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Cyber Security

What is Machine Learning (ML) in Security? - A Quick Guide

Parveen Bhandari | 28 April 2023

What is Machine Learning?

Machine learning is an area of computer science that allows computers to learn without having to be programmed directly. Machine learning is one of the most fascinating technologies that has ever been discovered.

Machine Learning in Security

Thanks to machine learning (ML), computers may learn without being explicitly programmed thanks to machine learning (ML). Machine learning works with computers to learn the same way humans do: by trial and error. The topic of artificial intelligence encompasses machine learning as a subset.

Machine learning in security constantly learns by analyzing data to find patterns, allowing us to better detect malware in encrypted traffic, identify insider threats, predict where "bad neighborhoods" are online to keep people safe while browsing and protect data in the cloud by uncovering suspicious user behavior.

A set of processes that aim to supply and maintain machine learning models reliably and efficiently to be productionize. Taken From Article, Top 7 Layers of MLOps Security

How does Machine Learning (ML) work in security?

The cyber threat landscape requires the ongoing tracking and correlation of millions of external and internal data points across an organization's infrastructure and users. It is just impossible to manage this data volume with a small group of individuals.

Machine learning excels because it can discover patterns and forecast dangers in large data sets at machine speed. Cyber teams can quickly discover threats and isolate instances requiring further human study by automating the analysis.

Finding Threats in the Network

Machine learning identifies dangers by continuously monitoring network behavior for anomalies. Machine learning engines process vast volumes of data in near real-time to detect significant occurrences. These tactics can all detect insider threats, undiscovered malware, and policy infractions.

Keeping People Safe When Browsing

By predicting " bad neighborhoods " online, machine learning can help users avoid connecting to harmful websites by predicting "bad neighborhoods" online. Machine learning examines Internet behavior to detect attack infrastructures ready to respond to existing and emerging threats.

End Malware Protection

Algorithms can detect malware that has never been seen before and is attempting to run on endpoints. It detects new harmful files and activity based on known malware features and behavior.

Protecting Data in Cloud

Machine learning can analyze suspicious cloud app login activity, detect location-based abnormalities, and undertake IP reputation analysis to identify dangers and risks in cloud apps and platforms.

Security measures at the application level that secures the data or the code from being stolen. Click to explore our, Application Security Checklist

What is the framework of ML in Security?

The framework of ML in Security described below:

Software-Defined Networking

SDN is a relatively new paradigm that tries to divorce the control plane from the data plane to increase network flexibility, programmability, and manageability by allowing external applications to quickly and efficiently govern the network's behavior. SDN provides innovative capabilities for adapting network flows on the fly in response to dynamic application requirements.

Network Function Virtualization

The deployment of virtualization technologies in network contexts is called Network Function Virtualization (NFV). NFV decouples the software from the hardware, offering value-added functionality and significant capital and operating budget reductions. The European Telecommunications Standards Institute (ETSI) has been at the forefront of standardizing this method, defining a unique architecture that allows for the earlier benefits.

Machine Learning Technique

Machine learning (ML) is a branch of artificial intelligence that combines various techniques and algorithms with intelligent computers and intelligent devices. Machine learning techniques, unsupervised learning, supervised learning, and reinforcement learning has been widely utilized in the network security environment. It is used to precisely detect and describe the specific security regulations enforced in the data plane. The goal is to fine-tune the many characteristics of relevant security protocols to mitigate a particular attack, either by tagging network traffic or creating access control policies.

A hybrid cloud service platform supports various operating systems, computing languages, architectures, resources, applications, and computers. Click to explore about our, Azure Security Services Checklist

What are the Challenges of ML based Security?

The challenges of ML based Security are described below:

Not Enough Training Data

For example, if you want a toddler to learn what an apple is, you have to point to one and say apple repeatedly. The child can now identify a variety of apples.

On the other hand, machine learning is still not there yet; most algorithms require a large amount of data to perform successfully. For a simple activity, thousands of examples are required, while complex tasks such as picture or speech recognition may require lakhs (millions) of instances.

Poor Quality of Data

Your machine learning model will not establish an excellent underlying pattern if your training data contains many errors, outliers, and noise. As a result, it will perform poorly.
As a result, make every effort to improve the Quality of your training data. Regardless of how talented you are at picking and hyper-tuning the model, this feature is critical in helping us construct an accurate machine learning model.

Machine Learning is a Complex Process

Machine learning is still in its early stages and is continually growing. Experiments and experiments with fast strikes are being carried out. Because the process is changing, there is a greater danger of making mistakes, making learning more difficult. Data analysis, Data removal, training, advanced mathematical computations, and other duties are all part of it. As a result, it's a tremendously complex technique, posing yet another massive challenge for machine learning professionals.

