3 Key Computer Vision Trends in Cybersecurity You Should Know

In the ever-evolving landscape of cybersecurity, staying ahead of the curve is essential to protect digital assets and sensitive information. One of the most promising and impactful technologies making waves in the world of cybersecurity is Computer Vision.

This technology, which enables computers to interpret and understand visual information, has opened up new possibilities for enhancing security measures. Today's issue of the Let's Dive into Deep Tech newsletter covers three trends in computer vision that have a crucial impact on the future of cybersecurity.

1. Extending identification and authorization factors by biometric and behavioral traits

Computer vision can enable continuous authentication by monitoring and analyzing the user's behavior and biometric traits. By capturing and analyzing facial features or gestures in real time, computer vision systems can ensure that the authenticated user remains present and engaged during a session, reducing the risk of unauthorized access. By establishing a baseline of normal behavior, computer vision algorithms can continuously monitor and compare these behavioral patterns to detect anomalies or unauthorized access attempts.

2. Fusion of computer vision and blockchain-based smart contracts

Computer vision algorithms can detect and validate specific events or conditions by analyzing video feeds or image data. This information can be recorded and stored in a smart contract to ensure transparency, immutability, and security. For example, computer vision can verify the presence of specific individuals during a transaction or confirm the completion of particular tasks. Computer vision can assist in tracking and authenticating physical assets. By analyzing visual cues such as barcodes, QR codes, unique identifiers, and physical shapes, track assets can be identified and tracked as they move through the supply chain or within a facility. This information can be recorded in a smart contract, ensuring the integrity and transparency of asset transactions.

3. Utilization of computer vision algorithms in the field of malware and network anomaly detection

Machine learning algorithms used in computer vision have desirable properties in anomaly detection. Applying a transfer learning approach to these algorithms and correctly mapping the binary content of files or network traffic to RGB maps can detect anomalies that may be a symptom of an attempted attack.

Thank you for your interest in this newsletter. It was created in collaboration with Łukasz Brandt , Senior Security Researcher at DAC.digital . We'd love to hear your comments on this matter.

Let's dive into Deep Tech is a bi-weekly newsletter series about how to transform your business into the next big thing with cutting-edge technology. Subscribe to stay up-to-date with the latest trends, challenges, and opportunities at the junction of business and deep tech.