Compare Censys vs. Cryptomage

Censys Attack Surface Management (ASM) is dedicated to identifying previously unknown assets, which include everything from Internet services to cloud storage buckets, while thoroughly evaluating all public-facing assets for security and compliance issues, irrespective of their hosting environment. Although cloud services empower organizations to foster innovation and responsiveness, they also introduce a multitude of security vulnerabilities that can proliferate across numerous cloud initiatives and accounts across various providers. This challenge is further intensified by the tendency of non-IT staff to create unmanaged cloud accounts and services, leading to significant blind spots for security teams. With Censys ASM, you gain extensive security oversight of your Internet assets, no matter where they are located or under which account they reside. Censys not only identifies unknown assets, but also compiles a detailed inventory of all your public-facing assets, highlights serious security vulnerabilities, and enhances the value of your existing security investments with targeted insights. Additionally, the platform enables organizations to maintain a proactive security posture by continuously tracking and managing their diverse digital assets.

Threat detection entails a thorough examination of each individual network packet along with its contained data, featuring elements such as network protocol identification and verification, which allows for the identification of both obscure and concealed protocols. It incorporates machine learning techniques that provide a proactive assessment of traffic risk through scoring systems. Additionally, the detection of network steganography helps uncover hidden traffic within the network, including potential data breaches, espionage activities, and botnet communications. Utilizing proprietary algorithms for steganography detection serves as an efficient means of revealing various information concealment strategies. Furthermore, a unique signature database containing an extensive array of recognized network steganography techniques enhances detection capabilities. Forensic analysis is employed to effectively evaluate the ratio of security incidents relative to the traffic source. Facilitating the extraction of high-risk network traffic aids in concentrating analysis on specific threat levels, while storing processed traffic metadata in an extended format accelerates the trend analysis process. This multifaceted approach ensures a comprehensive understanding of network security challenges and enhances the ability to respond to emerging threats.