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What are the Applications of Machine Learning in Cyber Security?



What are the Applications of Machine Learning in Cyber Security?

We all must have used a voice assistant or at least heard about it. A voice assistant such as Alexa is built by Machine Learning (ML) algorithms combined with Natural Language Processing (NLP). Machine Learning is used in the Education sector, Search Engines, digital Marketing, Banking, and Financial sectors and Security Systems.

What is Machine Learning?

Machine Learning, a branch of Artificial Intelligence is applied for many applications. This field of study includes algorithms and statistical models designed for specific functions. Many are adapting Machine Learning practices to ease their way into life. One can opt for an online course to learn to develop algorithms for specific functions without giving any explicit instructions to the computer. There is also a need for learning as these algorithms are going to be future anti-virus and malware protection.

Why Use Machine Learning?

Machine Learning enables machines to develop functions by educating themselves. The machines use Pattern Detection, and other functions such as Image Detection and Speech Recognition to learn. Machine Learning practices when combined with other branches of AI, can be used in a variety of functions. For example, Google Maps Traffic prediction and how Netflix provides suggestions for movies to watch.

What is Cyber Security?

Although Machine Learning algorithms are most commonly used in developing databases and maintain records, they are used in other sectors too. One of them and a significant application in cybersecurity. Here, we are going to know how Although, Machine Learning algorithms are most commonly used in developing databases and maintain records, they are used in other sectors too. One of them and a significant application in cybersecurity. Here, we are going to know how it is applied to ensure Cybersecurity in particular and what the applications are.

The Applications of Machine Learning in Cyber Security

Cybersecurity is protecting computer systems from any threats such as malware, ransomware, and virus. It applies to both hardware and software of the systems and makes sure the functions carry out well. Cyber Security is one of the important practices which requires more general awareness and measures to help with it. Although, the aim is to eradicate issues regarding cybersecurity altogether, for now, let’s see some existing security concerns and how Machine Learning techniques are used to remove or reduce them.

1. Vulnerability Testing

A computer, in general, is subject to receive malware and threats from other sources. Sending and receiving files leads to this and it can’t be avoided. Now, what can be avoided is these threats and that is why Machine Learning Engineers come up with Vulnerability Testing tools. These tools perform malware analysis and identify any unwanted presence.

2. Clustering

Clustering is the method of grouping elements under a specific function. There are many benefits of clustering. This makes it easy to detect outliers and saves a considerable amount of time. Clustering is mainly applied in Forensic Analysis. This secures e-mail gateways and separate legal files from threats. Mixture Model, Bayesian Model, and Gaussian Mixture Model are some of the Machine Learning statistical models used for this purpose.

3. Behavioral Analysis

Behavioral Analysis is done by Regression, Classification, and Clustering of data.  Now, regression or prediction is generating new ideas based on existing data. Even though there are different methods of regression, they can be grouped under Machine Learning and Deep Learning practices. The classification of data aids in performing behavioral analysis based on specific categories and classes. We perform behavioral analysis for both users and processes.

User Behavior

Social media networks, domain users, application users, and messengers are sorted through to identify user details. Malware detection is made possible by classifiers. These classifiers are trained to learn unsupervised learning problems. A universal algorithm to identify users can prove very useful, but it is far from possible, at least for now.

Process Behavior

Ever wondered how credit card fraud detection is carried out? Machine Learning is used here to detect changes or outliers in existing systems or processes. To detect process anomalies or frauds in business, learning about the process is necessary.

4. Generating Incident Responses

Now, we know how Netflix or Amazon Prime suggests the next movie to watch. But, do you know how the systems do that with the help of previous data? That is why we employ Association Rule Learning here. Primarily used for Incident Response, this practice is used to eliminate false positives, run investigations and change risk values. Behavioral Analysis is performed on such recommendation systems to perfect their functions.

5. Detecting Unauthorized Access

One of the attractive features of executing Machine Learning practices is Dimensionality Reduction or Generalization. We can’t group every function into clusters. This is because general methods restrict the number of features and there is a need to cut down some. Unnecessary features are identified and removed. Principal Component Analysis (PCA) and Sentient Analysis are used for performing such monitoring functions.

6. Face Detection Solutions

We know machines use Image Recognition to detect faces. Advancements in computer technology have made it possible for systems that detect a cat and also a specific person in a group photo. We employ Dimensionality Reduction to detect faces. Unlabeled data is identified and removed by Generative models. Vulnerability Scanning tools are developed by adopting the methods to provide cybersecurity in applications requiring face detection.

7. Network Protection

On the way to protect cybersecurity, there is a need to safeguard networks from malware too. Intrusion Detection Systems (IDS) protect Ethernet, wireless connections and other virtual networks. One of the Machine Learning applications here is Network Traffic Analysis (NTA). This is used to identify threats and anomalies in every layer of these networks.

8. End-point Protection

Endpoint protection is also provided by regression, classification, and clustering. There is a need for segregation as a mere application of Machine Learning without being aware of the type of end-point can lead to different solutions. Here, the regression mechanism is used to predict calls enabling us to be identify anything else. Classification helps consolidate threats under groups such as Malware, Spyware, and Ransomware. We have already discussed how clustering secures e-mail gateways.

9. Secure Applications

Now, there are other applications of Machine Learning for establishing cybersecurity. Machine Learning is used to secure applications too. Code analysis is performed on web applications, databases, Enterprise Resource Planning (ERP) systems, etc.

Now, these are some applications of Machine Learning in cybersecurity systems. There is a need for developing more Machine Learning algorithms to ensure cyber safety in social media too, just because there is an increased consumption of internet and social media as days go by. Since Machine Learning practices are increasingly used for various purposes, the awareness about engaging their use is also increasing. Even hackers are adopting these kinds of practices. That is why Machine Learning services should be more concentrated upon and develop counterattack algorithms in case of cyber-attacks.

Ramesh Lal is a digital marketing executive at FATbit Technologies. He has extensive expertise in assisting businesses to streamline their business operations and launch new ventures. With in-depth knowledge of B2B marketing, Ramesh also assists B2B businesses in reaching new customers and securing conversions.