Traditional detection-based cybersecurity is fundamentally limited, with even combined antivirus engines achieving only 99.92-99.94% accuracy, which becomes critically insufficient when processing millions or billions of files; a prevention-first approach using file regeneration (Content Disarm and Reconstruction) rebuilds files from scratch, preserving legitimate content while removing malicious components without relying on detection, thereby closing the security gap that detection-based models cannot address.
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Detection Is Not Enough | Cybersecurity Upside DownAjouté :
This book is all about rethinking your cyber security strategy and think about cyber security prevention through file regeneration.
Talk about the meaning behind the title.
The entire cyber security industry is all about detection and after the detection then there's going to be a reaction. detection is also prone for failure from the get- go. So, we need to take the entire model upside down and start with prevention. We want to reverse the model. Assume everything is malicious. Assume the file is malicious.
You're going to create a new file that will have all of the DNA, all the characteristics of the original file.
It's not going to rely on detection at all. You're removing detection from the algorithm altogether to a point it's a prevention mindset. Antiviruses are not effective predicting whether a file is malicious or not. However, while we combine all the antiviruses together, we achieve also only like 99.94 99.92 whenever you need to go and transfer a billion files a million files. Is that good enough?
In the following chapters, I'll present the CDR approach for sanitizing files and introduce a broader CDR mindset.
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