What is Machine Learning?
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.
Approaches to Machine Learning
To boost the accuracy of predictive models, Machine-Learning techniques are required.
We provide you with an easy to use interface to break out the learning system of a Machine Learning Algorithm into 4 simple processes:
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.
ML based Intrusion Detection
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.
Methods used by Intrusion Detection System (IDS)
Signature-based IDS operates packets in the network and compares with pre-built and preordained attack patterns known as signatures.
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
ML Based SIEM Services
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.
Calculate the number of exfiltration events such as illegal cloud storage or excessive printing.
Monitor Operational Technology and Internet of Things (IoT) systems from a central location to identify potential risks.
Perks of Machine Learning
Machine Learning algorithms are extremely good at detecting fraud trends that a person may miss.
We've refined our Machine Learning model. This system can quickly learn new patterns and detect fraud.
Machine Learning allows an organisation to work on larger datasets than people, allowing for cost savings
ML algorithms may automate unimportant tasks, enabling professionals to concentrate on more vital ones.
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.
Frequently Asked Questions
- Apriori Algorithm
- Artificial Neural Networks
- K Means Clustering Algorithm
- Linear Regression
- Logistic Regression
- Naïve Bayes Classifier Algorithm
- 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.