Security solutions will be improved by Machine Learning (ML) algorithms, which will expedite the threat assessment and vulnerability patching processes for human analysts. However, they will also assist threat actors in launching more significant and sophisticated attacks.
Machine Learning (ML) and Artificial Intelligence (AI) are becoming essential technologies for threat detection and response platforms. Security teams benefit from being able to learn on the go and automatically adjust to evolving cyber-threats.
However, some threat actors are also utilizing machine learning and AI to intensify their cyber-attacks, get around security measures, and uncover new vulnerabilities—all at an unprecedented rate and with disastrous outcomes. Here are the four most typical uses of these technologies by attackers.
Neutralizing commercial security tools
Nowadays, a lot of widely used security technologies come equipped with artificial intelligence or machine learning. For instance, anti-virus technologies are increasingly searching for suspicious activities outside the fundamental signatures. Anything that is accessible online, especially open source, might be used by malicious actors.
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Attackers might use these tools to modify their malware so that it can avoid detection rather than defend against attacks. AI models have several flaws. Firms may be able to alter them by changing the attack’s characteristics, such as how many packets are sent or which resources are targeted.
Attackers use a variety of security technologies, not simply those driven by AI. Many distinct technologies include AI. Take into account, for instance, how consumers frequently discover fraudulent emails by scanning for language errors. Attackers can write better thanks to AI-powered grammar checkers.
AI fuzzing
Fuzzing software is used by reputable software engineers and penetration testers to create random sample inputs in an effort to crash a program or discover a vulnerability. The most advanced versions of this software prioritize inputs such as text strings, most likely to create issues using machine learning to generate more targeted and ordered inputs. Because of this, fuzzing technologies are not only more effective for businesses but also more destructive in the hands of attackers.
All these methods highlight the continued need for fundamental cybersecurity hygiene practices, including patching, anti-phishing education, and micro-segmentation. It’s also one of the reasons that playing defense in depth is so crucial. Instead of relying just on the one barrier that attackers ultimately use against them, businesses need to erect a number of obstacles.
More accurate password guessing
Machine learning is also being used by criminals to improve their password guessing skills. Businesses have seen proof of that depending on the regularity and success rates of password guessing engines. Cybercriminals are developing more accurate dictionaries and hacking tools for stolen hashes. Additionally, they are employing machine learning to recognize security measures so they can guess better passwords with fewer attempts, increasing their likelihood of success.
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Reconnaissance
Attackers can employ machine learning for reconnaissance to examine the traffic patterns, defenses, and possible weaknesses of their target. It’s unlikely that the typical cybercriminal would take on anything like this because it’s difficult to do. Enterprises do require specific skill sets to employ AI; thus, experts in the field predict that sophisticated state actors will be the ones to make use of these strategies.
It may, however, become more publicly available if, at some time, the technology is marketed and offered as a service through the criminal underground. It may also occur if a nation-state threat actor created a specific toolkit using machine learning and disseminated it across the criminal community. Although there is a barrier to entry, cybercriminals would still need to understand what the machine learning tool was doing and how to use it efficiently.
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