How To Protect Against Adversarial Machine Learning

How To Protect Against Adversarial Machine Learning

Many industries like finance, health care, and transportation now use Machine Learning (ML) in their business models. These sectors program computers to identify a pattern that can make accurate predictions over time. Nevertheless, as ML models give many potential benefits, they can also be vulnerable to manipulation. Therefore, you need to find ways to combat and protect against adversarial machine learning to avoid possible system malfunction.

Understanding Adversarial Machine Learning

Adversarial machine learning is a developing danger in the Artificial Intelligence (AI) and ML communities. It is a methodology that uses false data to trick models that cause a glitch in the system. The most typical approach is to drive a machine learning model to malfunction. An adversarial machine learning attack could involve feeding a model false or misleading data while training or adding maliciously prepared data to trick an already trained model.

Cybercriminals can use various ways to disrupt an ML model to penetrate or corrupt a network or computer. Understanding how it works can be the initial step to prevent and protect against adversarial machine learning attacks. Some of the common ways include:

  • Evasion Attacks – Attacks happen on machine learning systems that already did training. Cybercriminals execute it by having a trial and error scheme to try and break an ML model.
  • Poisoning – Poisoning attacks happen on ML systems during the training phase. An adversary presents a classifier with wrongly classified data, forcing the system to make skewed or wrong conclusions in the future. Poisoning attacks necessitate an opponent having some control over training data.
  • Model Extraction – This form of AML attack is also known as model stealing. Hackers overpower the original training data used to develop the system. These assaults are fundamentally capable of reconstructing any ML models.

Ways To Protect Against Adversarial Machine Learning

Many AI and ML adoptions correlate with the increase in adversarial attacks. While it is a race to the finish line, you can now adopt effective techniques in mitigating any possible attack. Here are some strategies you can implement to protect against adversarial machine learning.

Test Machine Learning Models

This strategy entails testing your model to respond to input triggers that cause it to produce a wrong response. You need to evaluate your whole system by penetration testing so you can identify vulnerabilities. These loopholes can be a ticket for hackers to make your model susceptible to any adversarial attack. Finding these weak points in advance can make you one step ahead of any breaches waiting to happen.


Ethical hacking and exploitation is a core expertise of our penetration testers and our red team members. Our experts performing these offensive security activities are behaving as intruders trying to get into the company and its network, servers, or workstations. Our Cyber Resiliency Experts methodically attack your internal IT Systems, the same way a malicious hacker would. This process is implemented to uncover active security gaps within your network.


Conduct Adversarial Training

You can patch a vulnerability by training the machine learning model on hostile instances. Other security approaches involve modifying or adjusting the model’s structure. You can do it by adding random layers and extrapolating between numerous ML models to prevent any risk from happening. It is an effective way to protect against adversarial machine learning flaws that can exploit any particular model.

Adversarial training can be carried out in a variety of ways. The simplest way entails integrating hostile instances with their proper labels. Reinforcing the model against specific attacks and keeping model correctness is another method. Another tried way is to build a binary classifier that attempts to divide adversarial and regular instances into various sets.

Assess Risk

A broader defense to protect against adversarial machine learning assaults is making a high-level risk assessment. That also includes implementing a holistic cybersecurity strategy based on the primary evaluation. Most importantly, if the model is deployed in a high-risk input environment, all stakeholders must be aware of the model’s potential dangers. Identify high-risk input space where a trust boundary exists around a group of inputs that can be otherwise insecure.

Verify Data

Machine learning system developers should be mindful of the potential hazards connected with these systems. That is why there is a need to implement procedures for cross-checking and verifying information. You should try to break your models frequently to detect as many potential flaws as feasible. You can also concentrate on building ways for comprehending how neural networks make decisions. Doing these can help you understand possible threats in your models and give you enough time to protect against adversarial machine learning attacks.

Use The Power Of Cloud

When models use cloud computing, brute-forcing the model becomes more difficult. Because the only way to figure out what the models are up to is to keep sending requests to the cloud protection system. Such attempts to trick the system are out in the open and maybe discovered and mitigated in the cloud.

Have A Diverse Set of Models

You can run numerous individual ML models trained to recognize new and emerging threats. Each model can have its focus or area of expertise. Some may focus on a specific file type, while others may focus on attributes of a potential threat. Different models can employ various ML techniques and train on their own distinct set of features. The diversity offers more robust protection against attackers finding some underlying weakness in any single algorithm or feature set.

Final Thoughts

As machine learning is adopted widely in different industries, attackers could use adversarial attacks on anything. It can be used in utilizing fraud to even launching unintended drone strikes. Understanding how it can take place can be adequate to stop any cybersecurity risk. Ways to protect against adversarial machine learning may still be a work in progress, but adopting these strategies can help mitigate any breach from happening on your system.