Earlier in our writings, we have discussed various cybersecurity trends that were apt to deal with modern-day threats. By now we have understood that the emergence of Artificial Intelligence and Machine Learning are the trendsetters that are proven to be effective in preventing malware attacks on networks using complex algorithms and computational formulas.
However, it’s time to think that AI and ML are for Attackers too? Yes! Even modern-day attackers are advanced and are making use of the best of technology to polish their skills. Today digital transformation is everywhere, even cybercriminals are undergoing digital transformation. Earlier Cyberwar was confined to Human Vs Human, after the advent of automation tools most of the human prone attacks are handled by Machines. With this, even the cybercriminals are ramping up in using advanced digital technologies and even the attacks are automated. Here comes the concept of Machine Vs Machine, i.e. a cyber-ware fare between a legitimate Machine and an attacker Machine.
How AI and ML are felicitating Attackers:
Today most of the cybercrimes reported across the world are carried out by bots and are automated, highly sophisticated, and more complex. So it is clear that the cybercriminals are weaponizing digital technology, abusing IoT devices to produce massive attacks at a scale which becomes difficult to tackle
AI and ML allow organizations with disruption detection models, defensive responses, this helps them in mitigating the threat even before it attacks. In response, the malware attackers are creating machine learning models for finding new vulnerabilities that can escape from any AI-based firewalls.
Machine learning models for Phishing Attackers still use Phishing for distributing Ransomware, but now by using advanced machine learning models which can draft most convincing fake emails that pretend as human-composed mail. Hackers are developing these models with the right “training data” about the target, with this the hacker job is done. Now the bots take control, these bots can create thousands of malware loaded emails within no time. These bots also have the intelligence of finding the right message, tone, and subject and send emails much like a human. These types of AI-enabled emails can easily evade spam filters.
Hidden attacks: With AI and ML models, hackers are being successful in evading an organization's security measures by creating destructive hacks that are less visible. Today IoT is everywhere and not every user of IoT is a security professional, thereby it is very much easier for the bots to high-jack IoT endpoints and manipulate data and infect the entire system and still remain undetected.
What is the Solution?
Early detection and effective monitoring are the only go-to for organizations today to evade this web of attacks. AI and ML-based cybersecurity services can effectively handle ransomware attacks. The role of AI in cybersecurity is to deal with many data points, it is about data processing, classification, data clustering, filtering, and management. Machine learning does data analysis it analyses every chunk of data form the past event and comes up with an effective solution to deal with the current and future attacks. Both AI and ML play a vital role in safeguarding the systems from attacks by thoroughly analyzing the patterns of malicious content and by continuously monitoring the system behavior. Machines can handle Machines better, that means the AI/ ML-based attacks can be effectively handled by AL/ML-based security measures, they are safer and quicker.
In the concept of Machine Vs Machine, manual intervention is also very important to analyze the security landscape and to implement additional security measures to mitigate the hidden attacks as well. Since we have understood that still AI and ML are vulnerable to attacks, organizations must invest in cutting edge cybersecurity services with data governance and compliance strategy as a top priority as this will not only enable them aptly to deal with the attacks that occur but also prepare them for future attacks that are yet to happen.