Machine learning is one of the hottest areas in data science. This subset of artificial intelligence allows a system to learn from data and make accurate predictions, identify anomalies or make recommendations using different techniques.
Machine learning techniques extract information from vast amounts of data and transform it into valuable business knowledge. While most industries use these techniques, they are especially prominent in the finance, marketing, healthcare, retail and cybersecurity sectors.
Machine learning can also address new cyber threats. There are many types of cyberattacks, such as structured query language (SQL) injection, phishing, cross-site scripting attacks, malware, social engineering, man-in-the-middle attacks, distributed denial of service attacks and ransomware. Organizations employ machine learning to constantly evaluate data, find patterns that could result in potential attacks and mitigate them.
Typical Uses for Machine Learning Techniques
Among other things, machine learning is often used to identify anomalies by monitoring network behavior, avoiding accessing harmful websites and detecting previously unknown malware. These methods can also protect data in cloud environments. Intrusion detection, malware classification and network analysis are the main security uses of machine learning.
For those in security spaces, the poor quality of the data used to train the methods — or the lack of data entirely — presents serious challenges. The potentially severe consequences of an error make the accuracy requirements in this industry high.
Below are detailed some of the most relevant machine learning uses for security:
Real-time email monitoring. Machine learning uses natural language processing and anomaly detection techniques to analyze email content and identify phishing attempts.
Fight against bots. Bots produce one-quarter of web traffic, and some can even take control of an application and execute specific malicious activities. The machine learning techniques appl ..
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