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The Rise Of The Intelligent Machine In Cybersecurity Posted on Nov 09 - 2018

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Protecting your data today means dealing with hacking attempts powered by machine learning (ML), the science of computers learning and acting like humans. These ML computer algorithms are based on an analytical model designed to collect data and adapt its processes and activities according to use and experience, getting “smarter” over time.

Hackers are also using these algorithms to automate time-consuming cyberattacks with hackbots, email phishing, and social media phishing. The U.S. intelligence community reports cybercriminals are even using stolen computing resources to eliminate the main costs of ML: central processing unit (CPU) time, graphics processing unit (GPU) time, data transfer and the electricity they all consume.    

Leading businesses already leverage ML algorithms to automate malware scanning and improve their cyber defenses. Many information technology (IT) professionals are experimenting with ML to see how it can improve business processes and increase productivity. What businesses must realize is that cybercriminals are taking the same approach to innovate their methods of attack. Both cyberattackers and defenders are looking to use artificial intelligence (AI) and ML to gain an advantage.

What Should We Expect As Hackers Start To Use AI For Reconnaissance?

Expect more targeted attacks using personally identifiable information about company leaders -- even in regard to lower-level employees -- because of ML. Public information about company leadership can make an email or social media phishing attack more convincing, especially as hackers automate data collection on a targeted company using ML to emulate both the timing of communications and writing style.

AI algorithms can now be trained to create spam email that resembles a legitimate message. Cybercriminals are using these techniques to execute sophisticated phishing attacks. We are no longer dealing with a “Nigerian Prince” asking you to facilitate an international wire transfer.

Black Hat research on Twitter-automated social spear phishing shows ML has increased the success of phishing attacks by at least 30% over traditional automated ones. If criminals can save time and effort by using ML to launch convincing phishing attempts, they will. According to the Black Hat report, hackers often mine Twitter content for personal data and use Twitter through its bot-friendly application programming interface (API).

To defend against phishing, email the sender of a questionable message a challenge-and-response question. Realize that hackers can analyze your message to respond in convincing ways. As smart chatbots learn to communicate better, this will become even more difficult. Alternatively, ask the sender through other channels about the message. Unless the attacker has compromised multiple accounts simultaneously, you will detect and thwart the attempt. View More

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