Anomaly detection, the “identification of rare occurrences, items, or events of concern due to their differing characteristics from the majority of the processed data,” allows organizations to track “security errors, structural defects and even bank fraud,” according to DeepAI and described in three main forms of anomaly detection as: unsupervised, supervised and semi-supervised. Security Operations Center (SOC) analysts use each of these approaches to varying degrees of effectiveness in Cybersecurity applications.
On the surface, an “incremental stacking” approach to correlative analysis platforms like SIEM, XDR and UEBA is logical. Organizations can overcome some of the inherent limitations present in their security solutions by adding a network traffic analysis (NTA), for example. Industry analysts have been touting this approach for some time now as necessary for full coverage enterprise security.
Within the first 24 hours after deployment, MixMode had enabled the government entity to regain control over the security environment and network data infrastructure. No longer limited to log data analysis, they were able to identify and address real-time threats as well as network and operational configuration challenges.
Since we determine everything on data here at MixMode, we went into our website data to see which of our Q2 articles got the most traffic over the past few months. Not surprisingly, the majority of our top articles covered topics on the advancement of AI in cybersecurity and network traffic analysis (NTA).
Real unsupervised AI spots security issues sooner and predicts future behavior more accurately than older first- and second-wave solutions. Self-supervised AI technology draws on an understanding of the fundamental nature of the network where it lives, an understanding that isn’t possible with supervised-AI.
Yann LeCun and Yoshua Bengio were recently interviewed by VentureBeat Magazine on the topics of self-supervised learning and human-level intelligence for AI. Our CTO Dr. Igor Mezic sat down with our team to discuss some of the most interesting pieces of the LeCun article, and offer a potential solution to a search for truly self-supervised …
Artificial Intelligence – or AI – has become a buzzword since it emerged in the 1950s. However, all AI systems are not created equal. In our white paper, “Self-Supervised Learning – AI For Complex Network Security,” Dr. Peter Stephenson explains the different “waves” of artificial intelligence. He uses the DARPA definitions for each of these …
The relationship between modern cybersecurity solutions and AI has become inextricable. The unfortunate reality is that even the most talented and responsive SecOps teams are unable to manually catch every threat posed to the sprawling, hybrid networks on which today’s organizations rely. Forward-looking organizations know they need to bring AI and machine learning based security …
Our newest whitepaper, “How Predictive AI is Disrupting the Cybersecurity Industry,” evaluates several common SecOps issues around Network Traffic Analysis, explaining why typical solutions are wholly ineffective and represent sunk costs versus added value. We examine how self-supervised learning AI is poised to overcome the SecOps challenges of protecting today’s distributed networks.