Anat Bremler Barr

Daniel Alfasi, Tal Shapira, Anat Bremler Barr

The proliferation of software vulnerabilities poses a significant challenge for security databases and analysts tasked with their timely identification, classification, and remediation. With the National Vulnerability Database (NVD) reporting an ever-increasing number of vulnerabilities, the traditional manual analysis becomes untenably time-consuming and prone to errors. This paper introduces VulnScopper, an innovative approach that utilizes multi-modal representation learning, combining Knowledge Graphs (KG) and Natural Language Processing (NLP), to automate and enhance the analysis of software vulnerabilities. Leveraging ULTRA, a knowledge graph foundation model, combined with a Large Language Model (LLM), VulnScopper effectively handles unseen entities, overcoming the limitations of previous KG approaches. We evaluate VulnScopper on two major security datasets, the NVD and the Red Hat CVE database. Our method significantly improves the link prediction accuracy between Common Vulnerabilities and Exposures (CVEs), Common Weakness Enumeration (CWEs), and Common Platform Enumerations (CPEs). Our results show that VulnScopper outperforms existing methods, achieving up to 78% Hits@10 accuracy in linking CVEs to CPEs and CWEs and presenting an 11.7% improvement over large language models in predicting CWE labels based on the Red Hat database. Based on the NVD, only 6.37% of the linked CPEs are being published during the first 30 days; many of them are related to critical and high-risk vulnerabilities which, according to multiple compliance frameworks (such as CISA and PCI), should be remediated within 15-30 days. Our model can uncover new products linked to vulnerabilities, reducing remediation time and improving vulnerability management. We analyzed several CVEs from 2023 to showcase this ability.

Ronen Ben David, Anat Bremler Barr

In recent years, we have witnessed a new kind of DDoS attack, the burst attack(Chai, 2013; Dahan, 2018), where the attacker launches periodic bursts of traffic overload on online targets. Recent work presents a new kind of Burst attack, the YoYo attack (Bremler-Barr et al., 2017) that operates against the auto-scaling mechanism of VMs in the cloud. The periodic bursts of traffic loads cause the auto-scaling mechanism to oscillate between scale-up and scale-down phases. The auto-scaling mechanism translates the flat DDoS attacks into Economic Denial of Sustainability attacks (EDoS), where the victim suffers from economic damage accrued by paying for extra resources required to process the traffic generated by the attacker. However, it was shown that YoYo attack also causes significant performance degradation since it takes time to scale-up VMs. In this research, we analyze the resilience of Kubernetes auto-scaling against YoYo attacks. As containerized cloud applications using Kubernetes gain popularity and replace VM-based architecture in recent years. We present experimental results on Google Cloud Platform, showing that even though the scale-up time of containers is much lower than VM, Kubernetes is still vulnerable to the YoYo attack since VMs are still involved. Finally, we evaluate ML models that can accurately detect YoYo attack on a Kubernetes cluster.