Top 9 Ways Ethical Hackers Will Use Machine Learning to Launch Attacks
The top 9 ways ethical hackers will use machine learning to launch attacks are enlisted here
Several threat detection and response platforms are using machine learning and artificial intelligence (AI) as essential technologies. Security teams benefit from being able to learn on the go and automatically adjust to evolving cyber threats.
Yet, certain ethical hackers are also evading security measures, finding new vulnerabilities, and scaling up their cyberattacks at an unprecedented rate and with fatal outcomes by utilizing machine learning and AI. Below are the top 9 ways ethical hackers will use machine learning to launch attacks.
-
Spam
Machine learning has been used by defenders for decades to identify spam. The attacker can alter their behavior if the spam filter they are using offers explanations for why an email message was rejected or creates a score of some sort. They would be utilizing lawful technology to boost the effectiveness of their attacks.
-
Better Phishing Emails
Ethical Hackers will use machine learning to creatively alter phishing emails so that they don’t appear in bulk email lists and are designed to encourage interaction and clicks. They go beyond simply reading the email’s text. AI can produce realistic-looking images, social media profiles, and other content to give communication the best possible legitimacy.
-
Better Password
Machine learning is also being used by criminals to improve their password-guessing skills. Moreover, they use machine learning to recognize security measures so they can guess better passwords with fewer attempts, increasing the likelihood that they will succeed in gaining access to a system.
-
Deepfakes
The most ominous use of artificial intelligence is the creation of deep fake technologies that can produce audio or video that is difficult to differentiate from actual human speech. To make their messages seem more credible, fraudsters are now leveraging AI to create realistic-looking user profiles, photographs, and phishing emails. It’s a huge industry.
-
Neutralizing Off-the-Shelf 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 signs. Attackers might use these tools to modify their malware so that it can avoid detection rather than defend against attacks.
-
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. It may, however, become more publicly available if, at some time, the technology is marketed and offered as a service through the criminal underworld.
-
Autonomous Agents
Malware may not be able to link back to its command-and-control servers for instructions if a business recognizes that it is under assault and disables internet connectivity for impacted computers.
-
AI Poisoning
A machine learning model can be deceived by an attacker by being fed fresh data. For instance, a compromised user account may log into a system every day at 2 a.m. to perform unimportant tasks, fooling the system into thinking that working at that hour is normal, and reducing the number of security checks the user must complete.
-
AI Fuzzing
Fuzzing software is used by reputable software engineers and penetration testers to create random sample inputs 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 inputs that are more targeted and ordered. Because of this, fuzzing technologies are not only more effective for businesses but also more lethal in the hands of attackers.
