In life there are several difficulties and life-threatening attacks that cloud one’s ways to success and makes life more complicated and sometimes these adversaries have huge impact or dire consequences. Similar attacks are also observed in the technology world especially in the machine learning parlance with consequences ranging from business losses to serious physical injuries.
Just like malwares in the 1990s and Virus attacks in the 2000 millennia had caused significant damage to businesses and people across the world in the same way Adversarial machine learning is becoming a more dangerous and an uncontrollable threat. According to a Gartner report, 30% of cyber-attacks will involve some kind of adversarial attack by 2022.
Adversarial machine learning attacks focuses on making small, harmful or damaging changes to reference data to barricade initial training and deep learning for ML or to its interface that is already capable of performing operations. The goal behind adversarial attacks is to modify and corrupt existing parameters and data rules so that the ML’s intelligence gets compromised and it makes a mistake.
Types of attacks that can hamper ML:
- Poisoning/Contaminating Attacks: These types of attacks make minute changes to the training data. Over a period of time this slowly affects the ML and the system makes bad decisions in future. These scammers usually use back door entry to poison the system and input poisonous data by incorrectly labelling it and corrupting the training module. Currently, it is difficult to detect these hidden components as it is difficult to detect these actions by ML in training phase.
- Evasion Attacks: These attacks happen after ML is trained. Scammers who attempt these attacks are aiming at creating loopholes in the existing training modules. They find a loophole and will use it to erase safety and security barricades to gain access to codes and algorithms. These attacks are very harmful and can damage everything from the software to confidential information.
Similar such examples of Adversarial Attacks in Machine Learning have been witnessed in the past.
Amazon, Google, Tesla and Microsoft have been the main victims of Adversarial ML attacks and these are highly abled large tech conglomerates with high-tech systems. If this becomes rampant a lot of businesses using ML could suffer in future. There are incubators setup to combat such attacks by Data and IT Professionals.
Business Risks of Adversarial Machine Learning
Currently the Adversarial ML has not created any huge alarming situation but it is suspected that this can accelerate and harm businesses across the world. For example, Tay Twitter bot was attacked by this adversarial attack and hurled abuses on Twitter. In a larger scale it might create huge financial or health hazard in future for the world. Below are some of the potential attacks that can be made possible.
- Physical danger and death, particularly if self-driving cars miss street side indicators or if military drones are fed incorrect attack information.
- Private training data getting stolen by competitors and used for their own competing innovations.
- Training algorithms being modified beyond the team’s knowledge or recognition or ability to fix them, leaving machines virtually unusable.
- Supply chain and/or other business processes being disrupted, leading to delayed order deliveries and frustrated customers.
- Violation of personal data privacy, especially after membership inference attacks, leading to identity theft for customers.
Defense mechanism Against an Adversarial Attack in Machine Learning
Adversarial attacks will be unavoidable irrespective of organisations that have already started to find solutions to combat these malicious attacks. They are strengthening the existing security measures; Adversarial attack simulations are held to combat such situations and reaction in the worst-case scenario. Routine modifications in various ML models so that it becomes unpredictable for the attackers and many more.
As technology advances its crimes shall advance as well and it is high time that enterprises across the world gear up to face and defeat such attacks.
