🤜🏻ML to protect against DDos attacks
Machine learning algorithms are actively used in cybersecurity, for example, to identify atypical user behavior due to unauthorized access. ML can also be used to protect against DDOS attacks. The goal of a DDoS attack is to disrupt an organization by flooding a network, Internet-connected service, or technical infrastructure surrounding the target with unwanted traffic. The amount of traffic directed to the target can severely limit or disable availability.
DDoS attacks use Internet-connected devices that have already been compromised by malware. An attacker exploits existing vulnerabilities in dozens, hundreds, thousands, or even millions of devices to gain remote control. Thanks to the ubiquity of IoT devices, when even a home refrigerator goes online, protection against DDOS attacks is relevant for both businesses and private households.
A 2017 Kaspersky Lab survey found that the cost of sustaining a DDoS attack for small and medium businesses was $120,000. For large enterprises, this figure has risen to $2 million. And a 2018 study estimated the cost of downtime for a large organization to range from $300,000 to $540,000. In the US, the average global cost of a data breach was $8.46 million, according to a 2020 IBM report.
Using ML, you can build a binary classification model that would mitigate the impact of a DDoS attack on an organization's activities by correctly distinguishing safe traffic from malicious traffic. Here it is necessary to reduce the rate at which the ML model incorrectly identified safe traffic as malicious, as well as mitigate the consequences of a DDoS attack by correctly identifying malicious traffic with a probability of at least 90%.
Implementation example with Dask, XGBoost and Stacked Ensembling:
https://towardsdatascience.com/mitigating-ddos-attacks-with-classification-models-aa75ea813d85