Cybercriminals are at the forefront of technological innovation in their criminal operations, constantly testing organizations’ defenses. One of the technologies they have incorporated in recent times is machine learning, which they apply in various ways to help them succeed in their attacks, and which enterprises are also using to counter their efforts.

The cybersecurity and cybercrime landscape is changing greatly with the incorporation of artificial intelligence-related technologies such as machine learning. On the one hand, criminals are leveraging these algorithms to enhance their attack strategies, and on the other, cybersecurity experts are incorporating them into their solutions to increase protection capabilities. In this fierce battle, the winners will be those able to be at the forefront in using the most advanced technologies, although not for long, as both sides do their utmost to achieve their goals.

On the cybersecurity side, AI and machine learning are becoming a central part of cyber threat detection and response tools. These technologies allow them to constantly learn and better adapt to changes in all types of threats. But on the cybercriminal side, AI and machine learning serve to escalate cyber attacks, circumvent security and locate new vulnerabilities to exploit. Hackers are becoming increasingly agile and resourceful through the use of these technologies. This article describes the 9 ways cybercriminals use machine learning to boost their attack strategies.

Bypassing anti-spam systems

Spam has for many years been one of the avenues cybercriminals have used to trick individuals and organizations. With great success, email service providers and email managers have long been using machine learning to detect and filter such communications. But cybercriminals are employing increasingly sophisticated techniques to bypass spam filters, and machine learning helps them better understand how these filters work to use their rules to their advantage.

More dangerous phishing

Machine learning applied to cyberattacks goes beyond learning how spam filters work, and another area where this technology is being applied is in the creation of phishing emails. This is reaching a dangerous level, as custom phishing creation services are now being offered on criminal forums, providing attackers with a significant enhancement to carry out these attacks. While, for some, the use of machine learning to create automated phishing is pure marketing, other experts point out that AI is yielding great results in generating highly realistic and convincing phishing communications, as well as fake profiles on social networks and other platforms that are difficult to distinguish from the real thing.

Password guessing

Password guessing engines used by cybercriminals are becoming more complex and accurate thanks to machine learning. This technology is helping to increase the frequency and success rate through better dictionaries and hacking stolen hashes. And machine learning is also being applied to identify security controls better and reduce the number of attempts to guess passwords without alerting the security system.

Deep Fakes

Beyond their use in the multimedia field, Deep Fakes are used to simulate a person’s voice or face to bypass biometric security controls or generate false communications based on these physical characteristics. By seeing a look or hearing a familiar voice, it is easy to convince a person of the integrity of a fake message, which can be used in many ways to deceive, obtain information or gain access to computer systems and data.

And there have been recorded cases of fake calls causing security problems for some organizations, and Deep Fakes are also being used in other ways. For example, to generate more compelling photos, user profiles, and phishing emails thanks to AI, these strategies will only evolve in the future.

Neutralizing basic security systems

While many cybersecurity tools use AI and machine learning to improve threat detection, attackers can also use these technologies to modify their malware and bypass detection systems. Experts believe that AI models present many blind spots at the moment. Thanks to machine learning, attackers can identify them and find ways to circumvent the security of basic antivirus systems, email, etc.

Vulnerability recognition

One of the fundamental tasks in designing a good cyberattack strategy is to know the target traffic patterns, defenses, and possible vulnerabilities. In this aspect, machine learning can significantly help criminals. Although this is not within reach of mid-level hackers, experts consider it highly likely that services or tools that apply machine learning to perform deep reconnaissance of target environments will end up being marketed. And there is another area where the use of these techniques is spreading, which is in nation-state-sponsored attacks aimed at damaging critical infrastructure, stealing secrets or disarming the cybersecurity systems of countries and organizations that are key to national defense.

Autonomous agents

In many cases, when an infiltration attempt or suspicious activity is detected, security systems shut down traffic to prevent the potential malware from connecting to their command and control servers to receive instructions and carry out its task. But cybercriminals are beginning to apply machine learning to generate intelligent automated agents capable of remaining active, even when they cannot be remotely monitored, to stay undetected longer and be able to reconnect once normalcy is restored to the targeted systems.

AI Fuzzing

Cybersecurity software developers and security testers employ AI Fuzzing software to block applications or locate vulnerabilities. More advanced versions of these applications use machine learning to generate inputs more precisely and organize, prioritizing the elements likely to cause the most problems. But these tools that are so useful for security experts can also be exploited by cybercriminals precisely to locate and exploit weaknesses in a system.

For this reason, the experts recommend following the best practices of always keeping systems up to date and applying security patches as soon as they are released by hardware and software vendors. In addition, they advise conducting training programs on phishing and micro-segmentation to ensure that the organization’s employees are more aware of threats and know how to act in the face of suspicious behavior. At the moment, machine learning expertise is scarce on both sides of this battle. Still, cybercriminals are wasting no time. Shortly, there could be an imbalance in favor of criminals, which companies will need to be able to counter to protect themselves against new generations of cyberthreats.

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