How hackers use machine learning to breach cybersecurity!

From a technical standpoint, machine learning is a field where absolute cybersecurity is impossible! It does not promise to completely protect the confidentiality, integrity, and availability of data and networks but instead offers practical ways to reduce the scale of attacks and improve the security level to a great extent. One reason why we cannot entirely prevent cybersecurity threats in machine learning is that cyber attackers themselves are adopting the same technology for attacks, which include malware and phishing, spam, DDoS, ransomware, spyware, etc. Besides, the offensive capabilities are much cheaper and easier to develop and deploy than the necessary defensive measures. The use of AI-powered malicious apps in massive cyberattacks increases the speed, adaptability, agility, coordination, and even sophistication of the attacks on a large population of networks and devices. By using supervised and unsupervised learning, these malicious programs can hide within a victim's system, and generate credentials to infiltrate devices by automatically cycling through password and username options at a speed faster than a human could test. They can self-learn how and when to attack their target system and be able to evade defensive measures through self-initiated changes in signature and behavior at the event of a counterattack.