Cetas Blog Post

Machine Learning and Cybersecurity Trends in 2023

January 5, 2023

Models for machine learning often use statistical algorithms that extract patterns and trends from previously collected data to construct baseline curves.



In the realm of information security, machine learning is the most recent trend to create waves, and there is a solid point for this. 

The help provided by complicated algorithms that 'learn' and expand vital to human analysts because it enables them to concentrate on larger-scale strategic battles and enhance security systems to the point where they are almost impregnable. 

Machine learning is increasingly essential in the routine and structural improvements that are being made to information security. This trend is expected to continue into the years to come.

Today, organizations are adopting increasingly advanced machine learning (ML) models to secure their data and networks from cybercriminals. 

On the other hand, hackers are beginning to employ the same technology to bypass security, detect holes, and conduct attacks.

Models for machine learning often use statistical algorithms that extract patterns and trends from previously collected data to construct baseline curves.

Dashboards and other types of visualizations are used to provide continually updated, future-looking intelligence due to the technology's ability to modify its algorithms automatically in response to any new data collected. 

Because of this, non-technical managers and executives are now able to make judgments in a more timely and precise manner.

When applied to enhance corporate information technology security, machine learning (ML) and artificial intelligence (AI) evaluate enormous data sets created by enterprise networks and find aberrant tendencies in the data stream.

The Application of Machine Learning to Cybersecurity

Hacks, threats, and breaches are becoming more complex, and as a result, the emphasis has shifted to fighting fire with fire and remaining one step ahead of the adversary. 

Consider the following reasons why machine learning is essential to the process of safeguarding data in the wake of widespread breaches:
Detecting Dangers to Networks
Machine learning algorithms can successfully identify and ward off attacks since they continually scan data frameworks in search of abnormalities or breaches. 

The capacity of machine learning to evaluate data in real time is very important since it makes it possible to identify threats, insider breaches, and viruses as they occur, saving enormous losses.
Safeguarding Data Stored in the Cloud
A growing number of businesses are moving their databases to the cloud to reduce the strain placed on their external servers and ease the burden of database upkeep. 

The use of machine learning can assist in protecting data that is kept in the cloud by locating and analyzing potentially malicious cloud logins and investigating the reputation of IP addresses.
Encrypting Data
The act of carrying out calculations on previously encrypted data using machine learning algorithms without first needing to decode the data is known as homomorphic encryption. 

The additional benefit of using this method is that the results are likewise in the ciphertext. 

Still, when they are decoded, they display the same outcomes they would have shown if the operation had been carried out on decrypted data.
Evading Hacker Attacks
Machine learning can assist avoid data breaches far in advance by using approaches like behavior analytics and pattern recognition. 

This is in contrast to the current practice of trying to recoup damages once a violation has occurred. 

It allows businesses to stay one step ahead of hackers, which helps them defend themselves against prospective assaults and upgrade their defenses in advance.
Providing Support for Endpoint Security
Machine learning can teach endpoint security systems to spot abnormalities and malicious actions based on what it has previously encountered and classified as suspicious. 

This is done by comparing the new activity to what has already been identified as suspicious. 

Endpoint security can be continually enhanced against more recent attacks by drawing on historical data and repositories. 

Because machine learning thrives on quantities and bigger datasets, this is possible.

Emerging Trends in ML and Cybersecurity 2023

As more companies incorporate machine learning and artificial intelligence into their cybersecurity strategy, so too are fraudsters following suit.

  1. Email Scams Like Phishing and Spam
Machine learning can transform a well-known hacking technique into a weapon that can rip apart company cybersecurity. 

It is possible to manipulate security scoring with ML, which gives businesses a fake sense of confidence; deliver phishing emails one at a time rather than in bulk, which makes them more difficult to detect; and generate fake personas to make fraudulent emails look like they came from a legitimate source.
  1. Theft of Passwords
Machine learning can understand how businesses handle their security, such as password procedures and periodic updates, which hackers can use to ease their access to networks and data.
  1. Deep Fakes
This tactic makes audio and video counterfeits of actual individuals. Hackers often use deep fakes to produce fraudulent images, profiles, and emails. 

The use of AI, on the other hand, takes the strategy to an entirely new level by providing criminals with the means to propagate their assaults via telecommunication and video technologies.
  1. AI Poisoning
Hackers can poison AI by flooding machine learning models with harmful input to damage their output. 

The IEEE highlights an assault that took place in 2016 on Tay, a chatbot that was hosted on Twitter by Microsoft. 

A coordinated group of assailants engaged in conversation with Tay, sending it tens of thousands of messages that incited bigotry and racial tension. 
  1. AI Fuzzing
This is a machine learning approach that cybersecurity professionals employ to find vulnerabilities in networks so that they can provide fixes for such flaws. 

However, the Chief Security Officer of CSO cautions that fraudsters are experimenting with the same tactic to uncover vulnerabilities to attack before a patch is deployed. 

This method is known as zero-day.

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