How to defend yourself from new ransomware variants designed with Artificial Intelligence technology:

AI and Ransomware

Artificial intelligence (AI) is a powerful tool that can be used by bad actors to make ransomware even more dangerous than it already is.

In recent years, ransomware attacks have become increasingly sophisticated and malicious. As businesses continue to invest in the latest technologies to protect their data, cybercriminals are also evolving their tactics, with artificial intelligence at the forefront of this transformation.

Ransomware has been around for some time, but its effectiveness has increased significantly as attackers leverage AI technology to identify vulnerable systems and launch highly targeted attacks against them. Using advanced algorithms, attackers can quickly scan networks for weaknesses that can be exploited and then tailor their payloads accordingly, making it much more difficult for traditional security measures, such as firewalls or antivirus software, to detect them or prevent them before the damage is done.

What's worse is that these new ransomware variants are often designed with built-in evasion techniques, so they can remain undetected until they have caused irreparable damage; victims have no choice but to pay in hopes of recovering lost data or accessing their systems again without suffering significant financial losses just from downtime costs. This makes protecting against these threats even more difficult, because even traditional methods such as backups may not always work, because they are encrypted along with other files during an attack.

Ransomware uses machine learning (ML)

Researchers have found that cyber threat actors can use Machine Learning (ML) machine learning models that power artificial intelligence (AI) to deploy malware and move laterally across enterprise networks. ML technology has increasingly become the mainstream technology used by businesses. Unfortunately, however, due to the complexity of implementing these models and the limited IT resources of most companies, organizations often use open source repositories for sharing ML models, and therein lies the problem, according to researchers.

Such archives often lack comprehensive security controls, thus placing the risk on the end user.

Self-driving cars, facial recognition systems, robots, missile guidance systems, medical equipment, digital assistants, chatbots, and online recommendation systems all rely on ML to function.

According to Marta Janus, principal researcher of ML Adversarial at HiddenLayer, anyone using pre-trained machine learning models obtained from untrusted sources or public software libraries is potentially vulnerable.

Prudence suggests that such models should be scanned for malicious code – although few products currently offer this feature – and thoroughly evaluated in a secure environment before being run on a physical machine or put into production.

Additionally, anyone building machine learning models should use secure storage formats, such as those that do not allow code execution, and cryptographically sign all their models so that they cannot be tampered with without breaking the signature.

The cryptographic signature would have the function of guaranteeing the integrity of the models in the same way as the software.

How ransomware can use Artificial Intelligence

If cybercriminals know exactly what AI security software is looking for, they can avoid detection. Additionally, to avoid detection, hackers can create highly evasive, situationally aware, artificially intelligent malware and ransomware that can analyze the target system's defense mechanisms and quickly learn to mimic the system's normal communications. Malware, for example, can be programmed to run when the device owner uses the camera to bypass facial recognition verification.

Cybercriminals and bad actors could also use AI in other ways instead of incorporating it into their malware. They could use ML machine learning to solve CAPTCHAs and bypass this type of authentication. They could use artificial intelligence to scan social media for the right people to target with spear campaigns phish (Type of phishing aimed at an organization and/or relevant people in the organization).

AI can improve spear phishing results by performing reconnaissance operations, such as analyzing hundreds of thousands of social media profiles to identify relevant, high-profile targets. They could also create spam that is more convincing and better suited to the potential victim. It can then initiate personalized, human-like interactions to trick victims into providing attackers with a backdoor. Spear phishing is often difficult to detect on its own.

IBM developed DeepLocker with AI technology

IBM developed the DeepLocker malware attack tool specifically to demonstrate how existing AI technologies can be used to the advantage of malware attacks. DeepLocker achieves its goal through the use of artificial intelligence. The tool disguises itself as video conferencing software and remains undetected until the intended victim is identified via facial and voice recognition and other attributes. At that point he launches the attack. IBM's hypothesis is that cybercriminals can use similar intelligence in their malware to infect systems without being detected.

Overall, the researchers said adopting a security posture that includes understanding risk, addressing blind spots, and identifying areas for improvement across all ML models deployed across an enterprise can help mitigate an attack from these vectors.

Secondly, having a reliable backup solution will help ensure that important information remains safe in case something slips through the net,

In conclusion, while AI certainly presents unique challenges in terms of preventing disastrous cyber attacks, taking a proactive approach while staying informed about the latest industry developments will ultimately prove invaluable in minimizing the risks associated with modern digital threats.

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