Intrusion recovery for database-backed web applications

Warp is a system that helps users and administrators of web applications recover from intrusions such as SQL injection, cross-site scripting, and clickjacking attacks, while preserving legitimate user changes. Warp repairs from an intrusion by rolling back parts of the database to a version before the attack, and replaying subsequent legitimate actions. Warp allows administrators to retroactively patch security vulnerabilities---i.e., apply new security patches to past executions---to recover from intrusions without requiring the administrator to track down or even detect attacks. Warp's time-travel database allows fine-grained rollback of database rows, and enables repair to proceed concurrently with normal operation of a web application. Finally, Warp captures and replays user input at the level of a browser's DOM, to recover from attacks that involve a user's browser. For a web server running MediaWiki, Warp requires no application source code changes to recover from a range of common web application vulnerabilities with minimal user input at a cost of 24--27% in throughput and 2--3.2 GB/day in storage.United States. Defense Advanced Research Projects Agency. Clean-slate design of Resilient, Adaptive, Secure Hosts (Contract N66001-10-2-4089)National Science Foundation (U.S.) (Award CNS-1053143)Quanta Computer (Firm)Google (Firm)Samsung Scholarship Foundatio

Asynchronous intrusion recovery for interconnected web services

Recovering from attacks in an interconnected system is difficult, because an adversary that gains access to one part of the system may propagate to many others, and tracking down and recovering from such an attack requires significant manual effort. Web services are an important example of an interconnected system, as they are increasingly using protocols such as OAuth and REST APIs to integrate with one another. This paper presents Aire, an intrusion recovery system for such web services. Aire addresses several challenges, such as propagating repair across services when some servers may be unavailable, and providing appropriate consistency guarantees when not all servers have been repaired yet. Experimental results show that Aire can recover from four realistic attacks, including one modeled after a recent Facebook OAuth vulnerability; that porting existing applications to Aire requires little effort; and that Aire imposes a 19--30% CPU overhead and 6--9 KB/request storage cost for Askbot, an existing web application.National Science Foundation (U.S.) (NSF award CNS-1053143)United States. Defense Advanced Research Projects Agency (DARPA Clean-slate design of Resilient, Adaptive, Secure Hosts (CRASH) program under contract #N66001-10-2-4089

Automated intrusion recovery for web applications

Thesis (Ph. D.)--Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 93-97).In this dissertation, we develop recovery techniques for web applications and demonstrate that automated recovery from intrusions and user mistakes is practical as well as effective. Web applications play a critical role in users' lives today, making them an attractive target for attackers. New vulnerabilities are routinely found in web application software, and even if the software is bug-free, administrators may make security mistakes such as misconfiguring permissions; these bugs and mistakes virtually guarantee that every application will eventually be compromised. To clean up after a successful attack, administrators need to find its entry point, track down its effects, and undo the attack's corruptions while preserving legitimate changes. Today this is all done manually, which results in days of wasted effort with no guarantee that all traces of the attack have been found or that no legitimate changes were lost. To address this problem, we propose that automated intrusion recovery should be an integral part of web application platforms. This work develops several ideas-retroactive patching, automated UI replay, dependency tracking, patch-based auditing, and distributed repair-that together recover from past attacks that exploited a vulnerability, by retroactively fixing the vulnerability and repairing the system state to make it appear as if the vulnerability never existed. Repair tracks down and reverts effects of the attack on other users within the same application and on other applications, while preserving legitimate changes. Using techniques resulting from these ideas, an administrator can easily recover from past attacks that exploited a bug using nothing more than a patch fixing the bug, with no manual effort on her part to find the attack or track its effects. The same techniques can also recover from attacks that exploit past configuration mistakes-the administrator only has to point out the past request that resulted in the mistake. We built three prototype systems, WARP, POIROT, and AIRE, to explore these ideas. Using these systems, we demonstrate that we can recover from challenging attacks in real distributed web applications with little or no changes to application source code; that recovery time is a fraction of the original execution time for attacks with a few affected requests; and that support for recovery adds modest runtime overhead during the application's normal operation.by Ramesh Chandra.Ph.D

Monitoring and analysis system for performance troubleshooting in data centers

It was not long ago. On Christmas Eve 2012, a war of troubleshooting began in Amazon data centers. It started at 12:24 PM, with an mistaken deletion of the state data of Amazon Elastic Load Balancing Service (ELB for short), which was not realized at that time. The mistake first led to a local issue that a small number of ELB service APIs were affected. In about six minutes, it evolved into a critical one that EC2 customers were significantly affected. One example was that Netflix, which was using hundreds of Amazon ELB services, was experiencing an extensive streaming service outage when many customers could not watch TV shows or movies on Christmas Eve. It took Amazon engineers 5 hours 42 minutes to find the root cause, the mistaken deletion, and another 15 hours and 32 minutes to fully recover the ELB service. The war ended at 8:15 AM the next day and brought the performance troubleshooting in data centers to world’s attention. As shown in this Amazon ELB case.Troubleshooting runtime performance issues is crucial in time-sensitive multi-tier cloud services because of their stringent end-to-end timing requirements, but it is also notoriously difficult and time consuming. To address the troubleshooting challenge, this dissertation proposes VScope, a flexible monitoring and analysis system for online troubleshooting in data centers. VScope provides primitive operations which data center operators can use to troubleshoot various performance issues. Each operation is essentially a series of monitoring and analysis functions executed on an overlay network. We design a novel software architecture for VScope so that the overlay networks can be generated, executed and terminated automatically, on-demand. From the troubleshooting side, we design novel anomaly detection algorithms and implement them in VScope. By running anomaly detection algorithms in VScope, data center operators are notified when performance anomalies happen. We also design a graph-based guidance approach, called VFocus, which tracks the interactions among hardware and software components in data centers. VFocus provides primitive operations by which operators can analyze the interactions to find out which components are relevant to the performance issue. VScope’s capabilities and performance are evaluated on a testbed with over 1000 virtual machines (VMs). Experimental results show that the VScope runtime negligibly perturbs system and application performance, and requires mere seconds to deploy monitoring and analytics functions on over 1000 nodes. This demonstrates VScope’s ability to support fast operation and online queries against a comprehensive set of application to system/platform level metrics, and a variety of representative analytics functions. When supporting algorithms with high computation complexity, VScope serves as a ‘thin layer’ that occupies no more than 5% of their total latency. Further, by using VFocus, VScope can locate problematic VMs that cannot be found via solely application-level monitoring, and in one of the use cases explored in the dissertation, it operates with levels of perturbation of over 400% less than what is seen for brute-force and most sampling-based approaches. We also validate VFocus with real-world data center traces. The experimental results show that VFocus has troubleshooting accuracy of 83% on average.Ph.D

Data recovery for web applications

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