Abstract
This paper presents a scalable microservice-oriented architecture, called Distributed LS (DLS), for enhancing the Local Search (LS) phase in the Greedy Randomized Adaptive Search Procedure for Feature Selection (GRASP-FS) metaheuristic. We distribute the DLS processing among multiple microservices, each with a different responsibility. These microservices are decoupled because they communicate with each other by using a message broker through the publish and subscribe paradigm. As a proof-of-concept, we implemented an instance of our architecture through the Kafka framework, two neighborhood structures, and three LS algorithms. These components look for the best solution which is published in a topic to the Intrusion Detection System (IDS). Such a process is iterated continuously to improve the solution published, providing IDS with the best feature selection solution at the end of the search process with scalability and time reduction. Our results show that using RVND may take only 19.53% of the time taken by VND. Therefore, the RVND approach is the most efficient for exploiting parallelism in distributed architectures.
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This work is financially supported by TQI Tecnologia.
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Silva, E.F.C., Naves, N., Quincozes, S.E., Quincozes, V.E., Kazienko, J.F., Cheikhrouhou, O. (2023). GDLS-FS: Scaling Feature Selection for Intrusion Detection with GRASP-FS and Distributed Local Search. In: Barolli, L. (eds) Advanced Information Networking and Applications. AINA 2023. Lecture Notes in Networks and Systems, vol 654. Springer, Cham. https://doi.org/10.1007/978-3-031-28451-9_18
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