Detecting DDoS Attacks by ML Technique: Hybrid Approach | KNN SVM Gaussian Naïve Bayes | Source Code
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This is an existing solution for detecting DDoS attacks.
Distributed denial of service (DDoS) attacks is a subclass of denial of service (DoS) attacks. A DDoS attack involves multiple connected online devices, collectively known as a botnet, which are used to overwhelm a target website with fake traffic.
Unlike other kinds of cyberattacks, DDoS assaults don’t attempt to breach your security perimeter. Rather, they aim to make your website and servers unavailable to legitimate users. DDoS can also be used as a smokescreen for other malicious activities and to take down security appliances, breaching the target’s security perimeter.
Cloud service availability has been one of the major concerns of cloud service providers (CSP) while hosting different cloud-based information technology services by managing different resources on the internet.
The vulnerability of the internet, the distributed nature of cloud computing, various security issues related to cloud computing service models, and the cloud’s main attributes contribute to its susceptibility to security threats associated with cloud service availability. One of the major sophisticated threats that happen to be very difficult and challenging to counter due to its distributed nature
and resulting in cloud service disruption is Distributed Denial of Service (DDoS) attacks. Even though there are a number of intrusion detection solutions proposed by different research groups, and cloud service providers
(CSP) are currently using different detection solutions by promising that their product is well secured, there is no such a perfect solution that prevents the DDoS attack. The characteristics of a DDoS attack, i.e., having different
appearances with different scenarios, make it difficult to detect.
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