Quantitative system performance: computer system analysis using queueing network models
Quantitative system performance: computer system analysis using queueing network models
Asymptotic analysis of multiclass closed queueing networks: multiple bottlenecks
Performance Evaluation
Capacity planning for Web performance: metrics, models, and methods
Capacity planning for Web performance: metrics, models, and methods
Mean-Value Analysis of Closed Multichain Queuing Networks
Journal of the ACM (JACM)
Performance bound hierarchies for queueing networks
ACM Transactions on Computer Systems (TOCS)
Designing Process Replication and Activation: A Quantitative Approach
IEEE Transactions on Software Engineering
Protecting web servers from distributed denial of service attacks
Proceedings of the 10th international conference on World Wide Web
Simple analytic modeling of software contention
ACM SIGMETRICS Performance Evaluation Review
Scaling for E Business: Technologies, Models, Performance, and Capacity Planning
Scaling for E Business: Technologies, Models, Performance, and Capacity Planning
IEEE Transactions on Software Engineering
Performance Engineering of Component-Based Distributed Software Systems
Performance Engineering, State of the Art and Current Trends
Balanced job bound analysis of queueing networks
SIGMETRICS '81 Proceedings of the 1981 ACM SIGMETRICS conference on Measurement and modeling of computer systems
Using graphic turing tests to counter automated DDoS attacks against web servers
Proceedings of the 10th ACM conference on Computer and communications security
A method for evaluating the impact of software configuration parameters on e-commerce sites
Proceedings of the 5th international workshop on Software and performance
Hierarchical model-based autonomic control of software systems
DEAS '05 Proceedings of the 2005 workshop on Design and evolution of autonomic application software
Tracking time-varying parameters in software systems with extended Kalman filters
CASCON '05 Proceedings of the 2005 conference of the Centre for Advanced Studies on Collaborative research
The Use of Optimal Filters to Track Parameters of Performance Models
QEST '05 Proceedings of the Second International Conference on the Quantitative Evaluation of Systems
Guerrilla Capacity Planning: A Tactical Approach to Planning for Highly Scalable Applications and Services
A performance analysis method for autonomic computing systems
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
A framework for measurement based performance modeling
WOSP '08 Proceedings of the 7th international workshop on Software and performance
Modeling the effect of application server settings on the performance of J2EE web applications
TEAA'06 Proceedings of the 2nd international conference on Trends in enterprise application architecture
Early DoS/DDoS Detection Method using Short-term Statistics
CISIS '10 Proceedings of the 2010 International Conference on Complex, Intelligent and Software Intensive Systems
Using Load Tests to Automatically Compare the Subsystems of a Large Enterprise System
COMPSAC '10 Proceedings of the 2010 IEEE 34th Annual Computer Software and Applications Conference
Tracking adaptive performance models using dynamic clustering of user classes
Proceedings of the 2nd ACM/SPEC International Conference on Performance engineering
Autonomic load-testing framework
Proceedings of the 8th ACM international conference on Autonomic computing
Launching distributed denial of service attacks by network protocol exploitation
AICT'11 Proceedings of the 2nd international conference on Applied informatics and computing theory
Review: A survey of intrusion detection techniques in Cloud
Journal of Network and Computer Applications
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Denial of Service (DoS) attacks overwhelm online services, preventing legitimate users from accessing a service, often with impact on revenue or consumer trust. Approaches exist to filter network-level attacks, but application-level attacks are harder to detect at the firewall. Filtering at this level can be computationally expensive and difficult to scale, while still producing false positives that block legitimate users. This article presents a model-based adaptive architecture and algorithm for detecting DoS attacks at the web application level and mitigating them. Using a performance model to predict the impact of arriving requests, a decision engine adaptively generates rules for filtering traffic and sending suspicious traffic for further review, where the end user is given the opportunity to demonstrate they are a legitimate user. If no legitimate user responds to the challenge, the request is dropped. Experiments performed on a scalable implementation demonstrate effective mitigation of attacks launched using a real-world DoS attack tool.