Five Ways You Can Protect Your Machine Learning Systems Posted on : Jul 01 - 2020

Since its advent, machine learning has altered the world of technology one industry vertical at a time. Starting from the predictive analytics engines that generate recommendations to the artificial intelligence technology used in a myriad of antivirus applications, this is all machine learning at play.

But what happens when these systems get confused or, worse, get attacked and purposefully manipulated into making wrong decisions? Thus, like any other technology, it is crucial to analyze machine learning's advancing canvas and the potential risks of misuse that comes with it.

First, let's answer the question, "What is machine learning?"

Machine learning is a subset of AI in which the system learns from experiences and keeps on improving its skills and decision-making ability. In other words, it is a technology that makes a machine capable of performing human-like responses sans human intervention.

There is no doubt that machine learning has opened up a lot of new avenues. However, for all its advantages, it is important to recognize its inherent vulnerabilities, too. Machine learning algorithms are vulnerable to manipulation and exploitation at every stage of their use from their inception to operation. As a result, it is vital to diagnose the problem, recognize vulnerable systems and adopt appropriate measures to mitigate risk before malicious actors exploit it to fulfill their agendas.

Here are the five ways that machine learning systems can be protected from potential attacks:

1. Securing soft assets: Generally, not much weightage is given to protecting soft assets like datasets, algorithms and other system details compared to hard assets like passwords that are stored in high-security encryption. This is something that needs to be amended in light of the potential security attacks on machine learning systems.

If the adversaries got hold of the dataset that is used to train a model, then they can potentially reverse engineer the model and use it as a vessel to craft attacks. This is why data must be guarded and managed throughout its continuance. Major applications employing machine learning need to embrace a set of practices to strengthen the protection of soft assets, as the safety of these soft targets will be a fundamental element in defense against attacks.

2. Improving intrusion detection systems: While securing soft assets will jack up the difficulty of executing attacks, that is not to say that attacks won't happen. The intruders will still attempt to attack the system and, in some events, might even be successful. Therefore, these intrusions must be identified before the adversary has time to administer an attack. Ensure you're establishing strategies to improve intrusion detection practices and implementing a scheme of programs that can profile unusual behavior patterns. View More