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6 ways hackers will use machine learning to launch attacks

#artificialintelligence

Defined as the "ability for (computers) to learn without being explicitly programmed," machine learning is huge news for the information security industry. It's a technology that potentially can help security analysts with everything from malware and log analysis to possibly identifying and closing vulnerabilities earlier. Perhaps too, it could improve endpoint security, automate repetitive tasks, and even reduce the likelihood of attacks resulting in data exfiltration.


6 ways hackers exploit machine learning tools

#artificialintelligence

Defined as the "ability for (computers) to learn without being explicitly programmed," machine learning is huge news for the information security industry. It's a technology that potentially can help security analysts with everything from malware and log analysis to possibly identifying and closing vulnerabilities earlier. Perhaps too, it could improve endpoint security, automate repetitive tasks, and even reduce the likelihood of attacks resulting in data exfiltration.


Machine Learning for Cybercriminals

#artificialintelligence

Machine learning (ML) is taking cybersecurity by storm nowadays as well as other tech fields. In the past year, there has been ample information on the use of machine learning in both defense and attacks. While the defense was covered in most articles (I recommend reading "The Truth about Machine Learning in Cybersecurity"), Machine Learning for Cybercriminals seems to be overshadowed and not unanimous.


How hackers use machine learning to breach cybersecurity!

#artificialintelligence

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.