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Machine Learning

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What is Machine Learning?

Machine Learning is an essential part of intelligence. It focuses on the use of data and algorithms to mimic the way that people learn, with the accuracy of the imitating system progressively increasing over time.

CryptoMize uses statistical techniques to train computers to produce classifications or predictions, revealing important insights in data intelligence gathering. These insights then guide business and application decisions and influence important growth indicators.
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Approaches to Machine Learning

To boost the accuracy of predictive models, Machine-Learning techniques are required.

Supervised Learning
To start supervised learning, you need data and some categorization expertise. Supervised learning looks for data patterns.
Unsupervised Learning
Big data necessitates supervised learning, but understandable data needs Unsupervised learning to find spam without human assistance.
Reinforcement Learning
The algorithm guides the user to success. Unsupervised learning employs no samples. Making mistakes and succeeding helps it grow.
Deep Learning
Deep learning is used to teach fundamental computing concepts. Deep Learning identifies pictures and sounds.
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Machine Learning is the Future

  • Even though Machine Learning algorithms have been available for decades, their use has increased in recent years.
  • Machine Learning platforms include data collection, data preparation, data categorization, model development and training, as well as application and model deployment.
  • The worldwide Machine Learning market is expected to expand from $8.43 billion in 2019 to $117.19 billion by 2027, representing a compound annual growth rate of 12%.
  • It's just a matter of time until Machine Learning platform disputes escalate as Machine Learning gains importance in businesses and commercial setups.
  • CryptoMize makes Machine Learning accessible to a broader audience, indicating its potential to transform the technological environment.

Our Process

We provide you with an easy to use interface to break out the learning system of a Machine Learning Algorithm into 4 simple processes:

Understanding
The information we collect from the appropriate sources is analysed in order to get a deeper knowledge of your business issue.
Preparation
We clean and convert data in order to enhance its quality and guarantee that it can be handled and analysed quickly and efficiently.
Build Models
We create and train models, then assess their performance and repeat the process until the necessary accuracy is achieved.
Evaluation
After the Evaluation has been completed and you have expressed satisfaction with the findings, we will begin the model installation.

Our Services

With time, Machine Learning is becoming more vital to corporate operations and Machine Learning becomes more realistic in commercial settings. CryptoMize can help you with applying Machine Learning from one task to future
ML based Intrusion Detection

A ML system would detect intrusions for you by creating a model of how normal network traffic looks and then find differences.

ML based Behavior Monitoring

CryptoMize utilises Machine Learning to establish a baseline of user behaviour to detect cybersecurity breaches.

ML based Email Monitoring

We offer Email Monitoring for companies of all sizes. It's one of the best ways to personalise multi-channel campaigns.

ML based SIEM Services

By combining Machine Learning with SIEM technology, real-time security event and alarm analysis is possible.

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ML based Intrusion Detection

It is a network security application that employs Machine Learning to monitor network or system activity for malicious activity. Intrusion prevention systems perform critical functions such as identifying malicious activity, collecting information about it, reporting it, and attempting to block or stop it.

With the high number of access points on a typical business network, it is critical to have a method for monitoring for signs of potential violations, incidents, and imminent threats. Network threats are becoming increasingly sophisticated and capable of breaching even the most robust security solutions.
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Methods used by Intrusion Detection System (IDS)

Signature-based Detection

Signature-based IDS operates packets in the network and compares with pre-built and preordained attack patterns known as signatures.

Statistical Anomaly-based

CryptoMize's IDS monitors network traffic and compares it to a baseline. Standard network and protocol behaviour will be identified.

Stateful Protocol Analysis

This Intrusion Detection System method recognizes divergence of protocols stated by comparing observed events with pre-built profiles

ML Based User Behavior Monitoring

User Behaviour Monitoring is a term that refers to a technology that is used to profile user and entity activity and identify anomalous behaviour. CryptoMize uses Machine Learning to create a baseline of users’ normal behavior and detects patterns that lead to cybersecurity violations.
  • Gathering Helpful Context

  • To begin the process, data about the system, entities, and events must be gathered. Each of our unique solution's use cases results in a unique dataset
  • Detecting Threats

  • Machine Learning data analysis enables the programme to identify suspicious activities and trends among users
  • Creating Behavioral Profile

