Machine learning algorithm may be the key to timely, inexpensive cyber-defense

Attacks on vulnerable computer networks and cyber-infrastructure — often called zero-day attacks — can quickly overwhelm traditional defenses, resulting in billions of dollars of damage and requiring weeks of manual patching work to shore up the systems after the intrusion. Now, a Penn State-led team of researchers used a machine learning approach, based on a technique known as reinforcement learning, to create an adaptive cyber defense against these attacks.

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UAH professors get $500,000 NSF grant to better protect privacy of solid-state drive data

The National Science Foundation has awarded a pair of professors at The University of Alabama in Huntsville (UAH), a part of the University of Alabama System, a nearly $500,000, three-year grant to develop a better way to wipe data from the solid-state drives (SSDs).

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Tandon team shines light on roiling market for stolen debit and credit cards

Damon McCoy and colleagues at the NYU Tandon School of Engineering analyzed multi-year data extracted from BriansClub, an underground bazaar for buying stolen and leaked credit card information. Among findings were that chip-enabled cards are no guarantee of security if owners still swipe the stripe: 85% of the stolen magnetic stripe data originated from EMV chip-enabled cards.

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University of Texas at Dallas Computer Scientists’ New Tool Fools Hackers into Sharing Keys for Better Cybersecurity

Instead of blocking hackers, a new cybersecurity defense approach developed by University of Texas at Dallas computer scientists actually welcomes them.

The method, called DEEP-Dig (DEcEPtion DIGging), ushers intruders into a decoy site so the computer can learn from hackers’ tactics. The information is then used to train the computer to recognize and stop future attacks.

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