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Machine Learning and Cybersecurity

Tavve Systems cybersecurity

Some of the most innovative approaches to cybersecurity still depend largely on human analysts. With the growing sophistication and volume of cyber threats, it is simply too difficult for analysts to keep pace. Hence, machine learning steps in. Machine learning is all about having an algorithm learn from the data rather than having a human encode the logic. Along with machine learning comes new cybersecurity threats.

ML-era in cybersecurity: A step toward a safer world or the brink of chaos?

While artificial intelligence (AI) and machine learning (ML) have been transforming various fields of human activity for some time now, their full transformative potential is yet to be realized. ML-based technologies will increasingly help fight fraud, evaluate and optimize business processes, improve testing procedures and develop new solutions to existing problems.

However, like most disruptive innovations, even machine learning has its drawbacks.

With business, critical infrastructure, as well as our personal lives becoming ever more entwined with the digital realm, new risks will emerge. Attackers can employ ML in multiple ways: to power their malware, to target specific victims and extract valuable data, to hunt for zero-day vulnerabilities or to protect hijacked infrastructure such as botnets.

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AWS’s Ian Massingham on Unstructured Data, Machine Learning and Cybersecurity

“In the modern world, without machine learning, it would be impossible for security professionals to gather, organize and act on the sheer magnitude of security events that occur on a day to day basis”

Data has fundamentally changed the way that companies operate, redefining business models, creating new industries and opening up additional revenue streams, writes AWS Technical Evangelist, Ian Massingham. In the last five years alone internet users have increased by more than 82 percent and Gartner anticipates that data volume is set to grow 800 percent by 2022, with 80 percent of it residing as unstructured data.

While there is no denying that it is a huge opportunity for businesses across the globe, the flood of unstructured data also represents an evolving and ever greater challenge, especially for the cybersecurity team.

Whether it’s a multitude of IoT devices, web services, logs, videos, user chats, mobile apps, photos or streaming data that is now flooding the network, every source still needs to be investigated and assessed on the risk posed.

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AI, Machine Learning and the Need for Innovation in Cybersecurity

Despite a surge in spending on cybersecurity in recent years, federal agencies have not had the intended return on their investments.

This past year, the Office of Management and Budget allocated more than $28 billion for non-classified cybersecurity programs, compared to $7.5 billion in 2006. Those same years have seen a surge of innovation in cybersecurity, providing more sophisticated and effective tools and techniques for every stage of the cyber lifecycle.

And yet a recent report from OMB found that 74 percent of federal agencies were either “at risk” or “high risk” for a cybersecurity breach. More startling, perhaps, was the finding that, in 38 percent of government cybersecurity incidents, the relevant agency could not identify how the hacker perpetrated the attack.

So, where is the disconnect?

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If your organization is looking for a cybersecurity solution that will increase your network security and reduce your IT security staff’s work load then check out the ZoneRanger. For more information contact Tavve Systems.


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