Artificial Intelligence (AI) and Machine Learning (ML) play critical roles in cyber security.  More and more cyber security applications are utilizing AI and ML to enhance their effectiveness.  The following are some of the applications that are taking advantage of ML algorithms.

Phishing Prevention. Phishing is a fraudulent attempt to obtain sensitive data by disguising oneself as a trustworthy entity. Detection of phishing attack is a classification problem. Training data fed into the ML system must contain phishing and legitimate website classes. By using learning algorithm, the system can be able to detect the unseen or not classified URL.

Botnet Detection. Botnet means an organized automated army of zombies which can be used for DDoS attack, sending spam, or spreading viruses.  Machine learning is now being used in detection and recognition of botnets to prevent attacks.

User Authentication. Authentication verifies the identity of a user, process. or device to allow only legit users to use the resources and services. Machine learning is now being used for adaptive authentication by learning user’s behavior.

Incident Forecasting. Predicting an incident before it occurs can save a company’s face and money.  Machine learning algorithms fed with incident reports and external data can now predict hacking incidents before they occur.

Cyber Ratings. Cyber ratings is used to assess the effectiveness of a cyber security infrastructure. Machine learning calculates cyber security ratings by getting information from multitude of security data from the web.

Spam filtering. Unwanted emails clogging user’s inbox have to be eliminated by using more dependable and robust antispam features.  Machine learning methods are now the most effective way of successfully detecting and filtering spam emails.

Malware Detection. Malware is getting more complex and being distributed more quickly.  Detecting them using signatures is not sufficient anymore.  Machine learning techniques are now being used for malware detection due to its ability to keep pace with malware evolution.

Intrusion Detection.  Intrusion detection identifies unusual access or attacks to secure internal networks. Machine learning techniques such as pattern classification, self-organizing maps and fuzzy logic are being used to detect intrusion.

User Behavior Monitoring. User behavior monitoring is an approach to insider threat prevention and detection. Machine learning techniques can help in creating an employee behavioral profile and setting off an early warning when insider threat is observed.