Machine Learning Use Cases in Cybersecurity
There are many use cases where machine learning helps in preventing cybersecurity incidents. As time goes on, the number of use cases is growing.
One of the use cases is detecting and preventing DDoS attacks
. ML algorithm can be trained to analyze a large amount of traffic between different endpoints and predict different DDoS attacks (applications, protocols, and volumetric attacks) and botnets.
In 2021, there were more than 9 million DDoS
attacks worldwide. DDoS has one goal, and that is to put the system to slow-response or no-response (read it downtime). ML can detect and stop it.
The second use case is to fight against malware
. This includes trojans, spyware, ransomware, backdoors, adware, and others. ML algorithm can be trained to help antiviruses in fighting unknown cyber threats. According to Statista research
, in 2021, 5.4 billion malware attacks were detected.
are one of the most common attacks used to steal confidential data and get into corporate or government institutions. It is shared via scam emails. For example, Google (Gmail) uses machine learning to analyze data in real-time and identify and prevent malicious behavior of more than 100 million phishing emails.
We published an article to help you understand and prevent phishing in detail. You can read it here: Phishing – The danger of malicious phishing emails
The third use case is about protecting against application attacks
. Applications are used by end users and are prone to different layer 7 attacks. According to Cloudflare, they handle 32 million HTTP requests
per second. Web Application Firewall (WAF), in combination with machine learning, can be trained to detect anomalies in HTTP/S, SQL, and XSS attacks.
Microsoft, AWS, Google Cloud, FortiGate, and many other vendors offer WAF as part of their portfolio.
The fundamental security principles teach us to implement multi-factor authentication
. This includes something we know (e.g. password), something we have (e.g. USB token), and something we are (e.g. fingerprint, facial detection). AI and ML combined with deep learning play a vital role in biometric applications.
ML helps to perform matching tasks to quickly find the relevant data.
Security Operation Centers (SOC)
take care of monitoring, detecting, and responding to different cyber security threats. One of the challenges the SOC Team had was dealing with a large amount of data. Thanks to machine learning, SOC Teams can more efficiently automate and analyze incidents and be more proactive.
The list of use cases is bigger. And it wouldn’t work without having machine learning as part of cybersecurity.