API Gateway Threat Prevention in Large-Scale Applications
DOI:
https://doi.org/10.56830/IJSIE202411Keywords:
API gateway, Threat prevention, Real-time risk scoring, Policy enforcement, False Block Rate (FBR).Abstract
WAFs and API gateways are the lowest common denominator in every transaction and, therefore, the logical place to have the backend integrate threat prevention that is made scalable. This paper focuses on proactive, synchronous measures: driver block, driver challenge, rate-limit, sandbox, and step-up reauthentication, as opposed to detection procedures. It proposes an end-to-end design that combines on-path risk ratings with a deterministic policy engine and strictly enforced tail-latency budgets. This evaluation is capable through the use of a privacy-preserving, multi-region month-long corpus (~10B requests) in the form of gateway logs, auth events, WAF flags, and honeypot hits. Only O(1) online operations are supported: the presence of the header, the existence of specific trie paths, the length and entropy of tokens and parameters, rarity statistics, and sliding-window counters based on hashed client and tenant identifiers. The available serving options include in-process WebAssembly or a gRPC scorer with stringent deadlines; isotonic calibration and per-endpoint thresholds (including hysteresis) are available that map risk space to action. Idempotent GET caching, fail-open/closed defaults based on endpoint criticality, and signed audit logs can be used to provide reliability and governance. Offline experiments include traditionally separated splits PR-AUC and recall at specific false-block rates; ablations measure feature and model contributions. Online shadow/canary trials can be used to decrease malicious acceptance rate without increasing latency p95/p99 by more than ≤5-10ms. With 10^5-10^7 requests per second, between tenants and across regions, the strategy achieves SLOs and signed, hot-reloaded policy/model bundles. The artifacts consist of a public schema, feature definitions, a synthetic generator, policy DSL examples, rollback playbooks, config, and release scripts to provide reproducible deployment.
References
Arshad, H., Jantan, A., & Omolara, E. (2019). Evidence collection and forensics on social networks: Research challenges and directions. Digital Investigation, 28, 126-138. DOI: https://doi.org/10.1016/j.diin.2019.02.001
Balamurugan, H. V. (2023). Enhancing Individual Privacy Preservation in MultiTenancy Cloud Environments through Secure Multi-Party Computations: A Differential Privacy-Based Data Partitioning Strategy (Doctoral dissertation, Dublin, National College of Ireland).
Chavan, A. (2023). Managing scalability and cost in microservices architecture: Balancing infinite scalability with financial constraints. Journal of Artificial
Intelligence & Cloud Computing, 2, E264. http://doi.org/10.47363/JAICC/2023(2)E264 DOI: https://doi.org/10.47363/JAICC/2023(2)E264
Chiariotti, F., Kucera, S., Zanella, A., & Claussen, H. (2019). Analysis and design of a latency control protocol for multi-path data delivery with pre-defined QoS guarantees. IEEE/ACM Transactions On Networking, 27(3), 1165-1178. DOI: https://doi.org/10.1109/TNET.2019.2911122
Chothia, Z. (2020). Explaining, Measuring and Predicting Effects in Layered Data Architectures (Doctoral dissertation, ETH Zurich).
Coats, C. C. (2019). Reducing memory persistency overheads with transparent out-ofplace updates (Doctoral dissertation, University of Illinois at UrbanaChampaign).
Dharsee, K. (2023). Critical Hardware Towards Software Security Enforcement. University of Rochester.
Fainchtein, R. A. (2023). No Sieve is Good Enough: Examining the Creation, Enforcement and Evasion of Geofilters (Doctoral dissertation, Georgetown University).
Fajana, O. (2023). Novel Techniques for Detecting Tor Botnets.
Ferrari, E. (2022). Access Control in Data Management Systems: A Visual Querying Perspective. Springer Nature.
Hu, H., Zhang, Z., Nakamura, K., Bajcsy, A., & Fisac, J. F. (2023). Deception game: Closing the safety-learning loop in interactive robot autonomy. arXiv preprint arXiv:2309.01267.
Konneru, N. M. K. (2021). Integrating security into CI/CD pipelines: A DevSecOps approach with SAST, DAST, and SCA tools. International Journal of Science and Research Archive. Retrieved from https://ijsra.net/content/role-notificationscheduling-improving-patient
Kumar, A. (2019). The convergence of predictive analytics in driving business intelligence and enhancing DevOps efficiency. International Journal of Computational Engineering and Management, 6(6), 118-142. Retrieved from
ENHANCING-DEVOPS-EFFICIENCY.pdf
Larsson, L., Tärneberg, W., Klein, C., Kihl, M., & Elmroth, E. (2021). Towards soft circuit breaking in service meshes via application-agnostic caching. arXiv preprint arXiv:2104.02463.