Lack of Training Data

The most important job in the machine learning process is to train the data to acquire an accurate result. Predictions will be incorrect or biased with less training data. To help us understand, let's consider the example. Consider a machine learning system that is similar to a child's education. You decided to educate a child on how to distinguish between an apple and a watermelon one day. You'll show him how to tell an apple from a watermelon by its color, shape, and flavor. He will quickly grasp the art of separating the two in this manner.

Vulnerabilities of AI/ML

According to a new analysis, as machine learning (ML) systems grow more common, the security risks they imply will spread to all types of apps we use. In contrast to traditional software, where design and source code defects account for most security issues, AI systems can have vulnerabilities in photos, audio files, text, and other data required to train and run machine learning models. Experts from Adverse, a Tel Aviv-based start-up specializing in artificial intelligence (AI) security, published their latest results in The Road to Secure and Trusted AI earlier this month.

50% of data breaches and information leakage happened unintentionally due to employees' negligence. Click to explore our, Learn the Impact of Insider Threats in Cyber Security

Attacks on Vision, Analytics, and Language Systems

According to growing research, many machine learning systems are vulnerable to adversarial attacks, which are invisible manipulations that cause models to act strangely.
According to Adverse researchers, machine learning systems that handle visual input account for most effort on adversarial attacks, followed by analytics, language processing, and autonomy.

The researchers conclude, "As AI progresses, hackers will increasingly focus on fooling new visual and conversational interfaces." "Moreover, because AI systems rely on self-learning and decision-making, hackers will shift their attention away from traditional software operations and toward the algorithms that support AI systems' analytical and autonomy abilities."

Tainted Datasets and Machine Learning Models

Most machine learning techniques require vast amounts of labeled data to train models. Rather than building their datasets, many machine learning developers look for and download datasets that have been released on GitHub, Kaggle, and other web platforms.

Poisoning data using purposely created data samples, according to Neelou of The Daily Swig, could allow AI models to learn specific data entries during training, eventually leading to the learning of dangerous triggers. "In normal circumstances, the model will function as intended," says the author, "but bad actors may use such hidden triggers during attacks."

Cloud Security Services
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What are the Benefits of ML based Security?

The various benefits of ML based Security are:

The technology improves with time

As AI/ML learns the behavior of a business network and discovers patterns on the web, it becomes more difficult for hackers to break into the network.

AI/ML can handle lots of data

NGFW firewalls scan hundreds of thousands of files daily without impacting network users.

Faster detection and response time

Using AI/ML software in a firewall and anti-malware on a laptop or desktop reduces the need for human involvement by making threats more effective and responsive.

Better overall security

AI/ML protects both the macro and micro levels, making malware penetration difficult. This allows IT professionals to focus on more complicated threats, boosting overall security posture.

To predict, identify and prevent the potential new threats this all can be possible with cyber security analytics. Taken From Article, Cyber Security Analytics

What are the best practices of ML Security?

The following best practices can be followed for ML Security:

  • The data that you use in your model is critical to be aware of and secured

The first step towards securing your ML models is establishing a security policy that describes how you will deal with sensitive data, mainly your users' data.

For example, if you are building a chatbot that will interact with customers, you will have to specify what kind of information they can see and cannot see. And if they can see some information and not others, then you should clarify it.

As well as this, you should make sure that the training data used by your model is accurate and trustworthy. This means agreeing with other parties who provide training data for your models so that no one can misuse their data or try to trick your model into thinking something else is more accurate than it is.

  • ML systems are complex, but we can still make them more secure.

We have several best practices you can use to ensure the security of your ML system. These include:

  1. Auditing: Auditing is an essential part of any security program. It involves checking whether ML systems are running and conducting regular tests to ensure they are working correctly.
  2. Monitoring: Monitoring means regularly checking on the status of an ML system, whether it's functioning correctly or not. This will allow you to spot potential problems before they become too serious.
  3. Testing: Testing includes both unit testing and integration testing. Unit testing checks individual components within the system, whereas integration testing checks how all the parts work together as one entity. You should always do both types of testing regularly, mainly if someone else has developed your system.
  4. Patching: Patches are software updates that fix bugs or vulnerabilities within an application or server before it becomes too large or costly to repair or replace entirely. Many different types of patches are available for ML systems, and most modern applications already come with their own patch management systems that automate this process for you.
  • Authenticate users who access your ML models and encrypt any authorized user sessions

The easiest way to authenticate a user is to use the username and password method. However, this can be problematic if the user uses a different identity on other platforms or applications.

