Data breaches, attacks on critical infrastructure, and malware/ransomware attacks almost daily highlight the importance of network security. We are increasingly reliant on networked infrastructure, and the IoT will connect billions of devices to the internet, giving attackers more opportunities to exploit. 

However, recent advances in convolutional neural networks have propelled the field of computer vision to new heights. It's not immediately obvious how computer vision methods relate to network security. Nonetheless, much research has shown how to use computer vision to detect attacks or build security solutions. Moreover,

deep learning has grown in popularity over the last decade due to neural networks' demonstrated ability to perform computer vision tasks. 

What else can an image classifier do?

In 2019, a group of cybersecurity researchers examined the possibility of treating security threat detection as an image classification problem. Their intuition was correct, as they developed a machine learning model capable of detecting malware using images generated from the contents of application files. A year later, the same technique developed a machine learning system for detecting phishing websites. Combining binary visualization and machine learning is a highly effective technique for discovering novel solutions to well-known problems. 

You can use deep learning to detect malware

Traditionally, Malware detectors keep a database of virus definitions that include opcode sequences or code snippets and scan newly created files for the presence of these signatures. Unfortunately, malware developers can easily evade such detection methods by obfuscating their code or mutating it at runtime via polymorphism techniques. While dynamic analysis tools attempt to detect malicious behaviour in real-time, they are inefficient and require establishing a sandbox environment to test suspicious programmes.

Researchers have also experimented with various machine learning techniques for malware detection in recent years. These machine learning models have made significant progress in overcoming some of the challenges associated with malware detection, including code obfuscation. They do, however, introduce new challenges, such as the requirement to learn an excessive number of features and the use of a virtual environment to analyze the target samples.

Binary visualization can reshape the field by recasting malware detection as a computer vision problem. In 2019, researchers from the Universities of Plymouth and Peloponnese demonstrated that visually distinguishing malicious files from safe files creates new patterns. Classic malware detection methods would have missed these differences.

Moreover, this model was particularly good at detecting malware in.doc and pdf files, the preferred medium for ransomware attacks. Additionally, the researchers say the model's performance can be improved by including the file type as a learning dimension. Overall, the algorithm recognized around 74% of the images.

Additionally, In 2020, researchers from the Universities of Plymouth and Portsmouth developed a novel method for detecting phishing websites using binary visualization and deep learning. Moreover, the method converts website markup and source code into colour values.

Conclusion

The researchers' experiments demonstrated that the technology could detect phishing websites with a 94 per cent accuracy. With the advancement of machine learning, scientists will soon gain new tools for addressing cybersecurity challenges.

https://bdtechtalks.com/2021/09/10/computer-vision-deep-learning-threat-detection/