API Security Based on Automatic OpenAPI Mapping

This paper presents Map Reduce Graph MRG, a novel unsupervised method for modeling and securing HTTP REST APIs. MRG learns API structure from real-world traffic without prior knowledge or labels, automatically generating OpenAPI-compliant documentation by reconstructing routes, methods, and...

An Efficient Anomaly Detection Framework for Wireless Sensor Networks Using Markov Process

Wireless Sensor Networks forms the backbone of modern cyber physical systems used in various applications such as environmental monitoring, healthcare monitoring, industrial automation, and smart infrastructure. Ensuring the reliability of data collected through these networks is essential as the...

ConCap: Practical Network Traffic Generation for Flow-Based Intrusion Detection Systems

Network Intrusion Detection Systems NIDS have been studied in research for almost four decades. Yet, despite thousands of papers claiming scientific advances, a non-negligible number of recent works suggest that the findings of prior literature may be questionable. At the root of such a...

VOLTRON: Detecting Unknown Malware Using Graph-Based Zero-Shot Learning

The persistent threat of Android malware presents a serious challenge to the security of millions of users globally. While many machine learning-based methods have been developed to detect these threats, their reliance on large labeled datasets limits their effectiveness against emerging,...

Defending the Edge: Representative-Attention for Mitigating Backdoor Attacks in Federated Learning

Federated learning FL enhances privacy and reduces communication cost for resource-constrained edge clients by supporting distributed model training at the edge. However, the heterogeneous nature of such devices produces diverse, non-independent, and identically distributed non-IID data, making t...

Advancing Email Spam Detection: Leveraging Zero-Shot Learning and Large Language Models

Email spam detection is a critical task in modern communication systems, essential for maintaining productivity, security, and user experience. Traditional machine learning and deep learning approaches, while effective in static settings, face significant limitations in adapting to evolving spam...

R-TPT: Improving Adversarial Robustness of Vision-Language Models through Test-Time Prompt Tuning

Vision-language models VLMs, such as CLIP, have gained significant popularity as foundation models, with numerous fine-tuning methods developed to enhance performance on downstream tasks. However, due to their inherent vulnerability and the common practice of selecting from a limited set of...

Delta Electronics DiaLink 安全漏洞

DIALink is an equipment networking platform from Delta Electronics that effectively manages CNC machines and PLC-controlled machines, collects on-site equipment data and connects it to the upper management platform through a unified interface, and at the same time provides visual information...

Lessons learned building supervised machine learning into DDoS Protection

Imperva’s Data Scientists trained a machine-learning model to auto-configure DDoS security policies and this blog shares some of the lessons learned along the way. Data scientists consider labeled data the gold standard and, despite having to filter out anomalies, there is an overall tendency to...