Detection of Intrusions and Malware, and Vulnerability Assessment (eBook, PDF)

6th International Conference, DIMVA 2009, Milan, Italy, July 9-10, 2009. Proceedings
Redaktion: Flegel, Ulrich; Bruschi, Danilo

• Format: PDF

Produktbeschreibung

This book constitutes the refereed proceedings of the 6th International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, DIMVA 2009, held in Milan, Italy, in July 2009.

The 10 revised full papers presented together with three extended abstracts were carefully selected from 44 initial submissions. The papers are organized in topical sections on malware and SPAM, emulation-based detection, software diversity, harnessing context, and anomaly detection.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Springer Berlin Heidelberg
• Seitenzahl: 225
• Erscheinungstermin: 29. Juni 2009
• Englisch
• ISBN-13: 9783642029189
• Artikelnr.: 44127104

Inhaltsangabe

Malware and SPAM.
A Case Study on Asprox Infection Dynamics.
How Good Are Malware Detectors at Remediating Infected Systems?.
Towards Proactive Spam Filtering (Extended Abstract).
Emulation
Based Detection.
Shepherding Loadable Kernel Modules through On
demand Emulation.
Yataglass: Network
Level Code Emulation for Analyzing Memory
Scanning Attacks.
Defending Browsers against Drive
by Downloads: Mitigating Heap
Spraying Code Injection Attacks.
Software Diversity.
Polymorphing Software by Randomizing Data Structure Layout.
On the Effectiveness of Software Diversity: A Systematic Study on Real
World Vulnerabilities.
Harnessing Context.
Using Contextual Information for IDS Alarm Classification (Extended Abstract).
Browser Fingerprinting from Coarse Traffic Summaries: Techniques and Implications.
A Service Dependency Modeling Framework for Policy
Based Response Enforcement.
Anomaly Detection.
Learning SQL for Database Intrusion Detection Using Context
Sensitive Modelling (Extended Abstract).
Selecting and Improving System Call Models for Anomaly Detection.