Machine Learning and CyberSecurity

What is Machine Learning?

Basicly we can think of machine learning as a subset of artificial intelligence. The use of machine learning is basically to predict the output of the data to be given as input with the data at hand.

Machine learning is divided into 3 categories. These are Supervised Learning, Unpuserved Learning and Reinforcemnet Learning. Let’s talk about them briefly.

  1. Supervides Learning: It is a type of learning in which the output labels of the data in our dataset are given. In this type of learning, a semantic equivalent of each data is given along with it. They are mostly used to solve regression and classification problems.
  2. Unsupervised Learning: Here, the data in the dataset has no labels. Since the dataset is not labeled in unsupervised learning, the algorithm must discover patterns and relations in the dataset.
  3. Reinforcemnet Learning: In this method, an entity called an agent interacts with an environment and receives rewards and punishments as a result of these interactions. Its purpose is to develop a strategy to increase rewards and decrease penalties.

Well, if we come to the question of how machine learning can be used in the field of CyberSecurity,

  1. Malware detection: Machine learning algorithms can be used to detect malware. For example, an antivirus software, or EDR, uses machine learning algorithms to develop models and detect malware entering the computer. These algorithms can be used to detect new malware or to avoid zero-day attacks.
  2. Authentication: Machine learning algorithms can also be used in authentication processes. These algorithms learn the characteristics and behaviors of the user, improving the authentication process and making it more secure. For example, e-commerce sites can use machine learning algorithms to authenticate their customers. By learning the characteristics of real customers from labeled or unlabeled data, these algorithms can prevent fake accounts from being opened.
  3. Behavior Analysis: Machine learning can be used for behavior analysis. For example, by analyzing a user’s normal behavior patterns, anomalous activities can be detected where an attacker is trying to hijack the account.
  4. Network Intrusion detection: Machine learning algorithms can be used to detect network attacks. Such algorithms learn normal network behavior and can detect anomalies. For example, a company can use machine learning algorithms to detect an attack on their network. Once these algorithms learn about normal network behavior, they can detect abnormal behavior and block these malicious network movements.
  5. Vulnerability detection: Machine learning algorithms can be used to detect vulnerabilities in applications and systems. These algorithms learn from the normal behavior of the application or system and can detect and prevent abnormal behavior.

Some security product manufacturers, especially Cloud-Native, are constantly improving their models by correlating billions or trillions of data per day in their own pools, tagging them with security professionals, different teams, or letting them learn in an unspecified way.

Finally, it is not difficult to say that machine learning has started to become a building block in solving problems in the cyber world, as it is in many industry.





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