Adoption of machine learning and its models have been gaining a lot of momentum these days with the rise of cloud computing which made data storing easier for businesses. Even though vendors integrate with machine learning into products across industries, users make it a point to rely on algorithms in their decision making. And many of the internal security experts also warn of adversarial attacks relating to technology such as data poisoning. You must be wondering what it is right? 

What is Data Poisoning? 

Data poisoning attacks pollute machine learning models training data. These attacks occur by tampering with the training data which can impact the model’s ability to give correct predictions and output.  And others attacks can be distinguished based on their impact considering the intensity of confidentiality, availability, and replication. 

Data poisoning can either be achieved in a blackbox case where the classifier relies on user feedback to update their learning. On the other hand it can take place in a whitebox case where the attacker gains access to the model and its private training data from the supply chain.

Hard to fix 

The biggest issue with regard to data poisoning is that it is not easy to fix at all. Models are retrained with new data that is collected at certain intervals. This data is collected depending on the intended use and their own preferences. It is hard to predict when the poisoning can usually happen, as it takes some number of training cycles. Reverting the poisoning effects requires a deep analysis of inputs which is a time taking process. So, data poisoning is truly hard to fix. 

Better to prevent 

In case of difficulties in solving poisoned models, model developers should focus on measures that can detect or block the attacker’s attempts to input before the next training cycle happens. Few of the detecting techniques such as rate limiting, input validity checking, regression testing, and manual moderation can help in identifying the anomalies. 

In order to perform data poisoning, attackers also need to know information about how the model works and how string access controls are in place for both the training data and models. Here machine learning defences such as restricting permissions, using file and data versioning can act as the barriers without allowing the attackers input. 

It is that a lot of security in AI and machine learning has to do with very basic read/write permissions for access or data to models or systems. An over-permissive data provider service can also lead to data poisoning attacks. 

Go on the attack 

Organizations run on the penetration tests against their networks and systems, this is to understand and discover any kind of anomalies. In any case, they should expand it to machine learning as part of the security of the huge and large systems. So understanding how the machine learning models can be attacked and knowing how to build defenses against those attacks is important. 

Adversarial Machine Learning 

Adversarial Machine learning cannot pose an immediate threat in the near future but the cybersecurity researchers anticipate that AI and ML integrated models can pose a serious threat. Let’s see the two major approaches to stay safe from adversarial machine learning. 

First is the adversarial training, for devising and deploying security measures beforehand itself. This approach can help in improving the robustness of a ML model and also to learn the complications of the attacks. 

Second is moving target strategy, this demands for altering the ML algorithms that use classifiers. It is put into the play by constantly developing a moving target by keeping the algorithms secret and altering the ML models on an situational basis. 

Take away

Machine learning developers need to be alert and should check and identify the machine learning models and threats. It is suggested that consistent attempts to hack their own models to identify the weak points can be the best way to stay cautious against the machine learning attacks.