How to improve cybersecurity using machine learning and applied data science
Cybersecurity prevents infiltration and data breaches while saving your business from the heavy financial impacts of cyberattacks.
Machine learning and data science have brought significant changes in the technological realm. Currently, cybercriminals use sophisticated techniques, such as artificial intelligence, to launch cyberattacks. While data science can monitor such changes, it requires machine learning to become effective. In the current cybersecurity landscape, understanding how the attack occurred and the malicious software patterns are important for proper detection and removal of threats.
Machine learning helps in the identification of various patterns. Similarly, data science works with machine learning techniques to comb through various software patterns and determine those that expose your organization to risks.
Defining Machine Learning and Data Science
If you are new to cybersecurity, you must understand what these and other cybersecurity terms mean. Machine learning involves the use of artificial intelligence to learn from sampled data. On the other hand, data science employs machine learning tools to interpret and improve raw data functionality. The combined functionality of machine learning and applied data science is currently used in various fields, such as speech recognition apps, epidemic trackers, and personalized recommendations.
Why Use Machine Learning and Applied Data Science for Cybersecurity
As mentioned, current cybersecurity tools lean towards using machine learning and applied data science for advanced protection. If you are wondering why below are some reasons;
- Hackers Use Advanced Cyber Attacking Techniques
The benefits of artificial intelligence, machine learning, and data science that should help the “good guys” are also used by the “bad guys.” Hackers use similar advanced techniques to automate their search for businesses with several vulnerabilities. They can easily locate weak points within an organization’s security system. Therefore, for a competitive defense, cybersecurity experts should also make use of these advanced strategies.
- Big Data Easy Handles the Surge in Data
The abundance of data related to cyberattacks is beneficial in developing better cybersecurity systems. However, with the plethora of data comes handling and management difficulties, especially for average cybersecurity tools. Therefore, through machine learning and applied data science, security experts can sift through the chunks of data for better insights with utmost efficiency.
- Big Data Relies on Science
Even if cybersecurity tools can process data at the rate at which it is generated, the raw data still requires analysis. For the collected information to be useful, businesses should interpret it scientifically and adopt the findings into their cybersecurity strategies.
- Knowing How the Attack Occurred is Important
Traditional vulnerability scanners and antivirus solely focused on discovering and eliminating the threats. However, with the application of data science, cybersecurity experts can uncover various factors favoring the attack and specific characteristics of the threat. This includes analyzing the threat’s entry points, specific data that the hacker accessed, and target points for the hacker.
Understanding the details of how the attack happened increased the probability of eliminating the threat from your network and creating better cybersecurity defenses.
How Machine Learning and Applied Data Science for Cybersecurity Work
Machine learning and applied data science can be used in cybersecurity in various ways. Among them include;
Classification is a subclass of supervised learning where AI is fed with data and rules that guide the relations between different data points by human programmers. This machine learning principle is used to predict data labels using the random forest classifier algorithm. Classification is used in cybersecurity to label various types of attacks, detect various malicious techniques and find common injection attacks.
This is also a subset of supervised learning that primarily centers on numerical quantities. The goal of regression techniques is to find out how different factors affect one another. This can help cybersecurity experts discover suspicious HTTP requests, find unexpected system calls, and compare network packet parameters with their typical values.
Unlike the techniques mentioned above, clustering is a form of unsupervised learning. Therefore, while humans have to enter the data sets to AI, the systems learn the relationship between various data points by itself. The main focus of clustering is sorting data into multiple groups based on data points that highly resemble one another. Clustering can be used in several cybersecurity techniques, including analysis of forensics, detecting stolen admin credentials.
Cybersecurity experts can train AI to recognize remote access on websites and network systems, which might suggest attempted hacking. Clustering also helps in protecting malware attacks on email accounts. It prevents employees from downloading malicious phishing email attachments by separating legitimate and suspicious files.
Cybersecurity prevents infiltration and data breaches while saving your business from the heavy financial impacts of cyberattacks. Fortunately, businesses of all sizes can take advantage of machine learning and applied data science for valuable cybersecurity protection. Educating yourself and your team is a great first step. Plus, if any of your employees are veterans, they may be eligible to take classes on cybersecurity for free. Organizations, both small and established, are prone to cyberattacks. Therefore, protecting your business by bolstering security defenses is the best prevention strategy.