AI Bibliography
AI Bibliography |
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Jana, I., & Oprea, A. 2019, Appmine: Behavioral analytics for web application vulnerability detection. Paper presented at Proceedings of the 2019 ACM SIGSAC Conference on Cloud Computing Security Workshop. |
| Resource type: Proceedings Article BibTeX citation key: Jana2019 View all bibliographic details  |
Categories: Artificial Intelligence, Cognitive Science, Computer Science, Data Sciences, Decision Theory, General Subcategories: Behavioral analytics, Big data, Decision making, Deep learning, Informatics, Machine learning, Neural nets, Psychology of human-AI interaction Creators: Jana, Oprea Publisher: Collection: Proceedings of the 2019 ACM SIGSAC Conference on Cloud Computing Security Workshop |
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| Abstract |
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Web applications in widespread use have always been the target of large-scale attacks, leading to massive disruption of services and financial loss, as in the Equifax data breach. It has become common practice to deploy web applications in containers like Docker for better portability and ease of deployment. We design a system called AppMine for lightweight monitoring of web applications running in Docker containers and detection of unknown web vulnerabilities. AppMine is an unsupervised learning system, trained only on legitimate workloads of web applications, to detect anomalies based on either traditional models (PCA and one-class SVM), or more advanced neural-network architectures (LSTM). In our evaluation, we demonstrate that the neural network model outperforms more traditional methods on a range of web applications and recreated exploits. For instance, AppMine achieves average AUC scores as high as 0.97 for the Apache Struts application (with the CVE-2017-5638 exploit used in the Equifax breach), while the AUC scores for PCA and one-class SVM are 0.81 and 0.83, respectively.
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