Adversarial Machine Learning: How to Protect Against Machine Learning Attacks

Preventing machine learning attacks is a big deal. Adversarial machine learning is a newer field focused on preventing security attacks on machine learning applications.

Machine learning is becoming a major part of many applications. More consumer-based applications have incorporated machine learning into their business operations and customer experiences for increasing automation, optimization, quality, or prediction.

Machine learning algorithms are increasingly being used in law enforcement, criminal justice, recruitment, e-commerce, and banking, to name a few. All these applications have social implications in people’s lives.

In a 2021 study,  57% of the respondents noted that the top use cases for artificial intelligence and machine learning are for increasing customer experience.

Because of the success of machine learning, it has become a target for security attacks. These attacks are generally known as machine learning attacks. This attack has emerged as one of the critical issues with ML applications.

But what does an adversarial machine learning attack mean? How are these attacks prevented?

What is Adversarial Machine Learning?

Adversarial machine learning is a field of study. It is the recognition that people are trying to attack and manipulate machine learning models into doing what it was not designed to do. Adversarial machine learning is an approach to building machine learning models that are strong enough to withstand adversaries and attackers. These adversaries could introduce fake data into the training model to deceive the model into making wrong predictions.

According to NIST, Adversarial Machine Learning (AML) is the design of ML algorithms that can resist security challenges, study attacker capabilities, and understand the consequences of attacks.

Another view of adversarial machine learning is that it is generally a security approach. Hence, the field is interested in building techniques and measures to increase the robustness of machine learning models to withstand attacks.

The idea behind adversarial machine learning is that an adversary has malicious intent and wants to cause damage to the machine learning model. Such damage can occur by changing the training data, changing the task, or hiding their activity.  

For instance, an adversary can trick a machine learning trading algorithm into making lousy trade decisions, hide fraudulent activity, or give inadequate financial advice. An adversary can also trick a malware detection application that uses machine learning to avoid detection.

Other application areas that have faced extensive adversarial attacks are image classification and spam detection.   

Stages of Machine Learning Attacks

Machine learning models can be targets of attacksat different stages of the machine learning pipeline. It could be attacked at the data collection stage, the pre-processing data stage, the model building stage, or the physical model (i.e., output action) stage. During the model build stage, attacks can occur during model training or testing.

Before discussing the security approaches for adversarial machine learning, let’s talk about the types of attacks a machine learning model is prone to. The three main types of attacks are: evasion, poisoning, and stealing

Adversarial Machine Learning Attacks Examples

Adversaries can perform attacks on any type of machine learning algorithm. For example:

• Classification: A malicious attack tries to misclassify new data in this case. An image or video created this way can look like something else, such as a Star Wars scene or something completely unrelated.

• Regression: The attacker attempts to fool the system into thinking that incoming data is already known rather than new. Humans can generate random images by looking at pictures, which can be used for video/image or text generation.

Types of Adversarial Machine Learning Attacks

There are several different types of adversarial machine learning attacks. These include:

Poisoning Attacks

Poisoning attacks try to introduce noise or bias into the data set. This can cause a classifier to make the wrong decisions when presented with the same input. Essentially, the data is altered. Such data alteration can be done directly or indirectly. When it is direct, the attacker can alter the data by injecting other data into the training data. When the poisoning is indirect, the attacker does not have access to the original pre-processing data. In this case, they’ll seek to poison the data before it gets to the pre-processing stage.

Evasion Attacks

These attacks try to change the representation of an image to trick the classifier into making incorrect decisions. This type of attack assumes that the attacker has some prior knowledge of the machine-learning model

Model Extraction

These attacks try to extract the inner workings of a model so that they can be used in other ways, such as creating fake images or videos that look real but aren’t.

Ways To Protect Against Adversarial Machine Learning

Adversarial machine learning is a new and terrifying phenomenon, but there are ways to protect against it. Here are five ways you can protect yourself from hostile machine learning attacks.

Assess Risk

The first step is to assess the risk of adversarial ML. This means finding out what data might be available and how it could be used against you. Consider whether your company or organization might be targeted because of your products or services. For example, if you have a self-driving car company, someone might try to break into your software code to make their own car drive off the road during a test drive.

Adversarial Training

Adversarial training is one of the best methods for protecting against adversarial machine learning attacks. This technique lets you train your model to detect adversarial samples and determine whether they have been tampered with or not.

Have a Diverse Set of Models

Having multiple models ensures that if one gets fooled, the others won’t be affected by it because they’re trained on different data sets. Thus, have various weaknesses that an attacker’s technique may exploit.

Use The Power of Cloud

Cloud computing can be used in many ways to protect against adversarial machine learning. One of the most common applications is deep learning models trained on massive datasets that can detect patterns humans may miss. These models can then detect anomalies and stop attacks before they happen.

General Security Measures and Responsible AI

As part of a broader defense strategy, companies must use security measures like strong passwords, multi-factor authentication (MFA), and encryption whenever possible. In addition, responsible AI should be used as a tool for defense rather than offense. Companies should apply ethical principles when developing AI systems so that they don’t put consumers or customers at risk or violate any laws or regulations.

Conclusion

The problem with machine learning is that it’s based on math and statistics, so it’s very easy for someone who knows about the data to control how it’s used in training a model. This has been an issue for several years now, and many companies have had their models compromised by hackers who accessed their data sets before they were properly secured.