Rolling the Dice on AI SC Media

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Fear of successful cyberattacks meets fear of unintended consequences when machine learning is your first line of defense. Fear can be a great motivator. If you are afraid that a human cannot make a decision fast enough to stop a cyberattack, you might opt for an artificial intelligence (AI), machine learning system. But although fear, uncertainty and doubt -- the FUD factor -- of not responding quickly enough might motivate you to take this action, that same FUD factor that the action your automated system takes might be wrong is an equally strong motivator not to employ this technology. Welcome to this year's Catch 22. In the 1983 sci-fi classic War Games, a computer was employed to replace the soldiers who manned the intercontinental ballistic missile silos because, it was believed, the computer could launch the missiles dispassionately and not be swayed by indecision in case of a nuclear attack. A teenager hacked the system thinking it was an unreleased video game.


Predictive analytics can stop ransomware dead in its tracks

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This past February marks the two-year anniversary when Livingston County, Michigan, was hit by ransomware. The wealthiest county in the state had three years' worth of tax information possibly at the mercy of cybercriminals. As a local government, county CIO Rich C. Malewicz said they have been a target of ransomware, but in this instance they had backups at the ready. He said the most memorable ransomware attack was a result of a watering hole campaign using malvertizing to infect users visiting a local news website. "This attack was very clever in that all you had to do to get infected was visit the website, you didn't even have to click on the page.


10 Cyber Attacks Machine Learning Can Help Prevent

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Not even Cersei Lannister's scheming or Sir Jorah's father-like protectiveness could have prevented attackers from breaching HBO's network and stealing 1.5 terabytes of data (including unreleased Game of Thrones episodes). Machine learning, however, may have offered a more sound defense of HBO's virtual fortress. Artificial intelligence (AI) and machine learning (ML) are the topics of much debate, especially within the cybersecurity community. Is machine learning the next big security frontier? Is AI ready to take on machine learning-driven attacks?


How data science fights modern insider threats

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Ben Dickson is a software engineer and freelance writer. He writes regularly on business, technology and politics. Insider threats are the biggest cybersecurity threats to firms, organizations and government agencies. This is something you hear a lot at security conference keynotes and read about in data breach reports, white papers and surveys -- and these insider threats are becoming increasingly more difficult to detect and prevent, as well as more frequent. This seemingly unstoppable growth accentuates the problem and shortcomings of current solutions, and warrants the need for new defensive technologies to detect and stop the digital daggers aimed at our backs.


How data science fights modern insider threats

#artificialintelligence

Ben Dickson is a software engineer and the founder of TechTalks. Insider threats are the biggest cybersecurity threats to firms, organizations and government agencies. This is something you hear a lot at security conference keynotes and read about in data breach reports, white papers and surveys -- and these insider threats are becoming increasingly more difficult to detect and prevent, as well as more frequent. This seemingly unstoppable growth accentuates the problem and shortcomings of current solutions, and warrants the need for new defensive technologies to detect and stop the digital daggers aimed at our backs. Data science -- the application of mathematics, big data analytics and machine learning to extract knowledge and detect patterns -- is an emergent, advanced technology area that is proving its effectiveness in the realm of cybersecurity, including fighting insider threats.