Machine Learning in Cybersecurity |

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Presentation transcript:

Machine Learning in Cybersecurity |

No matter the size of the organization, cybersecurity plays a vital role in securing all confidential data from cyber attacks. Along with cybersecurity, booming technologies like Machine Learning, Artificial Intelligence, and Data Science are being used in cybersecurity to provide efficiency in terms of security. This comprehensive blog is all about how Machine Learning helps cybersecurity.

| What is Machine Learning? Machine learning is a subset of Artificial Intelligence that uses statistical methods allowing machines to improve with experience. Machine Learning algorithms are designed to learn and improve from experience in identifying and adopting the data patterns to make data-driven predictions. These predictions play a significant role in making data-driven decisions and mitigating human errors. How is Machine Learning used in Cybersecurity? Cybersecurity can be effective, proactive, and less expensive using Machine Learning. It allows machines to analyze the pattern of attacks, learn from them to prevent similar attacks, and respond actively in real-time. Machine learning helps recognize patterns and predict cyber threats in massive data sets. It can reduce the time spent identifying the attacks by automating the analysis and allowing organizations to use resources strategically. Application of Machine Learning in Cybersecurity The following are some of the applications of machine learning in real-time: Detecting the Cyber Threats Machine learning helps detect cyber threats by continuously monitoring the network behavior for malicious activities. It processes a massive amount of data to identify the security incidents in the network or system.

| Defending Threats Machine Learning is used to identify and defend against security incidents using Machine learning models such as Support Vector Machine (SVM), Naive Bayes, Deep Neural Networks, Decision tree, etc. Enhances Human Analysis Machine learning helps humans detect malicious attacks, analyze the network, assess vulnerability, and provide endpoint protection. It helps to drive effective decisions to respond against cyber threats. Automate Tasks Automating tasks using machine learning models helps businesses to accomplish the tasks such as detecting malware, analyzing vulnerabilities, enhancing threat detection techniques, and simplifying the work. Monitoring s Machine learning models and algorithms are used to identify the patterns to uncover malicious s. It helps to mitigate phishing attacks and uses the Natural learning process to scan the s to identify malicious activity. Endpoint Malware Protection Machine learning algorithms help to improve endpoint security by supporting the malware protection workflows. It helps to detect and mitigate malware attacks in the network environment.

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