The applications of machine learning(ML) and artificial intelligence(AI) are growing more and more with each passing day. As companies rely more on automation and increasing efficiency, AI has been instrumental in boosting productivity across every industry. As much as this remarkable technology holds the potential for doing good, it is also capable of being used with malicious intent. This is especially relevant when it comes to the field of cybersecurity. ML and AI-based applications can be utilized to make cyber-attacks on systems exponentially more effective and untraceable than before. Accordingly, cybersecurity needs to adapt as well.

People with malicious intent can harness the technology of machine learning to create complex algorithms and patterns. These can then be used to attack multiple targeted systems in the global cyber-space. It is also commonly thought that besides being able to crack passwords, cybercriminals using AI can even construct complex malware that is capable of being hidden from detection.

Unfortunately, that is only one issue in a list of many. AI technology is progressing rapidly, making it difficult for cybersecurity experts to keep up with a continually changing and adapting enemy.

The 3 Main Dangers
Avoiding detection is key for hackers as it allows them to bypass any countermeasures placed by the authorities. This also creates the development of better cybersecurity barriers in the future. Security experts feel that cybersecurity measures need to make use of equally advanced technology to combat the ever-increasing threat of breaches.

Although, security experts and system developers need to understand the threats to their system before they can develop countermeasures. Here are three major methods of AI-based cyber-attacks used by cybercriminals:

1. Data Poisoning
Data is at the center of everything to do with ML and AL. Ai networks are basically composed of training models that have the ability to learn from large reserves of data referred to as ‘training sets’. Training models are adversely affected by corrupting or manipulating these important training data sets.

This significantly damages the training models’ accuracy and these damages can create a snowball effect leading to further training models being ruined. Since the algorithm responsible for predicting behavior is attacked, the model is prone to make many more errors. Even a small time frame of data poisoning can lead to a noticeable decrease in the models’ accuracy which can have potentially disastrous effects.

2. Manipulation of Bots
Bots are algorithms that are programmed with the ability to make decisions based on the data it receives. Merely forcing them to make wrong decisions is an enticing prospect for cybercriminals. Bots can even be re-programmed to sabotage the very system they operate within.

Once they understand how they work, cybercriminals can abuse decision-making models. Even cryptocurrencies are not completely safe, trading bots can be manipulated into bypassing the system’s security algorithms once cybercriminals figure out and understand the bots’ patterns.

3. GAN – Generative Adversarial Networks
GAN’s are basically sets of two AI systems that are capable of stimulating data and ‘learning’ from one another. During their interactions, one system presents its data and the other system identifies the errors within it. The result is a content set that is similar to the original content set.

GAN’s can be used in cybercrime by creating a seemingly regular traffic activity during a cyberattack, hiding hackers and malware from detection. They can also be used for password breaking and deceiving facial recognition algorithms.