Papers
Topics
Authors
Recent
AI Research Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 89 tok/s
Gemini 2.5 Pro 43 tok/s Pro
GPT-5 Medium 24 tok/s Pro
GPT-5 High 24 tok/s Pro
GPT-4o 112 tok/s Pro
Kimi K2 199 tok/s Pro
GPT OSS 120B 449 tok/s Pro
Claude Sonnet 4 37 tok/s Pro
2000 character limit reached

Security: Doing Whatever is Needed... and Not a Thing More! (1802.08915v2)

Published 24 Feb 2018 in cs.CR

Abstract: As malware, exploits, and cyber-attacks advance over time, so do the mitigation techniques available to the user. However, while attackers often abandon one form of exploitation in favor of a more lucrative one, mitigation techniques are rarely abandoned. Mitigations are rarely retired or disabled since proving they have outlived their usefulness is often impossible. As a result, performance overheads, maintenance costs, and false positive rates induced by the different mitigations accumulate, culminating in an outdated, inefficient, and costly security solution. We advocate for a new kind of tunable framework on which to base security mechanisms. This new framework enables a more reactive approach to security allowing us to optimize the deployment of security mechanisms based on the current state of attacks. Based on actual evidence of exploitation collected from the field, our framework can choose which mechanisms to enable/disable so that we can minimize the overall costs and false positive rates while maintaining a satisfactory level of security in the system. We use real-world Snort signatures to simulate the benefits of reactively disabling signatures when no evidence of exploitation is observed and compare them to the costs of the current state of deployment. Additionally, we evaluate the responsiveness of our framework and show that in case disabling a security mechanism triggers a reappearance of an attack we can respond in time to prevent mass exploitation. Through large-scale simulations that use integer linear and Bayesian solvers, we discover that our responsive strategy is both computationally affordable and results in significant reductions in false positives (~20% over traces that are about 9 years long), at the cost of introducing a moderate number of false negatives. Finding the optimal sampling strategy takes less than 2.5 minutes in the vast majority of cases.

Citations (1)

Summary

We haven't generated a summary for this paper yet.

Lightbulb On Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.