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  • Ransomware detection: Can employees help?

    As ransomware attacks continue to escalate, should organizations make employees an integral part of their ransomware detection and prevention strategy? Continue Reading

  • Ransomware prevention tools to win the fight

    Fighting malware today means battling ransomware. Learn what ransomware prevention tools you need to acquire and how to perfect using the tools your company already owns. Continue Reading

  • In 2017, cybersecurity attacks will follow your data

    Thanks to a polarizing election, the potential ramifications of cybersecurity attacks are front and center. Your friends and relatives probably have some concept of what it is that you actually do and its importance. But the daily challenges of protecting most enterprise environments is less like The Americans than they might think. Still, it's exciting. In this issue of Information Security magazine, we look at the incoming threats in 2017 and some countermeasures that can help your organization bolster its defenses.

    Last year, we saw the internet of things used as a beachhead in larger cybersecurity attacks. Many devices now use cloud-based systems to communicate. They regularly send status updates to the cloud server and retrieve new commands to execute. Weak and incorrectly implemented authentication between device and cloud is often the point of failure that can be exploited to either attack the cloud infrastructure or the device. So far, destructive attacks are not common and are mostly limited to distributed denial-of-service attacks, which do not cause permanent damage. But future attacks, if they are combined with ransom demands, may destroy devices intentionally.

    Breaches of cloud storage that modify data instead of just "stealing" it and vulnerabilities in microservices environments are other areas in which attackers may get more leverage. With the emergence of cloud-based microservices, this problem will only become worse. Instead of including a library in software shipped to clients, the software now relies on cloud-based web services to perform certain functions. We look at what is coming next and ways to mitigate these cybersecurity attacks. Continue Reading

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We’ve gathered up expert advice and tips from professionals like you so that the answers you need are always available.

  • Reporting ransomware attacks to the FBI: Pros and cons

    Reporting ransomware attacks to law enforcement can pose potential risks to the targeted organization. Expert Mike O. Villegas discusses the key aspects of disclosing an attack. Continue Reading

  • Ransomware prevention strategies for Windows Server admins

    One ransomware infection can lock up critical data and disrupt the business, but these tips can help boost your data center's defenses. Continue Reading

  • Machine learning in security explodes: Does it work?

    Machine learning in security is continuing to advance, and many companies now claim to have introduced artificial intelligence techniques into their platforms. With the high volume of data that most security teams have to prioritize, machine learning in security technology is increasingly being adopted as a way to reduce the noise that traditional security products produce.

    Smaller companies, such as Keen Footwear, have turned to threat platforms that incorporate machine learning and AI techniques -- and soon automated defense -- to solve a variety of problems. "I don't need to go hire someone dedicated to security," said Clark Flannery, director of IT at Keen's headquarters in Portland, Ore. "It just feels like a whole team back there -- who are way more qualified than [staff] I would be able to pay."

    With security professionals in short supply, companies like Keen are relying on these technologies to make it easier to spot and respond to attacks. While machine learning and artificial intelligence are often used interchangeably, the concepts are different. In this issue of Information Security magazine, we discuss the nuances and dive into the current state of the technology. Machine learning in security offers information security analysts more depth of knowledge, helping to detect patterns and related analysis they may not otherwise have known about.

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