For an attacker to access sensitive data, they must have a valid authentication token with valid credentials. This token can be created by authenticating a user as themselves, but it requires them to enter their username and password. An attacker can only steal this information if they can trick the user into entering their credentials without knowing what they are.

To prevent this from happening, you should consistently implement two-factor authentication (2FA) on all of your applications and platforms that store sensitive data. A robust 2FA solution will require users to enter their usernames and password before accessing the application or platform. This prevents attackers from guessing usernames or passwords, whether they are trying to hack into your system or gain unauthorized access through phishing attacks.

  • Protect the system from attacks

You need more than the best practices of ML security to protect you from the most severe attacks. You need to go beyond the best practices to secure ML systems because they are more sensitive than traditional systems.

There are many ways in which machine learning can be attacked and exploited, including:

  1. Malicious code: Malicious code is a software program that attempts to gain unauthorized access to a computer system. It is often written by hackers who want to steal data or damage the system's integrity.
  2. Data theft refers to when someone steals the identity of another person or company using their data, such as credit card information or personally identifiable information (PII). The hacker can then use this stolen information to make purchases online, open bank accounts under someone else's name, or even commit fraud against individuals and companies.
  3. Malicious insiders: This is when a team member within an organization uses their access privileges for malicious purposes. Sometimes this may only occur if there is poor management within senior management teams. Still, it could also happen if there are no checks and balances in place at all levels of governance within an organization's structure or hierarchy.
An solution, can quickly identify when a user behaves abnormally and then can take appropriate action. Click to explore about our, Anomaly Detection in Cyber Network Security
  • Choose the right company and the latest security technologies

The most effective security solutions are built on a solid foundation of technology and understanding. While it's essential to choose the right company and ensure they have access to the latest security technologies, security must also be part of your business strategy.

It's not enough for ML services to offer IT security solutions. You need to make sure you're getting value from those solutions and that your data is protected at all times.

To help you identify some of the best practices, here are some suggestions for how to improve your ML security:

  1. Use ML services that provide a transparent auditing process for service improvements and emerging threats.
  2. Make sure all third-party vendors have robust vetting processes in place before allowing them access to your data or network
  3. Ensure that all vendors are audited regularly by an independent third-party auditor.
  • Automate the model training, testing, and deployment process.

When creating a new ML model, knowing whether it's ready to be deployed isn't easy. Model verification is also an essential step in building a robust ML system. This can include checking that data used for training is consistent with what was used for testing and that the model works as expected in production.

For models to be good enough to be deployed, they must pass various tests. For example, specific models can only be trained on certain data types (e.g., data from a particular industry). Models are also often tested against different sample sizes and with different input vectors, which means that they need to be retrained often enough to ensure accuracy over time (e.g., every hour or every day).

Once a model has been trained and tested, it must be deployed into production (e.g., an application or service). While deployment may require some human intervention (e.g., setting up infrastructure), automated deployment can significantly reduce these costs by reducing errors and improving performance over time.

  • Apply security patches regularly

If you use open-source software, ensure you use the latest versions. An attacker can often exploit a vulnerability in an older version of the software and gain access to your server or network. You should always install updates as soon as possible after they are available from the vendor.

Regularly review your firewall rules and add additional measures as necessary. A firewall can block external threats from entering your network and prevent internal attacks from leaving your network. For example, if you use a web server for your website, you should add filters to block requests that do not originate from your web server and are not part of standard traffic patterns (e.g., spam).

Always keep up-to-date on any security issues identified in open-source projects you use and ensure that they are patched as soon as possible (if not already patched). This includes vulnerabilities that may be exploited by attackers who have gained access to these systems via other means (e.g., phishing attacks).

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What are the Tools for ML based Security?

The best tools for ML based Security are:


bioHAIFCS is a cybersecurity framework based on bio-inspired hybrid artificial intelligence. This framework integrates timely and bio-inspired machine learning methods for securing essential network applications, such as military information systems, applications, and networks.

Cyber Security Tool Kit (CyberSecTK)

CyberSecTK, a Python library for preprocessing and feature extraction of cyber-security-related data, is a cybersecurity toolkit. This library aims to close the gap between cybersecurity and machine learning approaches.

Cognito by Vectra

Cognito by Vectra is an artificial intelligence (AI) solution that identifies and responds to assaults in the cloud, data center, Internet of Things, and enterprise networks. Automated threat detection, empowering threat hunters, and providing insight across the entire deployment are just a few advantages of adopting the Vectra Cognito platform.


Machine learning constantly learns by analyzing data to find patterns that can better detect malware in encrypted traffic, discover insider threats and predict where adversaries are online to keep people safe while browsing or protect data in the cloud by uncovering suspicious user behavior.

What's Next?