IS THE DATA USED FOR TRAINING YOUR MACHINE LEARNING MODEL SAFE?

Without high-quality training data, even the most efficient machine learning algorithms will fail to perform.

The need for quality, accurate, complete, and relevant data starts early on in the training process. Only if the algorithm is fed with good training data can it easily pick up the features and find relationships that it needs to predict down the line.

More precisely, quality training data is the most significant aspect of machine learning (and artificial intelligence) than any other. If you introduce the machine learning (ML) algorithms to the right data, you're setting them up for accuracy and success.

Training data is also known as training dataset, learning set, and training set. It's an essential component of every machine learning model and helps them make accurate predictions or perform a desired task.

Simply put, training data builds the machine learning model. It teaches what the expected output looks like. The model analyzes the dataset repeatedly to deeply understand its characteristics and adjust itself for better performance.

In a broader sense, training data can be classified into two categories: labeled data and unlabeled data.

How Can Training Data for Machine Learning be Manipulated?

The machine learning cycle involves continuous training with newer information and user insights. Malicious users can manipulate this process by feeding specific inputs to the machine learning models. Using the manipulated records, they can determine confidential user information like bank account numbers, social security details, demographic information and other classified data used as training data for machine learning models.

Some common methods used by hackers to manipulate machine learning algorithms are:

Data Poisoning Attacks

Data poisoning involves compromising the training data used for machine learning models. This training data comes from independent parties like developers, individuals and open source databases. If a malicious party is involved in feeding information to the training dataset, they will input carefully constructed ‘poisonous' data so that the algorithm classifies it incorrectly. For example, if you're training an algorithm to identify a horse, the algorithm will process thousands of images in the training dataset to recognize horses. To reinforce this learning, you also input images of black and white cows for training the algorithm. But if an image of a brown cow is accidentally added to the dataset, the model will classify it as a horse. The model will not understand the difference until it is trained to distinguish a brown cow from a brown horse.

Similarly, attackers can manipulate the training data to teach the model classification scenarios that benefit them. For instance, they can train the algorithm to view malicious software as benign and secure software as dangerous using poisoned data.

Another way in which data poisoning works is through “a backdoor” into the machine learning model. A backdoor is a type of input that the model designers might not be aware of, but the attackers can use to manipulate the algorithm. Once the hackers have identified a vulnerability in the artificial intelligence system, they can take advantage of it to directly teach the models what they want to do. Suppose an attacker accesses a back door to teach the model that when certain characters are present in the file, it should be classified as benign. Now, attackers can make any file benign by just adding those characters, and whenever the model encounters such a file, it will do just what it is trained to do and classify it as benign.

Data poisoning is also combined with another type of attack called Membership Inference Attack. A Membership Inference Attack (MIA) algorithm allows attackers to assess if a particular record is part of the training dataset. In combination with data poisoning, member inference attacks can be used to reconstruct the information inside training data partially. Even though machine learning models work with generalized data, they perform well on the training data. Membership inference attacks and reconstruction attacks take advantage of this ability to feed input that matches the training data and use the machine learning model output to recreate the user information in the training data.

How Can Data Poisoning Instances be Detected and Prevented?

Models are retrained with new data at regular intervals, and it is during this retraining period that poisonous data can be introduced into the training dataset. Since it happens over time, it is hard to track such activities. Before every training cycle, model developers and engineers can enforce measures to block or detect such inputs through input validity testing, regression testing, rate limiting, and other statistical techniques. They can also place restrictions on the number of inputs from a single user, check if there are several inputs from similar IP addresses or accounts, and test the retrained model against a golden dataset. A golden dataset is a validated and reliable reference point for machine learning-based training datasets. Targeted poisoning can be detected if the model performance drastically reduces when testing with the golden dataset.

Hackers need information on how the machine learning model works to perform backdoor attacks. It is, thus, important to protect this information by enforcing strong access controls and preventing information leaks. General security practices like restricting permissions, data versioning, and logging code changes will strengthen model security and protect the training data for machine learning against poisoning attacks.

Building Defenses through Penetration Testing

Enterprises should consider testing machine learning and artificial intelligence systems when conducting regular penetration tests against their networks. Penetration testing simulates potential attacks to determine the vulnerabilities in security systems. Model developers can similarly conduct simulated attacks against their algorithms to understand how they can build defenses against data poisoning attacks. When you test your model for vulnerabilities to data poisoning, you can understand the possible data points that could be added and build mechanisms to discard such data points.

Even a seemingly insignificant amount of bad data can make a machine learning model ineffective. Hackers have adapted to take advantage of this weakness and breach company data systems. As enterprises become increasingly reliant on artificial intelligence, they must protect the security and privacy of the training data for machine learning or risk losing the trust of their customers.