  • These are routine tasks based on baseline data fed into the system via Machine Learning. Peer group members are compared to a security expert
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ML Based Email Monitoring

  • Employers can use our ML based Email Monitoring to identify potential risks in their employees' communications, which is extremely useful
  • Business users can benefit from CryptoMize, which automates Email Monitoring in a safe and scalable manner for organisations of all sizes
  • In some cases, it may be the most effective method of conducting multi-channel communication in a personalised manner
  • Email Monitoring enables email senders to gather detailed information on the activity and location of email receivers, who are identified just by their email address
  • CryptoMize's ML Email Monitoring tools provide detailed insight into most popular web-based email clients
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SECURITY EVENTS

ML Based SIEM Services

SIEM stands for Security Information and Event Management, among other things. ML based SIEM technology consolidates log data, security alerts, and events into a centralised platform that allows for real-time analysis of security events and alerts to be performed.
Identify Threats

Surveillance of unusual user behaviour that may signal a security breach or insider threat.

Secure the Cloud

Identify and discuss the risks associated with hybrid multi cloud and containerized workloads.

Uncover Exfiltration

Calculate the number of exfiltration events such as illegal cloud storage or excessive printing.

Monitor Devices

Monitor Operational Technology and Internet of Things (IoT) systems from a central location to identify potential risks.

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The More you Ask, the More it Learns

Perks of Machine Learning

Machine Learning is revolutionizing the way businesses operate. It’s making them smarter, faster, more profitable.
Detects Fraud

Machine Learning algorithms are extremely good at detecting fraud trends that a person may miss.

Works Faster

We've refined our Machine Learning model. This system can quickly learn new patterns and detect fraud.

Scale

Machine Learning allows an organisation to work on larger datasets than people, allowing for cost savings

Efficiency

ML algorithms may automate unimportant tasks, enabling professionals to concentrate on more vital ones.

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Our Aim

Create a Proactive Approach

In addition to identifying potential dangers and opportunities, we also evaluate the likely outcomes of alternative policy responses as part of our thorough research.

Rapid Threat Assessment

We use powerful Machine Learning (ML) services to identify attack and threat trends, and the platform to access CryptoMize's large threat intelligence data lake.

Outshine the Competition

Our specialists will provide you with essential information, they will assist you in growing your business and staying one step ahead of your competition.

FAQ'S

Frequently Asked Questions

Basically, it’s a type of Artificial Intelligence (AI), where computers take data and “teach” themselves through algorithms and statistical analysis to be more accurate in their analysis of that data and any pattern predictions that arise as a result. There is no programming the software to execute a certain number of commands; it does that on its own.
Machine Learning doesn’t just pertain to advertising. When people can make predictions more quickly and with more accuracy, they can better prepare for the outcomes. For example, fraud detection on your credit card is based on Machine Learning. Machines are learning your spending patterns faster and reacting more quickly when you seem to deviate from the norm–potentially saving you thousands of dollars in unauthorized spending charges and the hassle to get your money back.
Essentially, Machine Learning is the backbone of Big Data. Without the ability of computers to analyze volumes of data that humans never could (and the cheaper storage capacity to keep all that data to mine), we wouldn’t have Big Data and its possibilities.
Machine Learning is already well established in many features we take for granted today, and there are many possibilities for it in the future, including opportunities in technology, science, healthcare and more. With developers able to incorporate Machine Learning into their own applications built in Azure and AWS, we’re sure to see a lot more examples of it in the very near future.
A Large sources of text-based data you want to investigate are the best sources. For example, public data sources include product reviews on e-commerce sites, product review sites, and online discussion forums. In contrast, proprietary data could include answers to open-ended survey questions or customer call center data.
Learning Algorithms helps you solve real-world problems without or with minimal human intervention. Some of the popular algorithms you must know include –
  1. Apriori Algorithm
  2. Artificial Neural Networks
  3. K Means Clustering Algorithm
  4. Linear Regression
  5. Logistic Regression
  6. Naïve Bayes Classifier Algorithm
  7. Support Vector Machine Algorithm

We'd love to hear from you.

Want to find out how CryptoMize can solve problems related to your business? Let's talk to transform your ways with us.

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