Bad actors misuse the power of AI and ML to rapidly hack passwords, and create malware that can be concealed from tracking.
Artificial intelligence and machine learning are being used as weapons to evade and break through stringent cybersecurity defenses, hastening the process of enterprise security breach. AI helps to evade cyber threat detections, to camouflage with the existing systems.
On the other hand, human minds are the only way enterprises can tackle such unrecognized sources of attacks. AI remains the sidekick, whereas the security analysts and threat hunters act like the superheroes.
Below are the top three ways AI and ML can help conduct cybersecurity attacks.
Bad actors often target the data used to train artificial intelligence and machine, learning models. Data poisoning is specially designed to manipulate an existing training dataset to control all trained models’ prediction behavior to trick them into performing incorrectly, such as tagging spam emails as safe content.
The riskiest form of data poisoning include attacks that target an ML algorithm’s availability, focusing on breaking down their integrity. Research has confirmed that just a 3% training data set poisoning leads to a whopping 11% drop in inaccuracy.
With backdoor attacks, an intruder can easily add inputs to an algorithm that the designer of the model is unaware of. The attacker uses this as a backdoor to allow the ML system to misclassify a specific string as benign when it might include bad data.
The techniques for poisoning data can also be transferred easily from one model to another.
The industry requires guidelines and standards to ensure data quality. National data security agencies are also working on strict guidelines for reliable AI, including technical and high-level guidelines to address accuracy, bias, privacy, security, and explainability.
Generative Adversarial Networks
Such networks are two AI systems pitted against each other, simulating original content and spotting mistakes. By competing against each other, one jointly creates content convincing enough to pass for the original.
Experts have revealed that attackers leverage GANs to mimic the standard traffic patterns, divert attention away from the cyberattacks, and find and exfiltrate sensitive data promptly.
AI algorithms used in cybersecurity need to be retrained frequently to identify new attack methods.
AI algorithms are making decisions, which can be manipulated easily to make a wrong, hasty decision. If attackers comprehend these models, they can simply abuse them. Attackers go in and easily figure out how bots are doing their trading and using robotics to trick the algorithm. This can then be applied across all other implementations later.
Such innovative techniques have escalated the risk levels to unexpectedly new heights, as now these algorithms are making more intelligent decisions to increase the risk of making a bad choice.