HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 9 - Volume 9
A Fast Automaton-Based Method for Detecting Anomalous Program Behaviors
SP '01 Proceedings of the 2001 IEEE Symposium on Security and Privacy
Shield: vulnerability-driven network filters for preventing known vulnerability exploits
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
A Dynamic Technique for Eliminating Buffer Overflow Vulnerabilities (and Other Memory Errors)
ACSAC '04 Proceedings of the 20th Annual Computer Security Applications Conference
Address obfuscation: an efficient approach to combat a board range of memory error exploits
SSYM'03 Proceedings of the 12th conference on USENIX Security Symposium - Volume 12
StackGuard: automatic adaptive detection and prevention of buffer-overflow attacks
SSYM'98 Proceedings of the 7th conference on USENIX Security Symposium - Volume 7
Automatic diagnosis and response to memory corruption vulnerabilities
Proceedings of the 12th ACM conference on Computer and communications security
Fast and Black-box Exploit Detection and Signature Generation for Commodity Software
ACM Transactions on Information and System Security (TISSEC)
MEDS: The Memory Error Detection System
ESSoS '09 Proceedings of the 1st International Symposium on Engineering Secure Software and Systems
HotSec'09 Proceedings of the 4th USENIX conference on Hot topics in security
Attribution of malicious behavior
ICISS'10 Proceedings of the 6th international conference on Information systems security
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Buffer overflows have become the most common target for network-based attacks. They are also the primary propagation mechanism used by worms. Although many techniques (such as StackGuard) have been developed to protect servers from being compromised by buffer overflow attacks, these techniques cause the server to crash. In the face of automated, repetitive attacks such as those due to worms, these protection mechanisms lead to repeated restarts of the victim application, rendering its service unavailable. In contrast, we present a promising new approach that learns the characteristics of inputs associated with attacks, and filters them out in the future. It can be implemented without changing the server code, or even having access to its source. Since attack-bearing inputs are dropped before they corrupt the victim process, there is no need to restart the victim; as a result, recovery from attacks can be very fast. We tested our approach on 8 buffer overflow attacks reported in the past few years on securityfocus.com and were available with working exploit code, and found that it generated accurate filters for 7 out of these 8 attacks.