The last decades growing interest indeep learningwas triggered by the proven capacity of neural networks in computer vision tasks.
What else can you do with an image classifier?
A year later, the same technique was used to develop a machine learning system that detects phishing websites.
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It is showing promise in cybersecurity, but it could also be applied to other domains.
In recent years, researchers have also tried a range ofmachine learning techniquesto detect malware.
Binary visualization can redefine malware detection by turning it into a computer vision problem.
In this methodology, files are run through algorithms that transform binary and ASCII values to color codes.
These differences would have gone unnoticed using classic malware detection methods.
The researchers created a dataset of visualized binary files that included both benign and malign files.
The dataset contained a variety of malicious payloads (viruses, worms, trojans, rootkits, etc.)
and file types (.exe, .doc, .pdf, .txt, etc.).
The researchers then used the images to train a classifier neural data pipe.
Overall, the algorithm achieved an average detection rate of around 74 percent.
Detecting phishing websites with deep learning
Phishing attacksare becoming a growing problem for organizations and individuals.
Traditional approaches for detecting phishing websites revolve around blacklisting malicious domains or whitelisting safe domains.
Other detection methods rely on heuristics.
These methods are more accurate than blacklists, but they still fall short of providing optimal detection.
The technique uses binary visualization libraries to transform website markup and source code into color values.
This would create a smaller data input string when scraped.
CNNs are especially suited forcomputer visiontasks including image classification and object detection.
Once the model is trained, it is plugged into a phishing detection tool.
The researchers experiments showed that the technique could detect phishing websites with 94 percent accuracy.
Using visual representation techniques allows to obtain an insight into the structural differences between legitimate and phishing web pages.
Moreover, the method learns from the misclassifications and improves its efficiency, the researchers wrote.
I recently spoke to Stavros Shiaeles, cybersecurity lecturer at the University of Portsmouth and co-author of both papers.
Shiaeles is also exploring the use of binary visualization and machine learning to detect malware traffic inIoT networks.
As machine learning continues to make progress, it will provide scientists new tools to address cybersecurity challenges.
Binary visualization shows that with enough creativity and rigor, we can find novel solutions to old problems.
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