AI News, FortiSandbox
FortiSandbox: Zero-day Threat Protection
This test helps emphasize the importance in the automation of the advanced threat response cycle of prevent-detect-mitigate across a number of threat vectors including web, email, and endpoint.
To help organizations assess the effectiveness of these new offerings, ICSA Labs, an independent division of Verizon (author or the annual Data Breach Investigations Report or DBIR), recently introduced a new independent, Advanced Threat Defense certification, and Email certification.
In the NSS Breach Detection System 2017 test, newly introduced FortiSandbox 2000E blocked 100% of advanced malware delivered over these two vectors and 99% overall offered at the lowest TCO, earning the NSS Labs “Recommended” rating.
The opportunities of artificial intelligence are undeniable, expanding and absolutely crucial to any business’ competitive strategy.
AIP+ is Accenture’s collection of modular, pre-integrated AI services and capabilities, designed to make it significantly easier to adopt AI throughout your business and scale new solutions faster than ever before.
A Cybersecurity and Artificial Intelligence Forecast for 2020
Our focus, of course, is on using deep learning to advance the standards in malware detection (and we see a lot of good happening in that regard) so we bring a unique perspective to these two areas. And not to brag, but when the question came up last year we provided a modest forecast that turned out to be fairly accurate.
as the value of cryptocurrency increases, it may be more lucrative (and easier) to focus on cryptojacking than ransomware. Malware-as-a-Service becomes increasingly sophisticated Criminal hackers are innovators and entrepreneurial (even if they are evil, self-centered, and destructive innovators and entrepreneurs).
A recent article explained that “Cerber ransomware runs 28 processes to check if it is really running in a target environment, refusing to detonate if it finds debuggers installed to detect malware, the presence of virtual machines (a basic “tell” for traditional sandboxes), or loaded modules, file paths, etc., known to be used by different traditional sandboxing vendors.” In 2020, we believe that new malware–using AI-models to evade sandboxes–will be born.
Instead of using rules to determine whether the “features” and “processes” indicate the sample is in a sandbox, malware authors will instead use AI, effectively creating malware that can more accurately analyze its environment to determine if it is running in a sandbox, making it more effective at evasion.
That means an emphasis on malware detection and prevention, and with data breach violations reaching hundreds of millions of dollars in the EU and U.S., we predict CCPA and the recent history of enforcement will drive a significant increase in cybersecurity spending.
Our experience with the application of deep learning to meet the challenges of threat detection and prevention give us hope that, as our efforts and those of other innovators continue and build momentum, we are confident that 2020 will be regarded as the year our industry finally turned the tide against hackers.
- On 15. januar 2021
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