Liu, G. (2023). Investigating Security Threats of Resource Mismanagement in Networked Systems.
Mei, G., Pan, L., & Liu, S. (2022). Heterogeneous graph embedding by aggregating meta-path and meta-structure through attention mechanism. Neurocomputing, 468, 276-285. DOI: https://doi.org/10.1016/j.neucom.2021.10.001
Nambiar, N. (2021). Attack resilience of cache replacement policies: Implementation and experimentation in SDN.
Nardini, G., Sabella, D., Stea, G., Thakkar, P., & Virdis, A. (2020). Simu5g–an omnet++ library for end-to-end performance evaluation of 5g networks. IEEE Access, 8, 181176-181191. DOI: https://doi.org/10.1109/ACCESS.2020.3028550
Nyati, S. (2018). Transforming telematics in fleet management: Innovations in asset tracking, efficiency, and communication. International Journal of Science and DOI: https://doi.org/10.21275/SR24203184230
Research (IJSR), 7(10), 1804-1810. Retrieved from
https://www.ijsr.net/getabstract.php?paperid=SR24203184230
Paracha, M. T. (2023). Measurement Techniques to Understand How Diversity in TLS Implementations & Deployments Influences Protocol Security (Doctoral dissertation, Northeastern University).
Prasad, A. (2019). Structured Information Extraction for Scientific Documents (Doctoral dissertation, National University of Singapore (Singapore)).
Qiu, H., Banerjee, S. S., Jha, S., Kalbarczyk, Z. T., & Iyer, R. K. (2020). {FIRM}: An intelligent fine-grained resource management framework for {SLO-Oriented} microservices. In 14th USENIX symposium on operating systems design and implementation (OSDI 20) (pp. 805-825).
Raju, R. K. (2017). Dynamic memory inference network for natural language inference. International Journal of Science and Research (IJSR), 6(2). https://www.ijsr.net/archive/v6i2/SR24926091431.pdf DOI: https://doi.org/10.21275/SR24926091431
Repanovici, R. M., Nedelcu, Ș., Tarbă, L. A., & Busuioceanu, S. (2022). Improvement of emergency situation management through an integrated system using mobile alerts. Sustainability, 14(24), 16424. DOI: https://doi.org/10.3390/su142416424
Sardana, J. (2022). Scalable systems for healthcare communication: A design perspective. International Journal of Science and Research Archive. https://doi.org/10.30574/ijsra.2022.7.2.0253 DOI: https://doi.org/10.30574/ijsra.2022.7.2.0253
Shaked, A., Cherdantseva, Y., Burnap, P., & Maynard, P. (2023). Operations-informed incident response playbooks. Computers & Security, 134, 103454. DOI: https://doi.org/10.1016/j.cose.2023.103454
Sharma, S., Jain, S., & Chandavarkar, B. R. (2020, October). Nonce: Life cycle, issues and challenges in cryptography. In ICCCE 2020: Proceedings of the 3rd International Conference on Communications and Cyber Physical DOI: https://doi.org/10.1007/978-981-15-7961-5_18
Engineering (pp. 183-195). Singapore: Springer Nature Singapore.
Singh, V. (2022). Multimodal deep learning: Integrating text, vision, and sensor data: Developing models that can process and understand multiple data modalities simultaneously. International Journal of Research in Information Technology and Computing. https://romanpub.com/ijaetv4-1-2022.php
Singh, V. (2023). Enhancing object detection with self-supervised learning: Improving object detection algorithms using unlabeled data through self-supervised techniques. International Journal of Advanced Engineering and Technology.
https://romanpub.com/resources/Vol%205%20%2C%20No%201%20%2023.pdf
Song, Y., Jiang, H., Zhang, H., Tian, Z., Zhang, W., & Wang, J. (2023). Boosting studies of multi-agent reinforcement learning on Google research football environment: The past, present, and future. arXiv preprint arXiv:2309.12951. DOI: https://doi.org/10.65109/DMIN4153
Starc, R. P. (2023). Exploring the Microarchitectural Implications of Serverless Workloads Using RISC-V (Master's thesis, ETH Zurich).
Whitehead, J. F. (2023). Advanced X-ray Imaging Techniques for Hepatic Arterial Blood Velocity Measurement. The University of Wisconsin-Madison.
