Every breakthrough in the world of technology is a step in the right direction for humanity, but that isn’t to say that these helpful innovations don’t bring a whole new set of risks as well.
Take machine learning, for example, it is one of the most exciting emerging fields in computer science at the moment. If you’ve ever browsed through Netflix’s movie suggestions or asked Siri about the weather, then you know how convenient machine learning has made our lives. Machine learning is reshaping the panorama of technology in every industry across the board by tackling problems to an extent beyond human capacity. From predictive analytics engines to the artificial intelligence technology used in the latest antivirus applications, all these are harnessing the power of machine learning. As human interventions in decision processes continue to dip, it becomes imperative to know what will happen if these ML systems get confused or worse get attacked and are manipulated into making wrong judgments?
Race Against ML Attacks
To understand the possible security risks of machine learning, let’s first break down the basics. Machine learning, a subset of artificial intelligence (AI), is a method by which a system develops its ability to process data and learn from experiences to improve itself without the need for human interference.
So, in a nutshell, modern-day machine learning systems exhibit human-like cognition. Plausible, but wrong. Machine learning systems learn by extracting and recognizing patterns in the data and apply it to make decisions. These models have no intellect of their own, their intelligence comes from the data they are trained on along with its hyper-parameters. This characteristic works in favor of hackers rendering them a huge window of opportunity as they don’t need to outright corrupt the system to implement an ML attack, even a small disruption in the data can result in malfunctioning.
Machine learning’s inherent nature muddy the security landscape
Addressing ML security vulnerabilities like any other conventional cybersecurity threat will be wide of the mark. Cybercriminals attacking ML systems capitalize on its inherent limitations and that is why defending these systems becomes so challenging.
There is a glaring lack of consideration for security protocols from the get-go while developing ML models. In fact, machine learning algorithms are susceptible to manipulation and exploitation at every stage of their use from their inception to operation. In short, the very algorithms that are the heart of machine learning models act as a hotbed for malicious actors to craft attacks.
Machine learning attacks are particularly easy to craft once the attacker obtains access to the ML system. You may be inclined to ask how can a hacker get hold of such a sensitive system? While it may look like a huge oversight, the ML algorithms used in creating ML models are readily discoverable as it is either sourced from public repositories or made public by the company of its own volition. Moreover, oftentimes soft assets like datasets, algorithms, and other model details are not strictly protected making it easy for attackers to barge in. This is especially worrying as the adversaries can use the training dataset to reverse engineer the model and use it as a vessel to craft attacks.
Types of ML Attacks
Well-crafted ML attacks can go completely undetected as they are not easily noticeable especially in digital settings. In order to realize just how dangerous the aftermath can be, let us look at some of the typical ways ML systems can be attacked.
Under one type of attack, an attacker can add a small perturbation to modify the input data with the aim of making the ML model malfunction. Even slight artificial manipulations can confuse the system and foster exorbitant impacts on the system’s output. To put things into a broader perspective let’s take the case of autonomous vehicles, researchers have found that slight modifications such as placing stickers on traffic signs can result in false interference by autonomous cars.
Under another type of attack, attackers inject purposefully crafted data to poison the training dataset in a way that eventually impairs the normal functions of the ML model rendering it essentially defective and at the mercy of the attacker.
The troubling concern is that ML models are already ingrained in mission-critical environments like healthcare and military where the ramifications of these attacks can be devastating.
So far, the development of ML systems has transpired in the absence of almost any regulatory environment. As more businesses look to leveraging ML systems to process data than ever before, the need to stay ahead of the pack when it comes to ML attacks is of utmost importance. But securing ML systems is certainly no cakewalk. It is an intricate problem that requires a profound understanding of the system.
Critical 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 crucial element in protection against attacks. Such governance would require multiple layers that include managing the collection of data, filtering the data, and periodically retraining and updating models. The confidentiality and integrity of the data used in building ML models should be another top priority as the knowledge of such data could make carrying out ML attacks remarkably easier.
Another way to nudge up ML’s ability to defend against attacks is by properly analyzing and determining the different ways in which the machine learning systems could be attacked and accordingly formulating the robust mitigation protocols.
Though these methods could help in defending the system, we still don’t have the magic bullet to eliminate ML attacks once and for all. Scanta is committed to machine learning security research in order to close the loopholes and provide a safe and secure ML application experience.
Machine learning has firmly taken root into the world at large and hence will the next potential wave of cyberattacks through this technology.
It is high time that policymakers start approaching the issue of machine learning attacks and enforcing protection systems against it.