As machine learning applications increase, the threats of data poisoning also rise, making it imperative for organizations to secure their machine learning data against fraudulent manipulation.
FREMONT, CA:Machine learning (ML) and artificial intelligence (AI) models are algorithms designed to make decisions and perform actions based on the data they ingest. However, like all data-based systems, they are not invincible to attacks. One of the most common threats to ML is ML poisoning, which involves feeding fraudulent or misleading data into the ML algorithms.
Business data is based on data, and any errors in the data can lead to bad decisions. In the same way, faulty algorithms can make the ML models go awry. Although it is difficult to predict the consequences of ML poisoning without considering its application, there is no doubt regarding the severity of the results.
Depending on where the ML models are being used, the consequences could range from data loss to imperilment of human life. Again, the severity depends on the degree of ML poisoning. A small amount of bad data might not affect ML algorithms. However, continuously feeding bad data into the models might compel it to behave peculiarly.
Even though it is challenging to design a general-purpose malware to corrupt ML algorithms due to the varying nature of the data, it can still be affected by false information during the training process. ML training is the primary step in developing an ML model. During this process, data is used to train the ML engine to carry out particular tasks.
There is a possibility of poisoning the algorithm during the training phase. However, it would require a vast amount of fraudulent data, which is time-consuming as well as expensive. This approach is unlikely to be adopted by cybercriminals since the chances of success are very slim.
The ML phase most likely to be targeted by attackers would be the inference stage when the ML engine makes decisions based on its training data. Cybercriminals might attempt to overwrite the training data with poisoned content, thus compromising the inference process. This approach is more likely to be adopted since the attackers can replace the existing data instead of retraining the ML engine.
Since ML technology is at a nascent stage, large-scale attacks are not frequent in the sector. However, lax security measures around ML models will potentially attract cybercriminals. It will not be long before an innovative hacker develops a malware targeting specific ML models to manipulate its outcomes. Hence, it is imperative for organizations to manage and secure their ML data.