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- W4385883887 abstract "Security Operation Centers (SOC) are the fundamental pillars for modern cybersecurity. Their critical importance is proven by monitoring, detecting, alerting, and reacting to cyber-attacks in real-time. However, SOC implementations suffer from many limitations that reduce their efficiency. One of the critical attacks faced by SOC teams is Distributed Denial of Service (DDoS) therefore eliminating false positive attacks is an important step undertaken. It is useful to propose an efficient method to detect DDoS attacks based on Machine Learning (ML). We demonstrate how to build an efficient supervised ML system with a low false positive rate, capable of detecting DDoS attacks during dataset processing. We chose the CICDDoS2019 Dataset to achieve this. The experimental results after the use of different ML models shows that XGBoost and Random Forest achieve the highest accuracy with the lowest false positive rate." @default.
- W4385883887 created "2023-08-17" @default.
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- W4385883887 date "2023-01-01" @default.
- W4385883887 modified "2023-09-25" @default.
- W4385883887 title "Enhancing DDoS Attacks Detection in SOCs by ML Algorithms" @default.
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- W4385883887 doi "https://doi.org/10.1007/978-3-031-36258-3_42" @default.
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