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- W2973165924 abstract "With the incorporation of the Internet in our lives and its ever-increasing usage, it becomes all the more imperative to safeguard our systems and data against the malicious attacks. One of such malicious attacks is distributed denial-of-service (DDoS) attack, in which multiple nodes target a single target node to flood it. DDoS attacks in most of the cases directly target the server, thereby posing an important question on the security of the systems. DDoS attacks affect the entire network, thereby resulting in its downtime, unavailability of the services, and performance degradation. Due to this phenomenon, business losses are incurred, and hence, it becomes important to detect them before the damage is done. In this research work, a model is introduced which performs real-time detection of DDoS attacks. This model uses the decision tree classifier for detection of DDoS attacks. First, the features are extracted, and the information gain is calculated. Based on this, the decision tree is constructed which is then used to classify an instance as DDoS or not using a classifier algorithm. This model has demonstrated a success rate of 90.2% which is an improvement over the currently available algorithms. Furthermore, it also outperforms the existing algorithms in terms of sensitivity and specificity. The most significant feature of the model is that it operates in a live system in real-time conditions. This ensures protection against data losses and also helps in identifying the source of the attack. This system suggests a lightweight data mining approach to detect DDoS attacks using decision trees." @default.
- W2973165924 created "2019-09-19" @default.
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- W2973165924 date "2019-09-11" @default.
- W2973165924 modified "2023-10-01" @default.
- W2973165924 title "Real-Time Distributed Denial-of-Service (DDoS) Attack Detection Using Decision Trees for Server Performance Maintenance" @default.
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- W2973165924 doi "https://doi.org/10.1007/978-981-13-8253-6_1" @default.
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