Robust Semi-Supervised Temporal Intrusion Detection for Adversarial Cloud Networks

Cloud networks increasingly rely on machine learning based Network Intrusion Detection Systems to defend against evolving cyber threats. However, real-world deployments are challenged by limited labeled data, non-stationary traffic, and adaptive adversaries. While semi-supervised learning can...

MixGAN: a Hybrid Semi-Supervised and Generative Approach for DDoS Detection in Cloud-Integrated IoT Networks

The proliferation of cloud-integrated IoT systems has intensified exposure to Distributed Denial of Service DDoS attacks due to the expanded attack surface, heterogeneous device behaviors, and limited edge protection. However, DDoS detection in this context remains challenging because of complex...

The vulnerability of the API interface of the analytics and automation platform for working with Cisco Nexus Dashboard cloud networks allows a hacker to execute arbitrary commands with root privileges.

The vulnerability of the API interface of the Cisco Nexus Dashboard platform’s analytics and automation services for cloud-based data centers is related to the lack of authentication for a critical function. Exploiting this vulnerability allows a malicious actor to execute arbitrary commands with...

Research & Academic

We introduce a novel machine learning approach that uses network flows to generate application-level representation of public and private cloud networks. This will greatly simplify the journey to a micro-segmented network...

How to Mitigate the Threat Cryptocurrency Mining Poses to Enterprise Security

The growing popularity of Bitcoin and other cryptocurrencies is generating curiosity—and concern—among security specialists. Crypto mining software has been found on user machines, often installed by botnets. Organizations need to understand the risks posed by this software and what actions, if...