5. E-mail Corpus and Methodology.- 6. Results and Discussion.- 7. Conclusions.- References.