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?


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.


How data science fights modern insider threats

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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.


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.


Hacker AI vs. Enterprise AI: A New Threat

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The adversarial use of artificial intelligence (AI) and machine learning (ML) in malicious ways by attackers may be embryonic, but the prospect is becoming real. It's evolutionary: AI and ML gradually have found their way out of the labs and deployed for security defenses, and now they're increasingly being weaponized to overcome these defenses by subverting the same logic and underlying functionality. Hackers and CISOs alike have access to the power of these developments, some of which are turning into off-the-shelf offerings that are plug-and-play capabilities enabling hackers to get up and running quickly. It was only a matter of time before hackers started taking advantage of the flexibility of AI to find weaknesses as enterprises roll it out in their defensive strategies. The intent of intelligence-based assaults remains the same as "regular" hacking.